As 5G continues to change the way devices communicate, a new specification known as RedCap, short for Reduced Capability, is now under the microscope as a key player for lower-powered devices.

Unlike full-scale 5G, which is designed to deliver high bandwidth for data-heavy uses, RedCap is tailored for Internet of Things (IoT) devices such as wearables, sensors, and smart cameras. It offers the benefits of 5G but with reduced complexity, lower power consumption, and greater efficiency—perfect for devices that don’t need the full capabilities of a traditional 5G network.

What is RedCap API?

RedCap API is the developer interface that makes it easier to build and manage these low-bandwidth 5G connections. APIs (Application Programming Interfaces) are what developers use to connect applications to external services or hardware.

In this case, RedCap API will allow developers to integrate RedCap-enabled devices with networks, ensuring smooth communication between IoT devices and 5G systems.

Since RedCap is optimized for low-power, low-data devices, the API is designed with simplicity and efficiency in mind. Developers can use the RedCap API to set up connections for devices like smartwatches, fitness trackers, and smart home sensors that don’t need the full spectrum of 5G power.

It allows these devices to interact with networks without overwhelming them with unnecessary data or energy consumption. The goal is to provide enough connectivity for IoT functions—like monitoring, tracking, or real-time updates—while keeping battery usage low.

With RedCap API, developers can create solutions for devices that must run for long periods without frequent recharges, such as remote sensors or wearables. It brings 5G’s high-quality infrastructure to smaller-scale devices without the extra bulk of data-heavy networks, giving developers more flexibility when building products for the IoT market.

RedCap vs existing technologies

RedCap sits between full 5G and existing IoT standards like LTE-M (Long-Term Evolution for Machines) and NB-IoT (Narrowband IoT). While LTE-M and NB-IoT are already used for low-power, wide-area connections, they don’t have access to the advanced 5G infrastructure that RedCap offers.

RedCap utilizes the modern 5G architecture, meaning it can deliver better performance while still focusing on power efficiency and cost-effectiveness.

Unlocking smart living: 5G’s impact on everyday tech

Traditional 5G is designed for high-speed, high-bandwidth applications—think of things like 4K streaming, cloud gaming, and remote surgery. But for smaller devices, like a fitness tracker or a smart security camera, this level of performance isn’t necessary. RedCap fills the gap by offering enough bandwidth and power to ensure reliable performance while keeping battery consumption low. In contrast, LTE-M and NB-IoT are ideal for more basic, infrequent data transmission but lack the versatility that RedCap brings to devices that require more frequent connectivity.

In essence, RedCap combines the best of both worlds: it offers the advanced infrastructure and network quality of 5G but at a reduced scale that fits the needs of IoT and wearable devices.

5G RedCap trade-offs

While RedCap delivers many benefits for low-power devices, it does come with some trade-offs to ensure it remains affordable and power-efficient. According to Omdia, several compromises in performance were made to balance cost, complexity, and efficiency.

Fewer antennas

RedCap devices can use fewer antennas than standard 5G devices. This not only reduces the overall cost but also simplifies the device design, making it ideal for smaller, low-power IoT devices. However, this also means that these devices won’t achieve the same data transmission speeds as their full-scale 5G counterparts.

Lower maximum bandwidth

RedCap devices have a maximum bandwidth of 20 MHz, compared to the 100 MHz or more used by traditional 5G devices. While this limits the amount of data that can be transmitted, it is generally sufficient for most IoT applications, such as remote sensors or fitness trackers, which don’t need constant, high-bandwidth data streams.

Different transmission mode

RedCap supports half-duplex frequency division duplex (FDD) transmission, meaning that the device can either send or receive data at one time but cannot do both simultaneously. Standard 5G devices use full-duplex FDD, allowing them to transmit and receive data at the same time.

This trade-off lowers the cost but also limits the performance, which is generally acceptable for devices that don’t need real-time, high-speed communication.

Single carrier support

RedCap devices do not support carrier aggregation and are limited to single connectivity, only working in 5G standalone (SA) mode. This is yet another compromise aimed at reducing complexity and extending battery life. The simplified carrier support allows RedCap devices to function within a 5G network without the need for additional hardware capabilities.

Extended battery life

One of the main benefits of RedCap is the extended battery life. RedCap devices use 5G Power Class 3, which is designed to consume less power, ideal for wearables or remote sensors that need to operate for extended periods without frequent recharging.

Looking ahead

As telecom companies prepare to roll out RedCap-enabled devices, the RedCap API will be an essential tool for developers looking to tap into this next generation of connectivity. The potential applications are vast, spanning healthcare, smart homes, industrial monitoring, and more. With its focus on power efficiency and reduced complexity, RedCap offers a way for businesses to innovate while keeping costs down and maximizing the life of their devices.

For consumers, RedCap could mean longer-lasting wearable devices and smarter, more connected homes. For industries, it offers a streamlined way to deploy IoT solutions at scale. With the RedCap API, the integration of these devices into 5G networks becomes more accessible and efficient, paving the way for the continued growth of the Internet of Things.

As reported by Fierce Networks, telecom giants like AT&T and T-Mobile are preparing to release their first RedCap devices with the support of chipsets like Qualcomm’s Snapdragon X35, and it’s clear that this technology is set to become a cornerstone for connected devices.

Image credits: Emre Çıtak/Ideogram AI

The Internet of Things (IoT) has transformed the business world and made it possible for objects to exchange data as well as perform functions in various sectors. It has been used in manufacturing, healthcare, smart cities, logistics, and many other industries and IoT has become an essential component of today’s world. However, the very idea of the Internet of Things opens up new problems, above all, the problem of how to control the flow of information, how to provide protection, and how to guarantee stability in communication. This is where Software Defined WAN (SD-WAN) comes into the picture which provides a solution for IoT network management challenges.

SD-WAN is a virtualized network technology that enables an organization to aggregate, direct and manage its WAN infrastructure through centralized traffic flow management. These are broadband internet, MPLS and 4G/5G LTE which are suitable for handling the large number of devices that are characteristic of IoT. In this article, we will discuss how sd-wan improves the management of IoT networks and solve key challenges that include the following; Traffic management, Security, scalability and flexibility.

The IoT challenge: Large scale and distributed networks management

As IoT networks continue to grow at a very fast rate, there are various challenges that have emerged with the growth of the networks. The management of IoT devices and the data they produce in real-time from different locations is therefore a critical process that needs to be well coordinated. The key challenges include:

• Data overload: IoT devices including sensors and cameras are always in a state of transmitting data. If not well managed, this data can congest traditional networks, thus creating a bottleneck and slower throughputs.
• Security risks: Every single device that is connected to the network is a possible weak link and an entry point for the threats. Protecting thousands or even millions of IoT devices is an almost impossible task when one has to rely on the traditional networks that cannot offer real-time security patches or segmentation.
• Network reliability: Most of the IoT devices work in areas with poor network connectivity or they are located in areas with poor network coverage. This is because connectivity between such distributed networks is very important in order to provide consistent performance.
• Scalability: This is because as businesses expand their IoT presence, they require a network that can expand with them and that does not necessitate the spending on new physical hardware.

How SD-WAN Addresses IoT Challenges

SD-WAN offers a solution to the numerous problems that come with the management of IoT networks as it provides a flexible, scalable and secure network. Here’s how SD-WAN helps manage IoT networks efficiently:

1. IoT data through bandwidth management

IoT devices are capable of producing large data; however not all of it has to be delivered instantaneously. SD-WAN provides an enhanced traffic handling mechanism to enable business to direct the high priority IoT data, which may include alerts or monitoring details, to specific routes while less important data including logs or background operations can be managed through less critical routes.

Through the use of SD-WAN, a company is able to manage the use of their bandwidth in a way that will enable critical data to be transmitted through the network as fast and as effectively as possible without overloading the network. SD-WAN manages to choose the most efficient route for traffic based on the current state of the network and IoT applications require minimal latency and packet loss as the data is to be processed in real-time in use cases such as healthcare or manufacturing.

2. Strengthening IoT network security

To this end, IoT networks are highly susceptible to a number of cyber threats due to the increasing number of connected devices. Protecting these devices is possible only with the help of a network architecture that can generate various security measures for all entry points.

SD-WAN provides security through elements like encryption, firewalls, and micro-segmentation of the network. Network segmentation is an important aspect in IoT since it enables the dividing of the network into different segments for various devices. For instance, high-risk devices for instance security cameras can be put in a different segment than the critical systems such as the medical devices or the financial transaction systems. This makes it difficult for the attackers to spread within the network if one device is infected since it is isolated.

Also, SD-WAN has the capability of real-time threat detection and response thus helping in the prevention of threats that may be detrimental to the network.

3. Ensuring stable communications between IoT devices based on distributed architecture

Most IoT devices are used in areas that have limited or no network coverage which is a big challenge. This includes the use of remote sensors in agriculture, logistics tracking devices or industrial IoT in factories for instance. SD-WAN is a solution for this as it supports availability of different connection types such as broadband, LTE and satellite and thus guarantees reliability in different areas.

4. Supporting IoT network scalability

When companies are deploying IoT networks, they need the solutions that can accommodate the growth of the network. It is a common practice that traditional WAN architectures may need a lot of hardware investments or expensive MPLS links to address increasing data traffic.

On the other hand, SD-WAN is designed for the scale. It enables Organizations to incorporate new sites or devices into the network without having to completely redesign the network. This is particularly helpful for companies that are in the process of increasing their IoT etc., since they only need to incorporate new devices and connections into the SD-WAN architecture.

Also, SD-WAN is managed through the cloud meaning that IT departments are able to oversee, manage and secure the entire IoT network through one interface. This minimizes the effort and the time taken in managing large IoT environments spread across a geographical region.

5. Cutting down on expenditure and increasing productivity

The integration of SD-WAN can help organizations that are facing IoT network management save a lot of money. MPLS networks, while well known and dependable, are very costly and not very versatile. SD-WAN makes it possible for organizations to use lower-cost broadband for their connections while at the same achieving or even enhancing performance. In this way, the less important traffic can be routed through cheap connections while the important traffic can be routed through expensive connections and thus help in optimizing network costs.

In addition, SD-WAN has centralized management that helps to minimize the on-site IT infrastructure and thus cut on costs. Remote configuration and troubleshooting also helps in minimizing the time that is taken in order to diagnose and fix problems that can arise in IoT devices hence improving the overall smooth flow of operations.

IoT and SD-WAN: Current and future trends

With the number of IoT devices rising, SD-WAN is set to become the primary way of managing large scale networks that are more secure, manageable and efficient. Manufacturing, healthcare, retail and smart city sectors and many other have started using SD-WAN to manage their IoT environment.

Due to the fact that SD-WAN provides the organizations with better bandwidth utilization, enhanced security, efficient connection, and reduced costs, it is rapidly becoming the go-to option for IoT deployments. Due to these characteristics of SD-WAN, it can be integrated with IoT networks to help transport data since the IoT networks are ever evolving in terms of devices and the amount of data they require.

Featured image credit: Growtika

Why is Jasmy soaring? Could it be due to rumors linking Apple-Jasmy? Japan’s Prime Minister has sparked excitement with the news that the My Number Card function will be added to iPhones next spring. That’s where Jasmy coin comes into the story. There has been speculation for a while that the “Japon’s Bitcoin” might use it on My Number Card because of its decentralized data democracy tech and Jasmy IoT Platform. Although there is no official announcement yet, just this news makes Jasmy coin the top gainer in the largest 100 altcoins with a 27 rise.

Disclaimer: Nothing on this site should be construed as investing, financial, trading, or any other kind of advice. No cryptocurrency is recommended for purchase, sale, or storage by Dataconomy. Before making any investing decisions, you should do your own research and consult with a financial professional.

How did Jasmy-Apple rumors start?

The Jasmy-Apple news began to swirl when Japan’s Prime Minister announced that the My Number Card function would be integrated into iPhones next spring. This announcement followed a video conference between the Prime Minister and Apple CEO Tim Cook, during which they discussed the integration.

本日（5/30）、米Apple社CEOのティム・クック氏とテレビ会談を行い、来春、iPhoneにもマイナンバーカードの機能を搭載することを確認しました。
皆さんのスマホで様々なサービスが受けられるデジタル社会に向け、利便性の向上の取組みを進めてまいります。 pic.twitter.com/UUnOlmZdpo

— 首相官邸 (@kantei) May 30, 2024

JasmyCoin (JASMY), often referred to as the Bitcoin of Japan, had already been linked to the My Number Card iTrust authentication service, enhancing the card’s security and transparency through blockchain technology. The Prime Minister’s announcement makes the people believe that this technology would be part of the My Number Card’s integration into iPhones, sparking speculation about a deeper collaboration between Jasmy Coin and Apple.

The immediate market reactions and the bullish sentiment among the crypto community further fuelled the rumors. JASMY’s price saw a significant spike, and crypto analysts predicted continued gains, suggesting that the integration could lead to wider adoption and utility of JasmyCoin.

$JASMY Bulls are Back in the Town, Next Bullish Rally incoming..!!#Crypto #JASMY #JASMYUSDT pic.twitter.com/0FlGJYovIh

— Captain Faibik (@CryptoFaibik) May 30, 2024

Additionally, the Prime Minister’s emphasis on improving digital services and making them more accessible via smartphones, combined with Apple’s involvement, naturally led to speculation about a potential partnership. The buzz around JasmyCoin’s role in Japan’s digital ID system and its collaboration with Apple created a fertile ground for these rumors to flourish.

Besides rumors, one thing is sure Japon’s My Number Card is coming to iPhones. But what does it mean?

My Number Card & Apple

Japan’s My Number Card is a digital ID system for Japanese citizens and residents. It includes a unique identification number and stores personal information, which is used for various administrative and commercial purposes. The card aims to streamline government and business services, making them more efficient and secure.

The connection with Apple comes from a recent announcement by Japan’s Prime Minister that the My Number Card function will be integrated into iPhones next spring. This integration means that users will be able to access their digital ID and related services directly through their iPhones, enhancing convenience and security.

According to recent Apple-Jasmy news, the technology behind this integration involves Jasmy Coin (JASMY), which will help secure and manage the data on the My Number Card using blockchain technology.

AI may fuel the next bull run

A closer look at Jasmy’s IoT Platform to understand Apple-Jasmy rumors bettter

Jasmy’s IoT Platform is like a toolbox for businesses diving into the world of smart devices. It provides step-by-step help, from planning to product launch, making sure companies are on the right track. With Jasmy, managing all the data from these devices becomes easy with its secure platform. It’s like a control center where everything can be monitored and handled efficiently.

But Jasmy doesn’t stop there. It also offers the actual devices needed for building these smart solutions, like communication modules and wearables. This means businesses don’t have to hunt for compatible gadgets; they’re all provided in one place.

And it’s not just about tools; Jasmy offers hands-on support too. From consulting to community engagement, they’re there to guide businesses through every stage of their IoT journey.

Plus, Jasmy’s platform doesn’t just store data; it analyzes it too. Using special technology, it can sift through all the information collected from smart devices, making it easier for businesses to understand and use that data effectively.

You see their whitepaper by clicking here.

Featured image credit: Jasmy

Predicting the future of IoT doesn’t require a crystal ball; it demands an understanding of the transformative forces that are reshaping our world. The Internet of Things (IoT), a revolutionary network of interconnected devices and systems, is propelling us into a new era of possibilities.

As we stand on the cusp of this technological evolution, it’s evident that the future of IoT holds immense potential to revolutionize industries, redefine business models, and enhance the very fabric of our daily lives.

The rapid advancement of technology has paved the way for a world where virtually everything is interconnected. Internet of Things (IoT), has brought about revolutionary changes to the way we live, work, and interact with our surroundings.

From machine learning algorithms to AI-powered chatbots, we are now already intertwined with many innovative technological approaches. So, are we ready to put electronic devices that can communicate with each other at the center of our lives and embrace the future of IoT?

Don’t have to be a fortuneteller to predict the future of IoT

Before we talk about the future of IoT let us go through the definition and the current trends in IoT technology as of 2023. At its core, IoT refers to the interconnection of everyday objects, devices, and systems through the internet, enabling them to collect, exchange, and analyze data. This connectivity empowers us to monitor and control various aspects of our lives remotely, from smart homes and wearable devices to industrial machinery and city infrastructure. The essence of IoT lies in the seamless communication between objects, humans, and applications, making our environments smarter, more efficient, and ultimately, more convenient.

As of now, IoT has already permeated various sectors, ushering in a new era of possibilities. Industrial IoT (IIoT) has transformed manufacturing processes, optimizing production, predictive maintenance, and supply chain management. Smart cities utilize IoT to enhance urban living by improving traffic flow, energy consumption, and public services. Healthcare has also embraced IoT, with wearable devices monitoring vital signs and providing early warnings for potential health issues. Agriculture, retail, and transportation are just a few more examples of sectors benefiting from IoT integration.

The proliferation of IoT devices and the exponential growth of data generated by them have led to several trends and developments. Edge computing has gained prominence, enabling data processing closer to the source, reducing latency, and enhancing real-time decision-making. AI and machine learning are employed to extract insights from vast datasets, empowering businesses and individuals with valuable information. Furthermore, the rise of 5G networks promises higher bandwidth and lower latency, catalyzing the growth of IoT applications, and creating the foundations of the future of IoT.

Exploring the dynamic fusion of AI and the IoT

Looking ahead, the future of IoT holds remarkable potential. Over the next five years, we can expect a multitude of advancements that will reshape industries and lifestyles. Smart cities will continue to evolve, leveraging IoT to enhance sustainability, security, and quality of life. The healthcare sector will witness even more personalized and remote patient monitoring, revolutionizing the way medical care is delivered. AI and automation will play a pivotal role, in driving efficiency and innovation across various domains.

Autonomous vehicles will become more prevalent, with IoT-enabled sensors ensuring safer navigation and smoother traffic flow. Agriculture will benefit from precision farming, utilizing data-driven insights to optimize crop yield and resource utilization. The industrial sector will see increased adoption of robotics and automation, leading to streamlined operations and reduced downtime. As these trends unfold, the boundary between the digital and physical worlds will blur further, creating a seamless and immersive experience.

Enhancing urban living

In the future of IoT, the internet of things’ integration into smart cities holds transformative potential. Imagine a city where traffic congestion is a thing of the past, waste management is optimized, and energy consumption is minimized. Smart traffic management systems, equipped with IoT sensors and real-time data analysis, can dynamically adjust traffic signals, reducing congestion and improving overall traffic flow. Waste management becomes efficient as sensors in trash bins monitor fill levels, allowing for optimized collection routes, reducing costs and environmental impact.

Smart lighting systems brighten streets only when needed, and saving energy is another part we must consider as a benefit we can get from the future of IoT. Environmental monitoring stations equipped with IoT devices continuously collect data on air quality, noise levels, and weather conditions, helping city planners make informed decisions for a healthier urban environment. Smart parking systems guide drivers to available parking spaces, reducing the frustration of circling for parking and lowering carbon emissions.

Personalized and remote care

IoT’s impact on healthcare is profound, with the potential to revolutionize patient care. Wearable devices embedded with sensors continuously monitor vital signs such as heart rate, blood pressure, and glucose levels. This real-time data is transmitted to healthcare providers, enabling timely interventions for patients with chronic conditions. Remote patient monitoring allows doctors to track progress without frequent hospital visits, enhancing patient convenience and reducing healthcare costs.

IoT-enabled smart pills incorporate tiny sensors that transmit data when ingested, helping doctors monitor medication adherence. Hospitals benefit from IoT-powered asset tracking systems, ensuring equipment is readily available when needed. Additionally, AI-powered diagnostic tools analyze medical images, assisting doctors in detecting diseases early and accurately.

Efficiency and innovation

AI and automation coupled with IoT bring efficiency and innovation to various industries. In manufacturing, IoT-connected sensors on machinery provide real-time performance data, enabling predictive maintenance and minimizing downtime. Robotics integrated with IoT optimize warehouse operations, reducing manual labor and increasing productivity.

In customer service, chatbots and virtual assistants powered by AI interact with customers, addressing queries and providing information 24/7. AI-driven data analytics processes vast amounts of information, extracting valuable insights for informed decision-making. For example, retailers utilize AI to analyze consumer behavior, enhancing marketing strategies and product recommendations.

Safer and smoother transportation

IoT’s role in autonomous vehicles goes beyond self-driving capabilities. Sensors integrated into vehicles communicate with each other and with infrastructure, enhancing road safety and traffic management. Vehicle-to-vehicle (V2V) communication allows cars to share information about speed, direction, and braking, preventing collisions and creating a safer driving environment.

Vehicle-to-infrastructure (V2I) communication enables cars to receive real-time traffic data and adjust routes accordingly. IoT-powered autonomous vehicles can optimize traffic flow, reducing congestion and travel time. Moreover, IoT sensors monitor vehicle health, predicting maintenance needs and ensuring vehicles are roadworthy. So the future of IoT has the potential to solve the ongoing traffic problems all around the world.

Precision farming for sustainable yield

IoT’s impact on agriculture, known as precision farming or smart agriculture, has the potential to revolutionize how we produce food. Sensors in the soil measure moisture levels, nutrient content, and temperature, enabling farmers to provide the optimal conditions for crops. Drones equipped with IoT sensors survey vast fields, identifying areas needing attention, such as irrigation or pest control.

Livestock farming benefits from IoT-enabled wearable devices that monitor animal health and behavior. This data helps farmers detect illness early and improve breeding practices. IoT-powered irrigation systems ensure water is distributed efficiently, conserving resources. AI algorithms process data from IoT sensors to predict disease outbreaks, optimizing crop yield and reducing waste. As an offering from the future of IoT, agricultural investments can be a solution for both efficiency in food production and a remedy for world hunger.

The future of IoT must take a bumpy road

While the future of IoT is promising, it also presents its fair share of challenges. Security and privacy concerns are paramount, as the interconnected nature of IoT leaves room for potential breaches and data misuse. As IoT devices proliferate, ensuring robust encryption, authentication, and regular updates will be crucial. Additionally, the sheer volume of data generated poses challenges in terms of storage, processing, and meaningful analysis. Edge computing and efficient data management strategies will be instrumental in addressing these challenges.

Interoperability and standardization are also critical considerations. With numerous devices and platforms, establishing common protocols and frameworks will facilitate seamless communication and integration. Furthermore, the environmental impact of IoT, including electronic waste and energy consumption, must be carefully managed through sustainable design practices and responsible disposal methods.

How to prepare your business for the future of IoT

The future of business is intertwined with the Internet of Things (IoT), and preparing your company for this transformative era requires careful planning and strategic adaptation. Embracing IoT can unlock new opportunities, enhance operational efficiency, and elevate customer experiences.

Preparing your business for the future of IoT requires a comprehensive approach that encompasses technology, strategy, people, and processes. By understanding IoT’s potential, investing in the right technologies, fostering a culture of innovation, and prioritizing security and customer value, your business can position itself as a leader in the IoT-driven era.

Understand IoT’s relevance to your industry

As said before, the future of IoT is bright and you should start by assessing how IoT aligns with your industry and business model. Identify pain points, inefficiencies, and opportunities where IoT could make a significant impact. Whether you’re in manufacturing, healthcare, retail, or any other sector, understanding the specific benefits of IoT will help tailor your strategy.

Educate your team

Make sure that your team is well-versed in IoT concepts and potential applications. Provide training sessions and workshops to educate employees about the fundamentals of IoT, its benefits, and its potential challenges. Encourage cross-functional collaboration to explore innovative ideas and solutions.

Microsoft’s IoT training program could be the solution to prepare your team for the future of IoT.

Data strategy and analytics

IoT generates massive amounts of data, and deriving actionable insights from this data is crucial. Develop a robust data strategy that includes data collection, storage, analysis, and visualization. Leverage advanced analytics and AI to extract valuable information that informs decision-making, enhances products, and optimizes processes.

Security and privacy

IoT devices and networks create new avenues for cyber threats. Prioritize security by implementing strong encryption, authentication, and access controls for IoT devices and systems. Regularly update firmware and software to patch vulnerabilities. Incorporate privacy considerations to ensure that customer data is handled responsibly and ethically.

Scalable infrastructure

IoT requires a scalable and flexible IT infrastructure. Cloud computing and edge computing are essential components that enable efficient data processing, storage, and real-time analysis. Ensure your infrastructure can accommodate the increasing number of connected devices and the data they generate.

Partnerships and ecosystems

Collaboration is key in the IoT ecosystem. Establish partnerships with technology providers, IoT platform vendors, and data analytics experts. Build a network of partners that can support your IoT initiatives with their expertise and solutions.

Pilot projects and proof of concepts

Start with small-scale pilot projects to test IoT applications before full-scale implementation. These pilots help identify challenges, refine strategies, and demonstrate ROI to stakeholders. Once successful, scale up these projects gradually to get ready for the future of IoT.

Customer-centric approach

IoT should enhance customer experiences. Understand your customers’ needs and preferences to develop IoT solutions that address pain points and deliver value. Personalize offerings and services based on IoT-generated insights to create a more engaging and tailored experience.

Regulatory and compliance considerations

Stay informed about regulations and compliance standards relevant to getting ready for future of IoT in your industry and region. Ensure your IoT implementations adhere to data protection, safety, and environmental regulations.

Continuous innovation

IoT is an evolving landscape. Foster a culture of innovation that encourages employees to explore new IoT-driven opportunities. Monitor industry trends, emerging technologies, and customer feedback to adapt your IoT strategy accordingly.

Financial planning and investment

Develop a clear financial plan for your IoT initiatives. Allocate resources for hardware, software, talent acquisition, and ongoing maintenance. Consider the long-term ROI and potential revenue streams that the future of IoT could bring.

Change management

Transitioning to an IoT-driven business model may require organizational changes. Communicate the benefits of IoT to your workforce, manage resistance, and provide training to facilitate a smooth transition.

Measure and optimize

Regularly assess the outcomes of your IoT implementations. Measure KPIs such as efficiency gains, cost savings, customer satisfaction, and revenue growth. Use these insights to refine your IoT strategy and prioritize future initiatives.

The possibilities of IoT are vast and impactful across various sectors. From creating smarter and more sustainable cities to revolutionizing healthcare, enhancing automation and innovation, and improving transportation and agriculture, IoT’s potential is profound. The key to harnessing these possibilities lies in responsible implementation, addressing security and privacy concerns, and fostering collaboration among stakeholders. As we continue to embrace IoT’s potential, we move towards a connected world that is not only more efficient but also more responsive to the needs of individuals and society as a whole.

Featured image credit: fanjianhua/Freepik.

The world is becoming ever more interconnected; some are saying that it is ushering in a fourth industrial revolution, commonly referred to as the “Industrial Internet of Things.” This transformation enables objects for previously unconnected industries, machines, people and processes to interact with each other in unprecedented ways. Whether you are an end-user wanting access or control over products and services, or a manufacturer wishing to build better technology systems faster than before – one key component remains essential: embedded software development. In this blog post, we will go over why embedded software development lies at the core of any successful IoT product/application rollout, common practices being employed around its implementation by developers today including quality assurance tests they must comply with such as Platform Security Architecture (PSA) Certification programs, in addition, best practices companies should employ when partnering up with third-party embedded system vendors.

What is embedded software development and why is it important for IoT-enabled applications

Embedded software development plays a major role in the introduction of IoT-enabled applications. It corresponds to the application and programming of specific functionalities installed into devices such as sensors, wearables, mobile phones, and appliances so that they could communicate with each other using IoT protocols. The significance of embedded software development in IoT is derived from its capability of integrating various tools and letting them operate together seamlessly. Automatically through the right set of IoT protocols, it translates from machine language on one gadget to another allowing information exchange at a smooth rate. Consequently, it gives more satisfactory operation, accuracy, and responsiveness of IoT applications whereby it becomes hugely accepted by all users and efficient too. For up-to-date reference on embedded software development best practices, you can go for N-ix resources. In short, embedded software development makes IoT-enabled applications intelligent and more sophisticated thereby enabling establishing the background for the commencement of the next generation advancement of prodigiously innovative and ground-repeating technology.

Challenges and benefits of using embedded software in IoT systems

The Internet of Things (IoT) has transformed the way we live and work. Embedded software, specifically designed to handle this new reality, is a critical component to make it all possible. However, through IoT applications comes the challenge of integrating sensors into embedded software systems. Since IoT systems depend on sensors to capture and process data, incorporating them in embedded software can be quite daunting. But once accomplished, the benefits of embedded software in IoT systems are unimaginable: increased efficiency and automation; improved decision-making and resource allocation. The IoT ecosystem continues to change as more people and companies embrace the idea, making one thing very clear—understand and embrace the challenges and benefits of using embedded software in these systems.

Establishing a secure connection between devices on the network

The transmission of data is a critical business function and necessary for individuals, as well. It’s imperative that the connection between devices on the network is secure to ensure safety for sensitive information passing through the network from unauthorized access or cyber threats. Businesses need to value good data management principles that will create confidentiality, integrity, and availability from different domains throughout data movement. Secured protocols should include encryption and authentication since they protect information against loss into foreign hands. With increased risks of cyber attacks mainly because of online activities, it becomes extremely important to put in place enhanced security procedures – such as encryption and authentication to safeguard data while maintaining its confidentiality, integrity, and availability.

Designing programmable logic controllers to manage device interactions

As technology continues to advance, businesses are continuously trying to find ways of improving efficiency and minimizing costs. One way to achieve this is by the use of programmable logic controllers (PLCs) through which devices interact and can be managed. PLCs refer to these digital computers that control mechanical processes like an assembly line or chemical processing through sensors as well as other input devices for receiving data. These controllers can be designed so that they manage device interactions efficiently to increase productivity, lengthening uptimes among many others. With the ability to program these devices to meet customized needs, a business will have increased its scope of operations in such a way that would suit its unique nature requirements. The possibilities with PLCs are endless; hence making it a lucrative tool one can employ increases efficiency and regulations on the operation.

Integrating cloud services with embedded software programs to enable user interfaces

With new technologies emerging daily, cloud services and embedded software programs are quickly becoming the building blocks of our technology infrastructure. User benefits enabled through cloud services include responsiveness, scalability, and cost-effectiveness. On the other hand, both sensor integration into projects as well as embedded software program transformation into user applications facilitates users to conveniently build various devices and systems freely in their everyday life. Schemas used to enable input devices and industrial automation systems with cloud services will let us take another step forward to innovative user interfaces that operate almost invisibly even for proficient users. The inherent advantages of this development could make countless kinds of work easier for developers, engineers, managers or just curious full-function clients.

Final thoughts

Embedded software development is a necessity when thinking about IoT-enabled applications. It allows for the integration of cloud services, enabling the provision of device management services including secure connections between devices on the same network and connectivity within a certain IoT solution, helping to create a successful system. Such processes help to ensure that new protocols can be implemented quickly and efficiently to make sure data gets across within an IoT solution without getting corrupted or messed up in any way. Overall, embedded software should be considered important when trying to design anything modernly concerned with IoT systems, whether at home automation, industrial automation or wearable technology levels.

Featured image credit: Emile Perron/Unsplash

Industrial IoT networking is transforming the way industries operate by creating interconnected systems that boost efficiency, reliability, and adaptability. It represents a paradigm shift in the industrial landscape, heralding a new era where technology and innovation are at the core of operational excellence.

Industrial IoT (IIoT) refers to the interconnection of industrial equipment and devices through the Internet, allowing data to be collected, analyzed, and acted upon in real time. This sophisticated network provides an unparalleled level of connectivity, bridging the gap between physical machinery and intelligent software systems. The integration of sensors, communication networks, data analytics platforms, and automation tools creates a seamless ecosystem that enables smarter decision-making and real-time responsiveness.

As industries continue to evolve in the age of digital transformation, understanding the potential of IIoT becomes vital for organizations seeking to remain competitive. The adoption of IIoT isn’t merely a technological upgrade; it’s a strategic move that aligns operations with contemporary business demands. The ability to harness vast amounts of data, coupled with the power to analyze and interpret that data through advanced analytics, offers opportunities to innovate and excel in ways previously unattainable.

The real-time nature of IIoT means that industries can now monitor and respond to changing conditions instantaneously. Whether it’s a sudden change in equipment performance, an unexpected fluctuation in demand, or an emerging opportunity in the market, IIoT empowers organizations to act swiftly and with precision. This agility translates into more resilient operations that can weather uncertainties and capitalize on opportunities.

How does industrial IoT networking work?

IIoT works by interconnecting industrial machines and devices with sensors and software that collect data. This data is then sent to central systems for analysis, where insights can be derived to make informed decisions.

Data collection is the fundamental starting point for industrial IoT networking. Sensors and devices are strategically installed in various industrial equipment, ranging from heavy machinery to delicate instruments. These devices are capable of gathering an array of information, including temperature fluctuations, humidity levels, vibration patterns, pressure readings, and much more. The granularity and specificity of this data depend on the type of sensors used and the particular industry requirements. The objective is to gather real-time, actionable information that can provide insights into the functioning and condition of the equipment.

Once the data is collected, the next phase is to transmit this information to a centralized system where it can be processed and analyzed. This stage involves a complex process of data aggregation and transportation, often necessitating the conversion of raw data into a format suitable for transmission. Various communication methods can be employed, including wired connections like Ethernet or wireless technologies like Wi-Fi and cellular networks. The choice of the network depends on factors such as the volume of data, transmission speed requirements, and the specific industrial environment.

Upon receiving the transmitted data, the next critical step is processing and analysis. This phase often begins with data cleansing, where any noise or irrelevant information is filtered out. Following this, the data may undergo transformation and loading into an analytics system where advanced algorithms, possibly incorporating artificial intelligence and machine learning, are applied. The goal here is to dissect the data, identify patterns, and derive meaningful insights that reflect the actual conditions and performance of the industrial equipment.

The strategic value of IoT development and data analytics

The final stage in Industrial IoT networking is to convert the insights derived from data analysis into concrete actions within the industrial system. Depending on the findings, automated actions may be triggered, such as adjusting equipment parameters to optimize performance or sending alerts to human operators for intervention. In some cases, the system might initiate preventive measures if potential failures or inefficiencies are detected. This phase bridges the gap between data and decision-making, enabling a proactive and responsive industrial environment that leverages technology to enhance productivity, safety, and sustainability.

Key components of industrial IoT networking are as follows:

• Sensors: These are the eyes and ears of the IIoT system. They detect changes in physical conditions like temperature, pressure, light, etc. Different sensors can be used for various applications, depending on the specific needs of the industry.
• Communication network: This can be wired or wireless and serves as the conduit for data flow between devices. Options include Ethernet, Wi-Fi, cellular networks, and specialized industrial protocols like Zigbee or Modbus.
• Data processing & analysis tools: These are the brains of the IIoT system. They may include cloud-based or on-premises servers utilizing AI and machine learning algorithms to identify patterns, trends, and anomalies in the data.
• Human-Machine Interface (HMI): This allows human operators to interact with the IIoT system. It could be a dashboard displaying real-time data or controls allowing manual adjustments.

The complexity and functionality of Industrial IoT networking make it a powerful tool for modern industrial applications. By understanding its various components and applications, industries can utilize IIoT to drive innovation, efficiency, and growth. With continuous advancements in technology, the potential applications and benefits of IIoT continue to expand, making it a crucial aspect of the industrial landscape.

Wide range of industrial IoT networking applications

The applications of IIoT are as varied as they are impactful, touching virtually every aspect of industrial operation. From predictive maintenance and resource optimization to quality assurance, supply chain management, and compliance monitoring, IIoT provides a suite of tools that empower organizations to operate more efficiently, responsibly, and competitively.

One of the groundbreaking applications of industrial IoT networking is in the predictive maintenance area. Through continuous monitoring and analysis of equipment, IIoT systems can identify signs of wear and tear, unusual behavior, or other indicators that a machine might fail in the near future. This ability to predict failure before it happens allows organizations to schedule maintenance proactively rather than reactively. By addressing issues early, companies can avoid unexpected downtime and the associated costs, leading to more efficient operations and significant savings.

The industrial sector often requires the consumption of various resources like energy, water, and raw materials. Industrial IoT networking plays a crucial role in tracking and optimizing the utilization of these resources. By constantly monitoring consumption levels, evaluating efficiency, and providing insights into potential wastage, IIoT enables organizations to make informed decisions about resource allocation. This can lead to more sustainable use of resources, reduced operational costs, and an overall more environmentally friendly operation.

Quality is paramount in any production process, and industrial IoT networking provides an indispensable tool for maintaining and enhancing quality standards. Through constant monitoring of production processes, industrial IoT networking can detect deviations from predefined quality parameters, allowing immediate correction. This ongoing oversight ensures that quality standards are consistently met across all stages of production. The result is a higher quality end product, increased customer satisfaction, and a strengthened brand reputation.

Managing a complex supply chain can be a challenging task, but industrial IoT networking offers a solution by enabling real-time tracking of materials and products. With continuous visibility into the location and status of goods, companies can more accurately forecast delivery times, optimize inventory levels, and make data-driven decisions to enhance the overall efficiency of the supply chain. This level of control translates into a more agile and responsive supply chain, better alignment with customer demands, and a competitive advantage in the marketplace.

Compliance with environmental regulations and ensuring the safety of workers are critical aspects of industrial operations. industrial IoT networking technology can aid in monitoring environmental factors such as emissions, waste disposal, and energy consumption, providing accurate data to ensure compliance with regulatory standards.

Additionally, industrial IoT networking systems can track safety parameters within a facility, like air quality or hazardous conditions, allowing timely interventions to protect workers’ well-being. By integrating environmental and safety monitoring, organizations can foster a culture of responsibility and accountability, aligning operations with legal requirements and societal expectations.

How do you build one?

Understanding the specific needs and objectives of an organization is the foundation of any successful IIoT implementation. This involves a thorough assessment of the current systems, identifying areas where IIoT can add value, and defining clear objectives.

Whether it’s increasing efficiency, reducing downtime, improving quality, or achieving compliance, having a well-articulated goal aligns the project with the broader business strategy and ensures that all subsequent efforts are directed toward fulfilling that goal.

Choose the right technology

The selection of the right technology is a critical decision that can make or break an industrial IoT networking implementation. This includes choosing the appropriate sensors to collect relevant data, deciding on the communication network that suits the industrial environment (whether wired or wireless), and selecting data processing and analysis tools capable of providing meaningful insights.

A clear understanding of the requirements and a careful evaluation of the available technologies are essential to making the right choices that will support the intended objectives.

Design the network architecture

Creating a robust network architecture is a complex task that requires careful planning. This involves laying out the network topology, ensuring that all devices are properly connected, and creating pathways for data flow that are efficient, scalable, and secure.

Considerations should include the layout of physical locations, integration with existing systems, cybersecurity measures, and future expansion possibilities. A well-designed network architecture ensures smooth data transmission and scalability as the system grows.

Implement and test

The implementation phase for industrial IoT networking is where the plan comes to life. This involves installing sensors, setting up communication networks, configuring data processing tools, and integrating everything into a cohesive system. Initial testing on a smaller scale or through a pilot project allows for identifying any potential issues or gaps in the design.

Rigorous testing ensures that the system functions as intended and that any problems are addressed before a full-scale rollout.

Monitor and optimize

Once the industrial IoT networking system is up and running, continuous monitoring and optimization become crucial. Regularly reviewing the system’s performance helps in identifying areas for improvement, whether it’s enhancing efficiency, reducing latency, or improving data accuracy.

Regular updates, adjustments and fine-tuning keep the system aligned with the evolving needs of the organization and ensure that it continues to deliver value.

In a global economy characterized by rapid change and intense competition, the role of industrial IoT networking as a strategic asset cannot be overstated. It’s no longer an optional upgrade but a necessary investment for any industry aiming to thrive in the modern world.

By utilizing industrial IoT networking, organizations can unlock new levels of efficiency, adaptability, and innovation, positioning themselves at the forefront of industrial evolution. The convergence of machines, data, intelligence, and human expertise sets the stage for a future where industries are not merely surviving but thriving, driven by the relentless pursuit of excellence and the endless possibilities of the connected world.

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Are you tired of juggling multiple devices and wires, feeling tied down to your desk, or missing out on life’s precious moments? Then the wearable computers should be your way to go!

The wearable computer is your gateway to a dynamic and liberating experience, offering a perfect blend of style and substance. Gone are the days of bulky gadgets and cumbersome accessories. Today, wearable computers have emerged as the epitome of technological elegance, effortlessly elevating your lifestyle.

Picture yourself sporting a sleek smartwatch that not only tells time but acts as your personal assistant, delivering notifications, tracking your fitness, and even enabling quick payments on the go. Feels awesome, right?

What is a wearable computer?

In today’s rapidly advancing technological landscape, the concept of wearable computers has become increasingly prominent. A wearable computer is a revolutionary gadget that seamlessly integrates into our daily lives, offering a host of functionalities while being compact enough to wear comfortably on our bodies.

From smartwatches to wristbands and smart glasses, wearable computers are redefining the way we interact with technology.

The concept of wearable computers may seem like a product of the modern digital age, but its roots can be traced back several decades. The evolution of wearable computers has been a fascinating journey, marked by technological breakthroughs and visionary ideas.

Let’s take a brief trip through time to explore the key milestones in the history of wearable computers.

1960s – Early visionaries

The seeds of wearable computers were sown in the 1960s when visionaries like Ivan Sutherland and Douglas Engelbart conceptualized early versions of wearable devices.

Sutherland’s “Sword of Damocles” was an influential head-mounted display, while Engelbart’s “Augmentation Research Center” showcased the idea of using computers to augment human intelligence and improve collaboration.

1970s – Portable computers

The 1970s saw the emergence of portable computers that laid the groundwork for wearable technology. Devices like the Xerox PARC ALTO, considered one of the first personal computers, showcased the potential for miniaturization and mobility.

Although not wearable in the modern sense, these early portable computers set the stage for future innovations.

1980s – Wearable cameras

In the 1980s, Steve Mann, often referred to as the “father of wearable computing,” began developing wearable cameras that allowed users to capture their perspective in real-time.

These early experiments in lifelogging and first-person perspectives laid the foundation for future wearable technologies focused on capturing and processing data in real-time.

1990s – Wearable assistants

The 1990s witnessed significant advancements in wearable technology, with devices like the MIT Media Lab’s “Personal Information Environments” (PIE) and the IBM “WristPad” showcasing wearable computing concepts.

Exploring the dynamic fusion of AI and the IoT

These devices functioned as personal assistants, providing calendar updates, reminders, and communication capabilities.

2000s – Fitness trackers and smartwatches

The early 2000s marked a turning point for wearable computers, with the introduction of fitness trackers and early smartwatches. Devices like the Fitbit, launched in 2007, revolutionized the fitness industry by offering simple yet effective health monitoring capabilities.

Meanwhile, companies like Pebble started experimenting with smartwatches, bridging the gap between fashion and technology.

2010s – Mainstream adoption

The 2010s were a watershed moment for wearable computers as they started gaining mainstream acceptance. The launch of products like the Apple Watch in 2015 catapulted wearable technology into the spotlight, offering advanced features like heart rate monitoring, app integrations, and cellular connectivity.

How do wearable computers work?

Wearable computers may appear sleek and unassuming on the outside, but beneath their stylish exteriors lies a complex integration of hardware, software, and communication technologies.

These devices are designed to be compact, energy-efficient, and user-friendly, enabling seamless interaction with the user while offering a range of functionalities.

Hardware components

At the heart of every wearable computer is a collection of essential hardware components.

These typically include:

• Processor (CPU): The central processing unit is the brain of the wearable computer. It executes instructions, performs calculations, and manages data processing tasks
• Memory (RAM): Random Access Memory serves as temporary storage for data and program instructions that the CPU needs to access quickly
• Storage (Flash Memory): Flash memory is used for long-term data storage, including operating system files, applications, and user data
• Sensors: Wearable computers often incorporate various sensors, such as accelerometers, gyroscope, heart rate monitors, GPS, and ambient light sensors. These sensors collect data from the user’s surroundings and body to provide personalized services and track various health and fitness metrics
• Display: Wearable computers typically feature a small, high-resolution display that can be easily viewed on the user’s wrist, glasses, or other wearable form factors
• Battery: Due to their compact size, wearable computers rely on efficient and long-lasting batteries. Battery life is a crucial consideration to ensure uninterrupted usage throughout the day

Software and operating system

Wearable computers run on specialized operating systems (OS) optimized for their small form factors and specific functionalities. These OSs are designed to be resource-efficient and support seamless communication with other devices, such as smartphones or computers.

Popular wearable OSs include Wear OS (formerly Android Wear), watchOS for Apple Watch, and Tizen for Samsung smartwatches.

The OS enables the user to navigate through the device’s interface, access applications, and control various settings.

It also manages the communication between the hardware components and interprets the data collected by the sensors.

User interface and interaction

Wearable computers provide a user interface that is intuitive and easy to navigate despite the limited screen size.

Interaction with the device can occur through various methods:

• Touchscreen: Many wearables, like smartwatches, utilize touchscreens for user input, similar to smartphones
• Voice commands: Voice recognition technology enables users to interact with their wearable devices by giving spoken commands
• Gestures: Some wearable computers allow users to perform gestures, such as swiping, tapping, or shaking, to trigger specific actions
• Buttons or rotating crowns: Physical buttons or rotating crowns may be present on the device, providing additional control options

Data synchronization and communication

Wearable computers rely on data synchronization to ensure a seamless experience across multiple devices. For example, a smartwatch may synchronize health data, notifications, or application data with a paired smartphone or cloud storage.

Wearable devices communicate with other devices to send and receive data, enabling features like call notifications, text messaging, and app notifications.

Connectivity

Wearable computers often connect to other devices, such as smartphones or computers, to extend their capabilities. This connectivity can be achieved through:

• Bluetooth: The most common method for establishing a wireless connection between the wearable and a smartphone or computer
• Wi-Fi: Some wearables support Wi-Fi connectivity, allowing direct access to the internet without relying on a paired smartphone

Applications and ecosystem

Wearable computers support a wide range of applications designed to cater to various needs, such as fitness tracking, communication, navigation, and productivity.

App developers create applications specifically optimized for wearable devices, offering users a diverse ecosystem of tools to enhance their wearable experience.

We’re not really strangers to it

We find ourselves embracing a realm of technology that seamlessly intertwines with our lives, becoming an integral part of our daily experiences. Wearable computers, with their sleek designs and cutting-edge capabilities, are no longer a novelty but a familiar companion that effortlessly bridges the gap between the digital and physical world.

From the smartwatches adorning our wrists, keeping us connected and informed, to the augmented reality glasses that open doors to enchanting virtual dimensions, wearable computers have become an extension of ourselves, effortlessly enhancing our productivity, enriching our social interactions, and empowering us to live healthier, more fulfilled lives.

Smartwatch

The smartwatch has emerged as a ubiquitous wearable device that sits comfortably on the wrist. Apart from displaying the time, smartwatches offer a plethora of features, including notifications for calls, messages, and emails.

They can monitor heart rate, track fitness activities, and even act as a digital wallet for contactless payments. With touchscreens and voice commands, smartwatches are incredibly user-friendly and have become a fashion statement in their own right.

Activity trackers

Activity trackers, also known as fitness bands, are dedicated wearable devices aimed at monitoring physical activity and health metrics. They can count steps, measure distance, and estimate calorie expenditure.

These trackers help users maintain an active lifestyle and encourage them to set and achieve fitness goals. By syncing data to smartphones or computers, users can keep track of their progress and make informed decisions to improve their overall well-being.

Head-up Display (HUD)

HUD is a groundbreaking wearable technology that brings vital information directly into a user’s field of view. Initially developed for aviation, HUDs are now being used in various sectors, including automotive and sports.

By projecting data, such as speed, navigation instructions, and alerts, onto a transparent screen or directly onto glasses, HUDs enhance situational awareness without obstructing the user’s vision.

Smart glasses

Smart glasses are wearable computers cleverly disguised as regular eyeglasses. They come equipped with displays, cameras, and speakers, allowing users to access information and interact with the digital world through augmented reality.

From displaying maps and restaurant reviews to providing real-time language translations, smart glasses have numerous practical applications that can enhance daily life.

Body sensors

Body sensors encompass a broad category of wearable devices that attach to the body to monitor specific physiological parameters. These devices can track heart rate, blood pressure, body temperature, and more.

They are commonly used in healthcare settings, helping doctors and patients monitor chronic conditions and make informed medical decisions. Moreover, athletes and fitness enthusiasts use body sensors to optimize their training and performance.

Wristbands

Wristbands, often used interchangeably with smartwatches, focus primarily on health and fitness tracking. These minimalist wearables offer an unobtrusive way to monitor various metrics, such as sleep patterns, heart rate, and daily activity levels.

Some wristbands are designed specifically for children or the elderly, promoting a healthier lifestyle across different age groups.

What is next for wearable computers?

The future of wearable computers holds exciting and transformative possibilities. As technology continues to advance, we can expect wearable devices to evolve in various ways, offering even more seamless integration into our lives and unlocking new capabilities.

Enhanced health monitoring

Health and fitness tracking have been significant features of wearable computers, but we can anticipate even more sophisticated health monitoring capabilities. Wearable devices could incorporate advanced sensors for continuous monitoring of vital signs, blood glucose levels, hydration levels, and even early detection of potential health issues. These advancements may lead to wearable devices playing a more active role in preventive healthcare and remote patient monitoring.

Augmented reality experiences

Smart glasses and other augmented reality wearables are expected to become more immersive and capable of providing a seamless blend of digital and physical worlds. As augmented reality technology improves, wearable computers could enhance our experiences with real-time information overlays, interactive elements, and virtual object manipulation, revolutionizing fields such as education, training, entertainment, and productivity.

AI-driven personalization

Artificial intelligence will play a significant role in the future of wearable computers. AI algorithms can analyze user data, preferences, and behavior patterns to offer highly personalized experiences.

Wearables might anticipate our needs, make intelligent recommendations, and adapt their functionality based on context, making them even more indispensable and user-friendly.

Flexible and wearable displays

Advancements in flexible display technology will allow for more comfortable and versatile form factors. Wearable computers may adopt flexible or even rollable displays, enabling unique designs that can adapt to different parts of the body or seamlessly integrate into clothing and accessories. This flexibility could lead to wearable computers that are almost indistinguishable from regular attire.

Long-lasting batteries and energy harvesting

Battery life has been a crucial consideration for wearables. In the future, wearable computers are likely to feature more efficient power management systems, longer-lasting batteries, and even energy harvesting capabilities. This could allow wearables to operate for extended periods without frequent charging or even draw energy from the environment to power themselves.

Contextual awareness and predictive actions

Future wearable computers may become more contextually aware, using sensor data, user patterns, and environmental cues to anticipate user needs and proactively provide relevant information or perform tasks.

For example, wearables could automatically adjust settings based on the user’s location, time of day, or activity, offering a seamless and intuitive experience.

Expanded connectivity and integration

As wearables become more integrated into our lives and gather increasingly sensitive data, privacy and security measures will become paramount. Wearable manufacturers will focus on robust encryption, secure data transmission, and user-friendly privacy controls to protect user information effectively.

From their humble beginnings as visionary concepts to their present state as sleek, user-friendly companions, wearable computers have evolved into a technological marvel that enriches and empowers our lives.

With every tap on a touchscreen, every spoken command, and every glance at the display, wearable computers seamlessly integrate into our daily routines, becoming an extension of ourselves.

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In today’s interconnected world, the Internet of Things (IoT) has become an awe-inspiring reality rather than a futuristic concept. With billions of devices connected globally, businesses in all industries embrace IoT technologies to drive innovation, enhance operational efficiency, and provide exceptional experiences to their target audiences. By leveraging the power of IoT, organizations can tap into new possibilities, streamline processes, and gain a technological edge in this digital ecosystem.

According to a recent report by IoT Analytics, the global number of IoT connections grew by 18% in 2022, reaching 14.3 billion active IoT endpoints. The report predicts a further 16% increase in 2023, resulting in 16.7 billion connected IoT devices worldwide. These statistics highlight IoT technology’s ongoing expansion and adoption across industries, indicating its potential for continued growth.

A Statista report also suggests that the global IoT revenue is expected to reach $2,227 billion by 2028, witnessing a CAGR of 13.60% from 2023 to 2028. Thus, now is the right time if you, too, wish to leverage the increasing market share of the IoT ecosystem for your business and get maximum ROI.

Selecting a reputable IoT software development services provider becomes paramount when embarking on an IoT project that will bring your idea to life. Collaborating with such a company ensures that your project is in capable hands, benefiting from their expertise and track record in delivering outstanding IoT solutions.

This article will feature the best IoT app development companies in the US that can help you streamline your business process by tapping into the world of connected devices. So, without further ado, let’s dive right into the details for them all.

List of top10 IoT software development companies in the USA (2023)

Appinventiv

Appinventiv is a renowned digital solution provider in the US that boasts a large team of over 1000 skilled IoT developers. Established in 2015, the company has garnered recognition in the industry through its impressive portfolio, showcasing the expertise of its software professionals across varied verticals. The organization has been honored with the prestigious “Tech Company of the Year” title by the Times Business Awards 2023.

Appinventiv is a leading IoT app development company whose comprehensive range of IoT software development services encompasses IoT consulting, IoT app development, IoT wearable connectivity development, IoT testing and maintenance, and AIoT, among others. The services offered by their experts have been known to enhance efficiency and productivity across various industrial sectors, including healthcare, finance, automotive, agriculture, logistics, and supply chain.

Appinventiv’s IoT services empower organizations to capitalize on the capabilities of interconnected devices and utilize data to drive better decision-making and operational effectiveness. The experts at Appinventiv leverage their industry knowledge and cutting-edge digital tools, such as AI, cloud computing, and data analytics, to enable organizations to fully unlock the power of IoT.

Their customer-centric and design-led approach to software development has fostered successful collaborations with a global clientele that includes the likes of American Express, Vodafone, KPMG, Asian Bank, EmiratesNBD, Virgin Group, Adidas, Americana Group, and Bodyshop. The organization is known to have delivered reliable, innovative, and cost-effective solutions across the US, North Africa, Australia, Europe, the Middle East, Qatar, and India, leading to its widespread global presence and unique identity among its competitors.

IBM

IBM is another of the top IoT software development companies in the US that has been in the game for a long time. The organization was founded in 1911 and provides a wide range of services and solutions to businesses across the globe.

Their IoT offerings include services such as IoT platform development, device management, data analytics, and security solutions. Their business solutions allow businesses to connect, manage, and analyze data from numerous devices, enabling advanced insights and automation.

Accenture

Being a global technology company, Accenture has been providing various IoT software development services, including IoT strategy and consulting, solution architecture, development and implementation, data analytics, and cybersecurity. Founded in 1989, Accenture offers business solutions that extract valuable insights from data and optimize processes to drive innovation and efficiency.

Cisco

Cisco is another recognized organization that offers comprehensive IoT development services in the USA. The company was launched in 1984 and offers its clients IoT solutions across various industrial domains, which involve networking infrastructure, edge computing, security, data analytics, and IoT platform development.

It empowers businesses to leverage the full potential of the IoT ecosystem, navigate the connected world, and achieve digital growth.

Oracle

Oracle is a leading IoT services provider that offers a wide range of IoT solutions to organizations that are seeking digital transformation. With a strong industry presence, Oracle has been delivering innovative services since its establishment. Their IoT offerings encompass network infrastructure, data analytics, edge computing, security solutions, and IoT platform development. These comprehensive services cater to various industries, enabling businesses to connect devices and derive valuable insights from data effortlessly.

Infosys

Infosys is a multinational IT services company founded in 1981 that offers a comprehensive range of IoT services to enable organizations in their digital transformation journey. Their IoT services encompass the likes of IoT consulting, solution architecture, IoT application development, IoT data analytics, and IoT security. By leveraging their expertise, businesses can seamlessly navigate the complexities of the IoT ecosystem.

PTC

PTC has been one of the leading players in the US software industry for the past three decades. With an employee base of more than 10,000 skilled professionals, PTC is committed to providing cutting-edge IoT services and solutions. By leveraging their vast expertise and industry knowledge, PTC enables organizations to tap into the potential of connected devices and explore new opportunities in the digital ecosystem. Their services include IoT platform development, device connectivity, data analytics, remote monitoring, predictive maintenance, etc.

Oxagile

Oxagile is a leading IoT development company that offers a wide range of services to help businesses with their IoT projects. From initial consulting to developing IoT software, creating hardware prototypes, integrating systems, and continuously improving solutions, Oxagile covers the entire development process. With their expertise in technologies like AI, ML, computer vision, and big data, they deliver innovative and connected solutions for various industries.

GE Digital

GE Digital is a prominent IoT software development company in the US offering a comprehensive suite of solutions to facilitate businesses’ digital transformation journey. Their IoT services revolve around real-time data analytics, predictive maintenance, remote monitoring, and asset performance management. By leveraging the potential of connected devices and advanced analytics, GE Digital empowers businesses to acquire valuable insights, make well-informed decisions, and unlock fresh avenues for growth.

Bosch IoT

Bosch IoT Suite is a division of Bosch that offers IoT services such as IoT device connectivity, data management, cloud platforms, analytics, and application development. Their solutions cater to diverse industrial domains such as manufacturing, automotive, agriculture, energy, and smart homes. Businesses can gain a competitive edge in the digital era by leveraging their custom digital solutions.

Summing up

Selecting the right IoT development company from the list we have just shared can significantly impact the success of your digital transformation efforts. Each company possesses distinct strengths, expertise, and a track record of delivering exceptional IoT solutions. By partnering with the right firm, you can confidently embark on your digital journey, knowing that you have a capable ally by your side.

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Welcome to the realm of ambient intelligence (AmI), a visionary concept that transcends the boundaries of traditional computing and invites us into a realm of seamless interconnectivity and personalized experiences. In this captivating landscape, our homes become sentient companions, our cities evolve into responsive ecosystems, and our devices transform into intuitive extensions of ourselves.

What is ambient intelligence?

Ambient intelligence deals with a new world where computing devices are spread everywhere, allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviours.

Characteristics of ambient intelligence

AmI manifests itself in various real-world applications, enhancing our daily lives and transforming the way we interact with our environments. These examples showcase the power and potential of ambient intelligence to create intelligent, adaptive, and personalized experiences.

Let’s explore some notable instances where ambient intelligence has made a significant impact:

• Ubiquitous presence: Ambient intelligence envelops our surroundings, seamlessly integrating technology into our environment. It pervades our homes, workplaces, public spaces, and even our personal devices, creating a pervasive and interconnected network of intelligent systems. It goes beyond traditional computing devices, encompassing sensors, actuators, and smart objects that effortlessly collaborate to enhance our daily experiences.
• Context awareness: Ambient intelligence is acutely attuned to the context in which it operates. Through the use of sensors and data collection, it gathers information about the environment, users, and their activities. It understands and adapts to factors such as location, time, temperature, light, noise, and user preferences. This contextual awareness enables ambient intelligence to anticipate our needs and provide personalized and tailored experiences.
• Intelligent adaptability: One of the defining features of ambient intelligence is its ability to adapt and respond intelligently to changing circumstances. It continuously analyzes and learns from the data it collects, making adjustments and improvements over time. This adaptability allows it to optimize its functionalities, enhance user experiences, and seamlessly integrate new technologies or services as they emerge.
• Proactive and reactive capabilities: Ambient intelligence possesses both proactive and reactive capabilities. It proactively anticipates user needs and initiates actions or suggestions before users explicitly request them. For example, it can adjust the lighting and temperature in a room based on detected user preferences and environmental conditions. Additionally, it reacts responsively to user commands or requests, dynamically adapting to user inputs and providing real-time feedback.
• Seamless interactions: Ambient intelligence aims to make technology interactions natural, intuitive, and unobtrusive. It seeks to minimize the need for explicit user inputs by using voice recognition, gesture control, and context-based automation. This enables effortless and hands-free interactions, allowing users to focus on their tasks or activities without being encumbered by traditional device interfaces.
• Personalization and user-centricity: Ambient intelligence places a strong emphasis on personalization and tailoring experiences to individual users. By understanding user preferences, habits, and behavior patterns, it can provide personalized recommendations, suggestions, and services. It strives to create environments that adapt to users’ unique needs, enhancing comfort, productivity, and overall well-being.

Benefits of ambient intelligence

Let’s delve into the multitude of benefits that ambient intelligence brings to our lives.

Enhanced UX

Ambient intelligence revolutionizes the way we interact with technology, creating intuitive and natural user experiences. Through context-awareness and proactive adaptability, ambient intelligence anticipates and fulfills our needs, minimizing the need for explicit user inputs. Imagine a home environment that automatically adjusts lighting, temperature, and music based on your preferences, or a workplace that intelligently adapts to support your productivity. These personalized experiences enhance comfort and convenience, making technology seamlessly integrated into our daily lives.

Increased efficiency and productivity

Ambient intelligence streamlines processes and automates routine tasks, enabling increased efficiency and productivity. Smart homes, for example, can optimize energy usage by intelligently controlling heating, cooling, and lighting based on occupancy and environmental conditions. In industrial settings, ambient intelligence can enhance manufacturing processes through real-time monitoring, predictive maintenance, and adaptive control systems. By reducing manual intervention and optimizing resource allocation, ambient intelligence empowers individuals and businesses to achieve more with less effort.

Personalized services and recommendations

Ambient intelligence understands our preferences, habits, and behavior patterns, enabling personalized services and recommendations. Through continuous data collection and analysis, it tailors experiences to individual needs, enhancing customer satisfaction and engagement. Online platforms like streaming services and e-commerce websites utilize ambient intelligence to provide personalized content recommendations based on our viewing or purchasing history. This level of personalization not only enhances user experiences but also drives customer loyalty and business growth.

Energy efficiency and sustainability

Ambient intelligence plays a vital role in promoting energy efficiency and sustainability. By monitoring and optimizing resource usage in real-time, it helps minimize waste and conserve energy. In smart grid systems, ambient intelligence enables intelligent load management and demand response, balancing energy supply and demand to reduce the overall consumption. This efficient utilization of resources contributes to a greener and more sustainable future, addressing environmental challenges and promoting responsible practices.

Accessibility and inclusivity

Ambient intelligence has the potential to break down barriers and promote accessibility and inclusivity for individuals with disabilities. By integrating assistive technologies and adaptive interfaces, ambient intelligence can facilitate independent living, communication, and mobility for people with diverse abilities. For instance, smart home automation can enable voice-activated controls for individuals with mobility impairments, while intelligent personal assistants can provide assistance and support for people with visual or hearing impairments.

Ambient intelligence applications

Ambient intelligence has found extensive applications across diverse industries, revolutionizing the way we interact with technology and creating intelligent, adaptive environments. From healthcare to robotics, ambient intelligence is making significant contributions and opening up new possibilities. Let’s explore how ambient intelligence is transforming these industries.

The rise of machine learning applications in healthcare

Ambient intelligence in healthcare

Currently, the healthcare sector stands at the forefront of technological advancements, leading to a wide range of innovative initiatives. Notably, two significant developments in this field include home healthcare services and hospital support systems.

The overarching goal is to minimize healthcare expenses while simultaneously improving accessibility, customization, and availability of services. In the context of home healthcare, many initiatives focus on assisting the elderly population, aiming to enhance their independence and overall quality of life.

Within healthcare institutions, initiatives can address both specific scenarios, such as providing specialized care for Alzheimer’s patients, and more general applications, like utilizing discrete hidden Markov models to classify healthcare practitioners’ activities. The diverse nature of these initiatives highlights the healthcare industry’s commitment to leveraging technology for improved patient care and operational efficiency.

Ambient intelligence in robotics

For decades, robots have been a prominent element in works of artificial intelligence fiction. The prevailing belief was that in the near future, each individual would have a humanoid robotic companion, capable of fulfilling a variety of roles and catering to our every need. However, research and technological advancements have indicated a different trajectory.

The probable future lies in an ecosystem of agents, consisting primarily of our existing appliances and hardware, which communicate and collaborate with one another through enabling middleware to serve us. It is conceivable that robots of the future will mimic human-like behavior, seamlessly integrating into our environment and moving effortlessly through it while providing a wide range of services.

This shift in perspective reflects the evolving landscape of robotics and emphasizes the importance of creating a cohesive and interconnected network of intelligent agents to assist us in our daily lives.

Ambient intelligence in education

The rapid advancement of technology has brought about significant changes in the way teachers and students interact, with face-to-face communication being replaced by reliance on communication technologies. However, these technologies often fall short as substitutes for the richness of in-person communication processes.

To address these challenges resulting from technological advancements, ambient intelligence emerges as a promising strategy. One issue that arises is the lack of contextual information in existing internet communication technologies, which can lead to a perceived cold and impersonal communication experience. The shift from face-to-face conversations to online forums and text-based discussions hampers effective communication between teachers and students.

Ambient intelligence finds valuable applications in emergency services, which operate in complex and critical environments. In this context, the primary goals include real-time delivery of mission-critical information and coordination among multiple participants.

Ambient intelligence can facilitate the achievement of these objectives by enabling seamless communication and coordination. Furthermore, it holds potential for shaping the future of emergency services by enabling the rapid formation of virtual response teams and facilitating effective communication and coordination among team members, even across international borders.

Ambient intelligence in transportation

Considering the considerable time we spend traveling, the transportation sector offers potential advantages through the implementation of ambient intelligence. Equipping buses and vehicles with advanced technology can provide essential information about system performance and suggest improvements for more efficient utilization, thereby enhancing user experiences.

Examples of these applications include GPS-based positioning for public transportation vehicles and services, as well as vehicle identification and image processing to enhance transportation efficiency, safety, and identify points of interest in congested areas.

Ambient intelligence in commerce

Retailers have long employed wireless technologies like iBeacon to engage with smartphone users within brick-and-mortar stores. In line with their Online to Offline (O2O) strategies, e-commerce giants like Amazon and Alibaba are exploring the idea of unmanned supermarkets such as Amazon Go and Tao Cafe.

These establishments heavily leverage ambient intelligence (AmI) technology to provide a personalized shopping experience that mirrors online browsing, operating 24/7 and offering convenience comparable to online shopping.

Examples of ambient intelligence

• Amazon Go: Amazon Go is a project by the e-commerce giant Amazon that showcases the implementation of ambient intelligence in the retail sector. Amazon Go stores are cashierless supermarkets that utilize a combination of computer vision, sensor fusion, and deep learning algorithms to enable a seamless shopping experience. Customers can enter the store using their Amazon account, pick up items, and simply walk out without the need for traditional checkout. Ambient intelligence technologies track the items selected and automatically charge the customers’ Amazon accounts, offering a frictionless and personalized shopping experience.
• Google Nest: Google Nest is a brand that encompasses a range of smart home devices, including thermostats, cameras, doorbells, and speakers. These devices utilize ambient intelligence to create a connected and intelligent home environment. For example, the Nest Learning Thermostat leverages machine learning algorithms to learn and adapt to users’ temperature preferences and schedule, optimizing energy usage. Google Nest devices work in harmony, providing seamless control and automation for a personalized and convenient living experience.
• Philips Hue: Philips Hue is a brand that offers smart lighting solutions. With the use of ambient intelligence, Philips Hue lighting products can be controlled and customized to create personalized lighting experiences in homes and other environments. Through the accompanying app or voice commands via virtual assistants like Amazon Alexa or Google Assistant, users can adjust the color, brightness, and timing of their lights. The ambient intelligence capabilities of Philips Hue enable dynamic lighting scenes and automation, enhancing the ambiance and comfort of any space.

Privacy and security in ambient intelligence

As our surroundings become more intelligent and interconnected, ensuring the confidentiality, integrity, and availability of data is of paramount importance.

Amidst the realm of ambient intelligence, where our surroundings become infused with smart technologies, concerns around data protection and privacy take center stage. These systems gather vast amounts of information, spanning from personal preferences to sensitive health or financial data, necessitating robust measures to shield against unauthorized access, breaches, or misuse. Employing techniques like encryption, secure protocols, and data anonymization stands as the bulwark safeguarding privacy and cultivating trust.

Respecting user consent and bestowing control over data collection and usage lie at the core of ambient intelligence principles. Users deserve transparent options to grant or revoke consent for data sharing and personalized services. Clear privacy policies, intuitive interfaces, and granular controls empower individuals to make informed decisions about their data’s fate within the realms of ambient intelligence.

Ambient intelligence thrives on seamless communication and interactions across devices, sensors, and networks. Implementing secure communication protocols such as encryption and authentication mechanisms assumes paramount importance to preserve the confidentiality and integrity of transmitted data. This ensures that sensitive information remains shielded, even in the intricacies of highly interconnected environments.

Trustworthiness becomes the cornerstone for user acceptance and adoption of ambient intelligence systems. Guaranteeing the security and reliability of underlying technologies, algorithms, and decision-making processes emerges as an imperative. Regular security audits, vulnerability assessments, and adherence to industry standards weave the fabric of confidence in the integrity and safety of these intelligent systems.

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Ethical considerations come to the forefront in the realm of ambient intelligence. Responsible and ethical handling of data, mitigating biases in algorithmic decision-making, and tackling potential discriminatory or invasive practices form the bedrock of trust and societal acceptance. Transparent governance frameworks and ethical guidelines guide the development and deployment of these systems, aligning them with societal values.

Ambient intelligence vs artificial intelligence

Ambient intelligence and artificial intelligence are two interconnected but distinct concepts that contribute to the advancement of intelligent systems. While they share some similarities, they differ in their scope, focus, and application. Let’s explore the comparison between ambient intelligence and artificial intelligence:

Ambient intelligence:

• Focuses on creating intelligent environments that seamlessly integrate technology into our surroundings.
• Aims to enhance user experiences, adapt to context, and proactively fulfill user needs.
• Leverages sensors, actuators, and interconnected systems to collect and analyze data in real-time.
• Emphasizes context-awareness, personalized interactions, and adaptive behaviors.
• Examples include smart homes, intelligent transportation systems, and personalized healthcare.

Artificial intelligence:

• Centers around developing intelligent systems capable of performing tasks that typically require human intelligence.
• Focuses on reasoning, problem-solving, learning, and decision-making capabilities.
• Utilizes algorithms, machine learning, and data analytics to extract insights and make intelligent predictions.
• Applies to various domains, including natural language processing, computer vision, robotics, and autonomous systems.
• Examples include virtual assistants, image recognition algorithms, and self-driving cars.

While ambient intelligence concentrates on creating intelligent environments and enhancing user experiences in real-time, artificial intelligence revolves around developing intelligent systems capable of reasoning, learning, and problem-solving. Both concepts play essential roles in advancing technology, with AmI focusing on integrating intelligence into our surroundings and artificial intelligence focusing on the development of intelligent algorithms and systems.

The integration of artificial intelligence in Internet of Things introduces new dimensions of efficiency, automation, and intelligence to our daily lives. Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions. When combined, artificial intelligence in Internet of Things opens up a realm of possibilities, enabling intelligent, autonomous systems that can analyze vast amounts of data and take actions based on their insights.

The Internet of Things refers to the network of interconnected physical devices, vehicles, appliances, and other objects embedded with sensors, software, and network connectivity. These devices collect and exchange data, creating a massive ecosystem that connects the physical and digital worlds. On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans.

By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions. This combination empowers IoT devices to adapt to changing circumstances, optimize their operations, and provide personalized experiences to users.

The significance of artificial intelligence in Internet of Things cannot be overstated. It has the potential to unlock unprecedented opportunities across various sectors, including healthcare, transportation, manufacturing, agriculture, and smart cities. By harnessing the power of AI in IoT, we can create intelligent ecosystems where devices seamlessly communicate, collaborate, and make intelligent choices to improve our lives.

The intersection of artificial intelligence and Internet of Things

The fusion of artificial intelligence (AI) and the Internet of Things creates a powerful combination that propels the capabilities of IoT devices to new heights. Let’s explore the fascinating intersection of these two technologies and understand how AI enhances the functionalities of IoT.

Understanding the relationship between artificial intelligence and Internet of Things

The Internet of Things revolves around connecting physical objects and enabling them to gather and share data. On the other hand, artificial intelligence focuses on creating intelligent systems that can learn, reason, and make decisions. When AI and IoT converge, we witness a synergy where AI empowers IoT devices with advanced analytics, automation, and intelligent decision-making.

By integrating AI with IoT, devices gain the ability to interpret and analyze massive amounts of data collected from sensors and other sources. This enables them to extract valuable insights, identify patterns, and make informed decisions in real-time. AI algorithms can uncover hidden correlations within IoT data, enabling predictive analytics and proactive actions.

How does AI enhance the capabilities of IoT devices?

Artificial intelligence supercharges IoT devices with enhanced capabilities, making them smarter and more efficient. Here are some ways AI enhances IoT devices:

Advanced data analysis

AI algorithms can process and analyze vast volumes of IoT-generated data. By leveraging techniques like machine learning and deep learning, IoT devices can identify trends, anomalies, and patterns within the data. This analysis provides valuable insights for optimizing processes, predicting maintenance needs, and detecting potential risks or failures.

Intelligent automation

AI empowers IoT devices to automate tasks and processes intelligently. By learning from historical data and user behavior, IoT devices can automate routine actions, adjust settings, and optimize energy consumption. For example, smart thermostats can learn the temperature preferences of occupants and adjust the heating or cooling accordingly, leading to energy savings and personalized comfort.

Real-time decision-making

With AI, IoT devices can make decisions in real-time based on the data they collect and analyze. This enables them to respond quickly to changing conditions or events. For instance, in a smart grid system, AI algorithms can analyze electricity consumption patterns and adjust power distribution to ensure efficient usage and prevent outages.

Real-world applications of artificial intelligence in Internet of Things

The integration of AI in IoT has spurred numerous real-world applications across industries. Here are a few examples:

Smart healthcare

AI-powered IoT devices enable remote patient monitoring, personalized healthcare recommendations, and early detection of health issues. Wearable devices equipped with sensors and AI algorithms can continuously monitor vital signs, detect anomalies, and alert healthcare providers in case of emergencies.

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Autonomous vehicles

AI-driven IoT plays a vital role in the development of self-driving cars. These vehicles rely on AI algorithms to interpret sensor data, make real-time decisions, and navigate through complex road conditions. The fusion of AI and IoT enables autonomous vehicles to optimize their routes, avoid collisions, and enhance passenger safety.

Industrial automation

AI in IoT revolutionizes industrial processes by enabling predictive maintenance, optimizing supply chains, and improving operational efficiency. IoT devices equipped with AI algorithms can monitor machine performance, detect potential failures, and schedule maintenance activities before breakdowns occur. This proactive approach minimizes downtime and reduces maintenance costs.

Benefits of artificial intelligence in Internet of Things

The integration of artificial intelligence in Internet of Things brings forth a multitude of benefits, revolutionizing how we interact with technology and the world around us. Let’s delve into the advantages that arise from incorporating AI in IoT systems.

Improved data analysis and decision-making using artificial intelligence in IoT

One of the significant benefits of AI in IoT is its ability to analyze vast amounts of data and extract meaningful insights. With AI algorithms, IoT devices can process and interpret data in real-time, enabling accurate decision-making and actionable intelligence. Here are some key advantages:

Enhanced predictive analytics

AI-powered IoT devices can predict future outcomes and behaviors based on historical data patterns. By leveraging machine learning and predictive modeling, IoT systems can anticipate maintenance needs, optimize resource allocation, and predict customer preferences. This proactive approach enables organizations to make informed decisions, improve operational efficiency, and deliver better customer experiences.

Real-time monitoring and alerting

AI algorithms enable IoT devices to monitor critical parameters and trigger alerts in real-time. For instance, in a smart home security system, AI-powered cameras can detect unusual activities or intrusions and immediately notify homeowners or security personnel. This real-time monitoring enhances security and enables swift response to potential threats.

Contextual decision-making

AI in IoT enables devices to make context-aware decisions based on a deep understanding of the environment. For example, in smart city applications, AI-powered traffic management systems can analyze real-time traffic data, weather conditions, and historical patterns to optimize traffic flow and reduce congestion. This leads to improved transportation efficiency and reduced travel time for commuters.

Enhanced automation and efficiency through the integration of AI

AI empowers IoT devices with intelligent automation, optimizing processes and improving overall efficiency. Here’s how AI enhances automation in IoT systems:

Smart energy management

AI-powered IoT devices help optimize energy consumption by intelligently managing power usage. Smart thermostats, for instance, can learn user preferences, adjust temperature settings automatically, and optimize energy efficiency. By integrating AI algorithms, IoT systems can dynamically adapt energy consumption patterns to minimize waste and reduce costs.

Autonomous operations

AI-driven IoT devices can operate autonomously, reducing the need for manual intervention. For example, in industrial settings, AI-enabled robots can perform complex tasks, adapt to changing conditions, and collaborate with humans seamlessly. This automation improves productivity, reduces human errors, and enhances overall operational efficiency.

Streamlined processes

AI in IoT streamlines business processes by automating routine tasks and optimizing workflows. For instance, AI-powered inventory management systems can analyze demand patterns, predict stock requirements, and automatically place orders for replenishment. This reduces inventory holding costs, ensures timely availability of products, and improves supply chain efficiency.

Predictive maintenance and fault detection through artificial intelligence in IoT

AI enhances the capabilities of IoT devices in predictive maintenance and fault detection, resulting in cost savings and improved reliability. The advantages include:

Proactive maintenance

AI algorithms can analyze data from IoT sensors to identify potential equipment failures before they occur. By detecting early warning signs, such as unusual vibrations or temperature variations, IoT systems can schedule maintenance activities proactively. This predictive maintenance approach minimizes downtime, extends equipment lifespan, and reduces maintenance costs.

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Anomaly detection

AI-powered IoT devices excel at detecting anomalies within data streams. By establishing baseline patterns, AI algorithms can identify deviations that indicate potential faults or abnormalities. This early anomaly detection enables timely interventions, preventing costly breakdowns and ensuring continuous operations.

Condition monitoring

AI-driven IoT systems can monitor the condition of assets and equipment in real-time. By collecting and analyzing data from various sensors, IoT devices can assess the health and performance of machinery. For example, in manufacturing environments, AI-powered IoT sensors can monitor factors such as temperature, vibration, and energy consumption to detect signs of equipment degradation or impending failures. This real-time condition monitoring enables timely maintenance and minimizes unplanned downtime.

Personalization and smart UX enabled by artificial intelligence in IoT

Artificial intelligence in IoT enables personalized and intuitive user experiences, enhancing the way we interact with connected devices. The benefits include:

Customized recommendations

AI algorithms can analyze user behavior, preferences, and historical data to deliver personalized recommendations and tailored experiences. For instance, AI-powered IoT platforms can suggest personalized content, products, or services based on individual preferences, leading to a more engaging and satisfying user experience.

Voice and gesture recognition

AI-powered IoT devices can understand and respond to natural language commands and gestures. Voice assistants, such as Amazon Alexa or Google Assistant, utilize AI algorithms to interpret speech and perform tasks like playing music, setting reminders, or controlling smart home devices. Gesture recognition technologies, enabled by AI, allow users to interact with IoT devices through intuitive gestures, enhancing user convenience and accessibility.

Contextual adaptation

AI in IoT enables devices to adapt their behavior based on the context and user preferences. For example, smart lighting systems equipped with AI algorithms can automatically adjust lighting levels and color temperatures based on the time of day, occupancy, or user preferences. This contextual adaptation creates a comfortable and personalized environment for users.

Incorporating artificial intelligence in Internet of Things brings a multitude of benefits, including improved data analysis, enhanced automation, predictive maintenance, and personalized user experiences. These advantages have a transformative impact across various industries and domains. In the subsequent sections of this article, we will explore the challenges and limitations associated with artificial intelligence in IoT, as well as the key technologies and techniques driving this convergence.

Challenges and limitations of artificial intelligence in Internet of Things

While the integration of artificial intelligence in Internet of Things offers numerous advantages, it also presents certain challenges and limitations. It is important to understand and address these issues to ensure the successful deployment and utilization of AI in IoT systems. Let’s explore some of the key challenges:

Security and privacy concerns in artificial intelligence-driven IoT systems

The increased connectivity and data exchange in AI-powered IoT devices raise security and privacy concerns. Here are the main challenges:

Data privacy

AI algorithms require access to vast amounts of data to learn and make intelligent decisions. However, ensuring the privacy and protection of sensitive user data becomes crucial. Organizations must implement robust data encryption, secure data transmission protocols, and stringent access control mechanisms to safeguard user information and prevent unauthorized access.

Cybersecurity risks

The interconnected nature of IoT devices amplifies the potential attack surface for cybercriminals. AI-enabled IoT systems can become targets for malicious activities, such as data breaches, unauthorized access, or manipulation of critical operations. Implementing robust security measures, including intrusion detection systems, encryption, and regular security updates, is essential to mitigate these risks.

Ethical considerations

AI algorithms in IoT devices make decisions based on data analysis and learning. However, ensuring ethical use of AI becomes crucial to prevent biases, discrimination, or unethical decision-making. Organizations must adhere to ethical guidelines, fairness principles, and transparent AI practices to avoid unintended consequences and maintain trust among users.

Data management and scalability issues in artificial intelligence applications for IoT

The massive volume of data generated by IoT devices poses challenges in terms of data management and scalability. Consider the following challenges:

Data storage and processing

AI algorithms require substantial computational power and storage capacity to process and analyze IoT-generated data. As the number of connected devices increases, managing the sheer volume of data becomes a daunting task. Organizations must invest in scalable infrastructure and efficient data storage solutions to handle the ever-growing data streams.

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Bandwidth and network limitations

Transferring large volumes of IoT data to the cloud for AI processing can strain network bandwidth and lead to latency issues. This becomes particularly challenging in scenarios where real-time decision-making is required. Edge computing, where AI computations are performed closer to the data source, can help alleviate bandwidth constraints and reduce latency.

Integration with legacy systems

Integrating AI capabilities into existing IoT systems or legacy infrastructure can be complex. Legacy systems may lack the necessary compatibility or processing power to handle AI algorithms effectively. Organizations must carefully plan and execute integration strategies, ensuring seamless interoperability between AI-driven IoT systems and legacy infrastructure.

Ethical considerations and human-machine interaction in artificial intelligence in IoT

The advancements in AI technology raise ethical considerations and highlight the importance of human-machine interaction. Consider the following challenges:

Transparency and explainability

AI algorithms can be complex and difficult to interpret. Ensuring transparency and explainability of AI-driven decisions in IoT systems is crucial for user trust and accountability. Organizations must strive to develop AI models that provide clear explanations for their decisions, especially in critical scenarios like healthcare or autonomous vehicles.

Human-machine collaboration

As AI becomes more integrated into IoT systems, striking the right balance between human control and AI autonomy becomes essential. Organizations must design interfaces and interactions that facilitate effective collaboration between humans and AI-powered IoT devices. This involves understanding user needs, preferences, and the ability to override or intervene when necessary.

Job displacement and workforce adaptation

The integration of AI in IoT may lead to concerns about job displacement and changes in the workforce landscape. While AI can automate routine tasks, it can also create new opportunities and augment human capabilities. However, organizations must proactively address the potential impact on the workforce. This involves reskilling and upskilling employees to adapt to new roles that leverage the capabilities of AI in IoT, fostering a harmonious transition between human workers and AI-driven systems.

Addressing these challenges and limitations requires a holistic approach that encompasses robust security measures, scalable infrastructure, ethical considerations, and effective human-machine interaction. By doing so, we can unlock the full potential of artificial intelligence in Internet of Things and ensure its responsible and beneficial integration into our lives.

In the next section, we will explore the key technologies and techniques that drive the fusion of artificial intelligence and the Internet of Things. Understanding these advancements will provide insights into the underlying foundations of AI in IoT systems and its transformative potential.

Key technologies and techniques in artificial intelligence for Internet of Things

Artificial intelligence plays a vital role in enabling the capabilities of the Internet of Things. Let’s explore the key technologies and techniques that drive the fusion of AI and IoT, empowering intelligent and autonomous systems.

ML algorithms for analyzing IoT data using artificial intelligence

Machine learning forms the foundation of AI in IoT, allowing devices to learn patterns, make predictions, and adapt to changing circumstances.

Here are some important machine learning techniques used in IoT:

Supervised learning

Supervised learning involves training machine learning models with labeled datasets. In IoT applications, this technique can be used for tasks such as anomaly detection, predictive maintenance, or classification based on sensor data. Supervised learning algorithms, like decision trees, support vector machines, or neural networks, enable IoT devices to learn from historical data and make accurate predictions.

Unsupervised learning

Unsupervised learning involves training machine learning models with unlabeled datasets. In IoT, unsupervised learning algorithms are valuable for tasks such as clustering similar devices, identifying patterns in data, or detecting anomalies without prior knowledge of expected outcomes. Techniques like k-means clustering or hierarchical clustering are commonly used to uncover hidden structures and relationships in IoT data.

Reinforcement learning

Reinforcement learning enables IoT devices to learn through interaction with their environment. In this approach, devices receive feedback in the form of rewards or penalties based on their actions. Over time, through trial and error, the devices learn to make decisions that maximize rewards. Reinforcement learning is particularly useful in autonomous IoT systems, such as robotics or smart grid optimization.

Deep learning and neural networks in AI-driven IoT applications

Deep learning, a subset of machine learning, focuses on training neural networks with multiple layers to learn complex patterns and representations. Deep learning, in combination with IoT, unlocks various possibilities. Here are key aspects:

Convolutional neural networks (CNNs)

CNNs excel at processing and analyzing image and video data. In IoT applications, CNNs can be used for tasks like object recognition, facial recognition, or video surveillance. These networks learn hierarchical representations of visual data, enabling IoT devices to extract valuable information from images or videos captured by sensors or cameras.

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Recurrent Neural Networks (RNNs)

RNNs are suitable for processing sequential data, such as time-series sensor data. In IoT, RNNs can be employed for tasks like predicting future sensor readings, detecting anomalies in time-series data, or natural language processing for IoT devices. By capturing dependencies and temporal relationships in data, RNNs enable IoT devices to understand and make predictions based on sequential information.

Generative Adversarial Networks (GANs)

GANs consist of two neural networks: a generator network and a discriminator network. GANs can be used in IoT to generate synthetic data or augment existing datasets. For example, GANs can create realistic sensor data to expand training datasets or simulate diverse scenarios for testing IoT systems.

NLP for enabling IoT devices with AI

Natural language processing (NLP) allows IoT devices to understand and process human language, enabling seamless interaction and communication. Here are key NLP techniques used in AI-driven IoT applications:

Speech recognition

NLP-based speech recognition enables IoT devices to convert spoken language into text. This technology allows users to interact with IoT devices using voice commands, facilitating hands-free and intuitive control over connected systems.

Natural language understanding

NLP techniques enable IoT devices to comprehend and interpret the meaning behind human language. By extracting relevant information, entities, and intent from textual data, IoT devices can understand user queries, commands, or requests more accurately. Natural Language Understanding (NLU) techniques, such as named entity recognition, sentiment analysis, or language parsing, empower IoT devices to extract valuable insights from textual data.

Language generation

Language generation techniques allow IoT devices to generate human-like responses or output. This capability enables devices to provide informative and contextual responses to user queries or engage in natural conversations. By leveraging techniques like text generation models or language models, IoT devices can enhance user experiences and create more engaging interactions.

Edge computing and AI at the edge for IoT

Edge computing brings AI capabilities closer to the data source, reducing latency, improving responsiveness, and enhancing privacy. Here are key aspects of AI at the edge:

Local data processing

By performing AI computations locally on IoT devices or at edge computing nodes, data processing and analysis can occur in real-time without relying heavily on cloud infrastructure. This reduces the need for constant data transfer, lowers latency, and enables faster decision-making in time-sensitive applications.

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Privacy and security

Edge computing allows sensitive data to remain local, minimizing the risks associated with transmitting data to the cloud. AI algorithms deployed at the edge can process and analyze data on-site, reducing privacy concerns and enhancing data security. This is particularly crucial in scenarios where data confidentiality is paramount.

Bandwidth optimization

AI at the edge helps alleviate bandwidth constraints by reducing the amount of data that needs to be transmitted to the cloud. By performing local data processing and only transmitting relevant insights or summaries, edge computing optimizes network bandwidth usage and reduces associated costs.

The integration of these technologies and techniques drives the convergence of artificial intelligence and the Internet of Things, enabling intelligent decision-making, real-time insights, and seamless human-machine interactions. In the subsequent section, we will explore future trends and opportunities that lie ahead in the realm of artificial intelligence in Internet of Things

Future trends in artificial intelligence for Internet of Things

The fusion of artificial intelligence and the Internet of Things is continuously evolving, paving the way for exciting future trends and opportunities. Let’s explore some of the key areas that hold immense potential in the realm of AI for IoT.

Edge AI and the decentralized IoT architecture

Edge AI, which brings AI capabilities to the edge of the network, is poised to play a crucial role in the future of IoT. By processing data locally on edge devices, AI algorithms can deliver real-time insights and intelligent decision-making without relying heavily on cloud infrastructure. This enables faster response times, reduced latency, and enhanced privacy. The decentralized IoT architecture, powered by edge AI, will foster greater autonomy and intelligence at the network edge, enabling more efficient and intelligent IoT systems.

Integration of AI and blockchain in IoT systems

The integration of AI and blockchain technology holds immense potential for IoT applications. Blockchain, with its decentralized and immutable nature, can address key challenges in IoT, such as data security, privacy, and trust. Combining AI with blockchain can enable secure and trustworthy data exchange, facilitate autonomous decision-making in distributed IoT networks, and ensure data integrity and transparency. This convergence opens up new avenues for decentralized AI-driven IoT systems, particularly in areas like supply chain management, smart contracts, and secure data sharing.

AI-driven autonomous IoT systems

The future of AI in IoT lies in the development of autonomous systems that can make intelligent decisions and operate independently. AI-driven autonomous IoT systems can leverage advanced machine learning algorithms, reinforcement learning techniques, and sensor fusion to perceive their environment, learn from interactions, and make informed decisions in real-time. This paves the way for self-optimizing and self-adaptive IoT networks, where devices can dynamically adjust their behavior, optimize resource allocation, and collaborate intelligently without human intervention. Autonomous IoT systems have transformative potential in areas like smart cities, autonomous vehicles, and industrial automation.

Potential impact of 5G on AI-powered IoT

The advent of 5G technology is set to revolutionize the landscape of AI-powered IoT systems. With its ultra-low latency, high-speed connectivity, and massive device capacity, 5G networks will unlock new opportunities for AI in IoT. The high bandwidth and low latency of 5G will enable real-time data processing, facilitate seamless communication between devices, and support the proliferation of AI-driven applications. This will fuel advancements in areas like augmented reality, smart infrastructure, remote healthcare, and connected autonomous vehicles, transforming the way we interact with IoT devices and opening doors to new use cases.

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The future of artificial intelligence in Internet of Things holds immense promise. By leveraging edge AI, integrating blockchain, developing autonomous systems, and harnessing the power of 5G, we can unlock new frontiers of intelligence, connectivity, and innovation. As we embrace these future trends, it is crucial to continue addressing challenges, ensuring ethical AI practices, and maintaining a focus on human-centric design to harness the full potential of AI in IoT.

In the concluding section, we will recap the significance of artificial intelligence in Internet of Things, summarize the benefits and challenges discussed, and offer final thoughts on the future of this transformative field.

Conclusion

Artificial intelligence has emerged as a powerful force in transforming the Internet of Things landscape. By integrating AI capabilities into IoT systems, we unlock a realm of possibilities, empowering devices to analyze data, make intelligent decisions, and deliver personalized experiences. Throughout this article, we have explored the intersection of AI and IoT, the benefits it brings, the challenges it presents, and the key technologies driving this fusion.

The significance of artificial intelligence in Internet of Things cannot be overstated. AI enables improved data analysis and decision-making, enhanced automation and efficiency, predictive maintenance, and personalized user experiences. It has the potential to revolutionize various industries, from healthcare and manufacturing to transportation and smart cities.

However, as with any transformative technology, AI in IoT comes with challenges and limitations. Security and privacy concerns, data management, scalability issues, and ethical considerations must be carefully addressed. By implementing robust security measures, scalable infrastructure, and transparent AI practices, we can ensure the responsible and beneficial integration of AI in IoT systems.

The strategic value of IoT development and data analytics

Looking ahead, the future of AI in IoT holds tremendous promise. Edge AI and the decentralized IoT architecture will drive greater autonomy and intelligence at the network edge. The integration of AI and blockchain will enhance data security, trust, and decentralized decision-making. AI-driven autonomous IoT systems and the advent of 5G networks will pave the way for self-optimizing, real-time intelligent IoT networks, enabling groundbreaking applications and use cases.

As we venture into this future, it is crucial to continue advancing AI technologies, fostering collaboration between industry stakeholders, and nurturing ethical AI practices. By doing so, we can harness the full potential of artificial intelligence in Internet of Things, transforming our lives, industries, and the world as we know it.

IoT device security has become an increasingly pressing issue in recent years, as more and more devices become connected to the internet. From smart home appliances to medical devices, IoT devices have revolutionized the way we live and work.

However, the convenience and benefits of these devices come with significant risks. The vulnerability of IoT devices to cyber-attacks and data breaches has made their security a top priority for individuals, organizations, and governments around the world. In this context, understanding the risks and implementing best practices for securing IoT devices has never been more critical.

What is IoT device security?

IoT device security refers to the measures put in place to protect devices connected to the internet from unauthorized access, theft, and damage. IoT devices are typically small, low-powered devices that are embedded in everyday objects and are used to collect, process, and transmit data. Examples of IoT devices include smart home appliances, wearables, medical devices, and industrial equipment.

IoT devices are vulnerable to cyber-attacks due to their inherent design limitations, such as limited computing resources, lack of security features, and reliance on internet connectivity. Therefore, IoT device security involves implementing security protocols and mechanisms to mitigate these vulnerabilities and ensure that the devices and the data they collect are safe and secure.

Importance of IoT device security

IoT devices are increasingly being used in various industries to automate processes, improve efficiency, and enhance the user experience. However, this also means that they are collecting and transmitting sensitive data, which, if compromised, can have severe consequences for individuals and organizations.

Ensuring IoT device security is critical because it protects against data breaches, theft, and cyber-attacks, which can lead to financial losses, reputational damage, and legal liabilities. Moreover, compromised IoT devices can be used to launch large-scale attacks on other devices or networks, creating a ripple effect that can cause significant damage.

Risks associated with IoT devices

IoT devices offer many benefits, but they also come with various risks. The following are some of the risks associated with IoT devices:

• Privacy concerns: IoT devices collect vast amounts of data, which can include personal information, such as user location and behavior. This data can be used for nefarious purposes if it falls into the wrong hands.
• Cyber-attacks: IoT devices can be attacked by hackers who exploit vulnerabilities in their software or firmware. These attacks can cause the devices to malfunction or steal sensitive data.
• Malware: Malware can be introduced into IoT devices, which can then spread to other devices on the same network, causing widespread damage.
• Physical damage: IoT devices can be physically damaged or stolen, which can lead to loss of data and functionality.

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Types of security risks in IoT devices

The following are some of the security risks associated with IoT devices:

• Weak authentication and authorization mechanisms: Many IoT devices use weak or default passwords, making them easy targets for cyber-attacks.
• Lack of encryption: Some IoT devices transmit data over the internet without encryption, leaving the data vulnerable to interception and theft.
• Vulnerable firmware: Some IoT devices use outdated or unpatched firmware, which can be exploited by hackers to gain access to the device.
• Insecure communication protocols: Some IoT devices use insecure communication protocols that can be intercepted by attackers to gain access to the device.

Examples of security breaches in IoT devices

There have been several examples of security breaches in IoT devices, including:

• Mirai botnet attack: In 2016, the Mirai botnet attack compromised thousands of IoT devices, including cameras and routers, and used them to launch a massive DDoS attack on DNS provider Dyn.
• Jeep Cherokee hack: In 2015, hackers remotely took control of a Jeep Cherokee through its internet-connected entertainment system, demonstrating the vulnerabilities of IoT devices in vehicles.
• St. Jude Medical pacemaker hack: In 2017, security researchers found vulnerabilities in St. Jude Medical’s pacemakers that could be exploited to deliver lethal shocks to patients.

The consequences of security breaches in IoT devices

Security breaches in IoT devices can have severe consequences, including:

• Financial losses: A security breach can lead to financial losses for both individuals and organizations, including theft of money and intellectual property.
• Reputational damage: A security breach can damage the reputation of individuals or organizations, leading to a loss of trust and potential customers.
• Legal liabilities: A security breach can result in legal liabilities, including fines, lawsuits, and regulatory sanctions.
• Physical harm: A security breach in certain IoT devices, such as medical devices, can result in physical harm to individuals, including injury and death.

Factors affecting IoT device security

There are several factors that can affect the security of IoT devices, including the complexity of the devices, their interconnectivity with other devices and networks, resource constraints such as limited processing power and memory, and a lack of standards and guidelines for IoT device security.

The vulnerabilities of IoT devices

IoT devices are vulnerable to various security risks, including weak authentication and authorization mechanisms, lack of encryption, vulnerable firmware, insecure communication protocols, and physical damage or theft.

How can data science optimize performance in IoT ecosystems?

The role of IoT device manufacturers

Manufacturers of IoT devices play a crucial role in ensuring the security of their products. They need to design devices with security in mind, implement robust security protocols, provide regular firmware updates and patches to address vulnerabilities, and follow industry standards and best practices for IoT device security.

The impact of user behavior on IoT device security

Users of IoT devices also have a significant impact on the security of these devices. They need to take steps to ensure that their devices are secure, such as changing default passwords, keeping firmware up to date, avoiding insecure communication protocols, and protecting physical access to devices. Failure to take these precautions can result in compromised devices and data.

Best practices for securing IoT devices

Securing IoT devices requires a multifaceted approach. Here are some best practices for securing IoT devices:

• Choosing strong passwords and updating regularly: Users should select strong passwords that are difficult to guess or crack, and update them regularly to prevent unauthorized access to the device.
• Setting up multi-factor authentication: Multi-factor authentication provides an extra layer of security to IoT devices by requiring users to provide multiple forms of identification before gaining access to the device.
• Disabling unused features: Disabling unused features on IoT devices reduces the attack surface and minimizes the risk of exploitation.
• Updating IoT device firmware: Manufacturers regularly release firmware updates to fix vulnerabilities and improve the security of IoT devices. Users should ensure that their devices are running the latest firmware version.
• Avoiding public Wi-Fi networks: Public Wi-Fi networks are often insecure and can expose IoT devices to various security risks. Users should avoid using public Wi-Fi networks to access IoT devices.
• Keeping IoT devices physically secure: Physical access to IoT devices can also compromise their security. Users should keep their devices in a secure location and protect them from theft and tampering.

By following these best practices, users can help ensure the security of their IoT devices and protect against potential cyber-attacks or data breaches.Solutions for securing IoT devices

Securing IoT devices is a complex and ongoing process, and it requires a combination of technical and organizational measures. Here are some solutions for securing IoT devices:

Using IoT device security software

There are many security solutions available that can help protect IoT devices from cyber-attacks. These solutions include firewalls, antivirus software, intrusion detection systems, and security analytics tools.

Implementing network segmentation

Network segmentation involves dividing a network into smaller subnetworks, each with its security controls. This helps limit the spread of cyber-attacks across the network and reduces the risk of unauthorized access to IoT devices.

Conducting regular vulnerability assessments

Vulnerability assessments involve identifying and analyzing potential security risks to IoT devices. Regular assessments help identify new vulnerabilities and allow for prompt remediation before they are exploited.

The strategic value of IoT development and data analytics

Educating users about IoT device security

Education and training programs can help users understand the risks associated with IoT devices and the best practices for securing them. This includes topics such as password management, firmware updates, and how to identify and report potential security incidents.

By implementing these solutions, organizations and users can take proactive measures to secure IoT devices and protect against potential cyber threats.

“IoT security is a marathon, not a sprint”

Securing IoT devices is an ongoing process that requires continuous effort and investment. As the number of IoT devices continues to grow, so do the challenges and risks associated with securing them. However, by recognizing that IoT security is a marathon, not a sprint, stakeholders can take a proactive and long-term approach to securing these devices.

This includes implementing technical solutions, such as firewalls and intrusion detection systems, as well as educating users and adopting best practices for IoT security. By working together and committing to the ongoing security of IoT devices, we can help ensure that they are safe and secure for years to come.

With the rise of edge computing companies, businesses are increasingly turning to innovative partners to help them stay ahead of the curve.

The realm of edge computing has witnessed a substantial surge in recent years, propelled by the proliferation of remote work, the Internet of Things (IoT), and augmented/virtual reality (AR/VR) technologies, which have necessitated connectivity at the network’s periphery and novel applications.

Consequently, edge computing has been deployed extensively across a range of verticals and for a variety of use cases. This development is not surprising, given that the International Data Corporation (IDC) has forecasted a robust expansion of the global enterprise and service provider expenditure on hardware, software, and services for edge solutions until 2025, with spending expected to surpass $274 billion.

Established networking and telecom service providers have joined the fray, offering wireless and connectivity solutions to facilitate the operation and protection of applications at the network’s edge. Meanwhile, the advent of 5G, IoT, and secure access service edge (SASE) has spawned a new wave of startups focused on application development, connectivity, automation, and data collection, with a particular emphasis on managing the surge of data influx from the edge.

The importance of edge computing startups

In the dynamic and rapidly evolving world of technology, it is often the startups that have the power to disrupt the status quo and propel the industry forward. This is particularly true in the field of edge computing, where the need for innovative solutions has never been more pressing.

While established edge computing companies undoubtedly have a valuable role to play in driving the growth of this burgeoning sector, it is the startups that are uniquely positioned to offer fresh perspectives, embrace new ideas, and push the boundaries of what is possible. With their ability to take risks, experiment with emerging technologies, and pivot quickly in response to changing market demands, edge computing startups are poised to play a critical role in shaping the future of the industry.

Top 10 edge computing companies to watch in 2023

Let’s get to know the top 10 edge computing companies to watch in 2023! As the field of edge computing continues to expand and evolve, these companies are at the forefront of innovation, delivering cutting-edge solutions to some of the most pressing challenges facing businesses today.

Adaptiv Networks

Adaptiv Networks, a smart business connectivity provider, has introduced its cloud-managed SD-WAN service, known as Adaptiv Enterprise Connect, to the market. This service enables enterprises to focus on new applications and users at the network edge while providing an exceptional user experience for all business cloud services and private corporate applications.

Founded in 2002, the Quebec-based company, which is privately held, is setting its sights on a few key verticals, including hospitality, healthcare, and retail. To promote its network as a service and co-managed cloud solutions, Adaptiv is relying on its channel partners.

Celona

Celona is one of the most intriguing edge computing companies to keep an eye on, as it offers a platform that allows enterprises to create private 5G/4G LTE networks, catering to a previously unaddressed gap in the connectivity market. Through its strategic partnership with Aruba Networks, the Cupertino-based firm is further expanding its presence in the market for cellular products.

Additionally, Celona’s 5G LAN Device Certification Program and solution provider partner program, Fanatics, demonstrate the company’s ongoing efforts to streamline the deployment and adoption of private LTE/5G networks and provide a formal means for resellers, MSPs, and systems integrators to engage with Celona and register private 5G and LTE deals.

The strategic value of IoT development and data analytics

Sierra Wireless

Sierra Wireless, a wireless communications equipment designer and service provider, has been honing its focus on IoT software and managed services following its acquisition of M2M Group, a cluster of companies dedicated to IoT connectivity, in 2020. The Canadian telecom equipment manufacturer specializes in developing diminutive embedded wireless modules with 5G capabilities, tailored specifically for IoT applications.

In September, Semtech, an IoT provider based in Camarillo, California, unveiled its intention to acquire Sierra for $1.2 billion to unify their cloud portfolio, encompassing advanced security, provisioning, device management, and geolocation capabilities for optimized IoT applications. This acquisition will bolster Semtech’s offerings in the field of edge computing companies.

Versa Networks

Versa Networks, an SD-WAN-turned-SASE specialist, is empowering enterprises to overcome security and connectivity challenges, particularly as next-generation connectivity technologies like 5G become more prevalent. The company asserts that secure SD-WAN can streamline management across an expanding mobile footprint and that traditional wired internet services must coexist alongside 5G networks.

Versa, headquartered in Santa Clara, California, is a privately held standalone SASE company, and it recently disclosed a $120 million private equity funding round in October. As such, it is among the most prominent edge computing companies, offering solutions for managing and securing complex networks.

Aruba Networks

Aruba Networks, an HPE company, is a significant edge networking player for hardware, software, and services, making it one of the leading edge computing companies. Aruba’s flagship Aruba Edge Services Platform (ESP) enables companies to accelerate their digital transformation by automating network management, providing edge-to-cloud security, and offering predictive AI-powered insights.

In addition to its 500/600 series Wi-Fi 6/6E access points, Aruba offers a comprehensive portfolio of CX switches, and its solutions can be managed primarily through its cloud-first, AI-powered management platform, Aruba Central, which is a major selling point for customers. With the support of its partners, Aruba’s offerings are employed in a range of use cases, including those at the network edge and in areas that are acquiring connectivity for the first time. Aruba’s presence in the market is a testament to its ability to offer cutting-edge edge networking solutions.

Aarna Networks

Aarna Networks, established in 2018, is striving to simplify edge orchestration for enterprises by offering private 5G and enterprise edge computing application automation software. Aarna Edge Services, the company’s SaaS platform, delivers zero-touch orchestration as a service for edge infrastructure and public clouds, enabling users to manage compute, storage, and network operations from the edge to the cloud.

The San Jose-based startup’s principal go-to-market strategy is via its channel partners. Aarna has amassed a total of $3.5 million in funding, with its most recent funding coming from a seed round in December 2021. As a one of the most noteworthy edge computing companies, Aarna Networks has the potential to revolutionize the way enterprises manage their edge infrastructure and streamline their operations.

IIoT and edge computing are gaining traction in many industries

Cato Networks

Cato Networks, a cloud networking provider that specializes in SD-WAN and SASE, is a significant player in the edge computing market. The company has integrated its SD-WAN and cloud-native security service edge technologies to offer global cloud services that enforce access policies, secure against security threats, and prevent sensitive data loss.

Cato Networks operates exclusively through its channel partners, and it recently hired Frank Rauch, a leading figure in the channel industry, as its new global channel chief. This move is aimed at expanding the company’s business worldwide and helping its partners profit from the transition to cloud-native networking and security, as stated by the Tel Aviv-based firm. With its blend of SD-WAN and SASE capabilities, Cato Networks is one of the most innovative edge computing companies offering cloud and edge computing solutions in the market.

Cradlepoint

Cradlepoint, an expert in wireless edge networking based in Boise, Idaho, was acquired by Ericsson in 2020, bringing its expertise to the telecom equipment giant. Cradlepoint is revolutionizing the potential of LTE and next-gen cellular technologies, such as 5G, with its NetCloud Exchange, an extension of its cloud-managed Network-as-a-Service offering. The Cradlepoint NetCloud Manager platform enables IT administrators to manage branch offices, vehicles, and IoT environments, making it a top contender among edge computing companies.

During the pandemic, Cradlepoint has been collaborating with its customers, with the help of its partners, to explore the possibilities of cellular and wireless as their primary connection point, where it was once only considered as a backup or failover option for businesses. This has been particularly beneficial for use cases that require temporary connectivity that can be quickly scaled up or down. Cradlepoint’s commitment to innovation and its ability to deliver cutting-edge wireless solutions has placed it among the most prominent edge computing companies.

Macrometa

Founded in 2017, privately held Macrometa offers its Global Data Network and edge computing platform to help developers build real-time applications and APIs.

The San Mateo, Calif.-based company in 2022 revealed a handful of partnerships, including a partnership with DevCycle in September to launch Edge Flags, what the two companies are calling the world’s first ultra-low-latency global feature flags solution for developers. In November, it

entered into a partnership with content delivery network services provider Akamai Technologies that the company said would include product integrations.

Azion

Azion is one of the most interesting edge computing companies. It provides several solutions to facilitate the deployment of edge computing. Their Edge Platform is one such product that empowers developers to design and release serverless applications. This platform also allows for the creation of zero-trust security architectures and IoT deployments, further reinforcing Azion’s commitment to providing comprehensive edge computing solutions.

IoT protocols 101: The essential guide to choosing the right option

Current edge computing market leaders

While there are numerous edge computing companies with great potential, a handful of industry leaders have emerged as the go-to providers for cutting-edge edge computing solutions. These companies have demonstrated a deep understanding of the needs of the modern enterprise and have developed innovative solutions that push the boundaries of what is possible with edge computing.

We will review some of the current market leaders in edge computing and examine the unique strengths that have helped them rise to the top of the industry.

• Amazon Web Services (AWS): As a major player in the cloud computing market, AWS is well positioned to offer a variety of edge computing solutions, including AWS IoT Greengrass, which extends AWS to the edge of the network.
• Microsoft Azure: As a leading provider of cloud computing and artificial intelligence services, Azure is also a top contender in the edge computing market. Its Azure IoT Edge platform enables users to run AI, Azure services, and custom logic on devices.
• Google Cloud Platform (GCP): With its strong focus on data analytics and machine learning, GCP is well suited to deliver edge computing solutions that allow users to analyze and act on data in real time.
• Dell Technologies: A major player in the IT industry, Dell Technologies offers a broad range of edge computing solutions, including its VMware Pulse IoT Center, which enables users to manage, monitor, and secure their IoT devices.
• Hewlett Packard Enterprise (HPE): HPE has a strong portfolio of edge computing products and services, including its HPE Edgeline Converged Edge Systems, which enable users to process data and run applications at the edge of the network.

Bottom line

The edge computing market is growing at an unprecedented rate, and for good reason. The ability to process and analyze data closer to the source has the potential to unlock new levels of efficiency, productivity, and innovation across a wide range of industries.

As we’ve seen, there are a number of edge computing companies that are leading the charge, each with their own unique strengths and capabilities. From established players like AWS, Microsoft, and Dell Technologies to up-and-comers like Macrometa and Aarna Networks, these edge computing companies are shaping the future of the industry and paving the way for a more connected, intelligent, and efficient world.

What is IIoT? Imagine a world where machines and devices can communicate with each other in real-time, exchanging data and insights to optimize industrial processes and improve efficiency. This is the world of the Industrial Internet of Things (IIoT).

IIoT is a transformative technology that is revolutionizing the way that industrial organizations operate, enabling them to gain a competitive advantage in today’s rapidly changing business landscape. From predictive maintenance to remote monitoring, IIoT systems provide a range of tools and services that enable organizations to optimize their operations and reduce costs.

What is IoT?

IoT, or the Internet of Things, refers to a network of physical objects, devices, vehicles, buildings, and other items that are embedded with sensors, software, and connectivity, enabling them to collect and exchange data with other devices and systems over the internet.

What is IIoT?

The Industrial Internet of Things is a term used to describe the integration of physical industrial devices with network connectivity and software applications. It involves the use of sensors, embedded systems, and other hardware devices that are connected to the internet to gather and exchange data. The data collected from these devices is analyzed in real-time to provide insights that help organizations optimize their operations, increase productivity, and reduce costs.

The IIoT represents a significant shift in the way that businesses operate, as it enables a higher level of automation, control, and visibility over industrial processes. The integration of IoT devices with traditional industrial systems has also led to the development of new business models, such as predictive maintenance and remote monitoring.

Understanding IIoT architecture

The architecture of the IIoT refers to the design and organization of the various hardware, software, and networking components that make up an IIoT system. A typical IIoT architecture consists of three layers: the edge layer, the platform layer, and the enterprise layer.

The edge layer is where the sensors and other IoT devices are located. These devices collect data and transmit it to the platform layer for processing. The platform layer is responsible for managing the data and providing services such as data storage, analytics, and visualization. The enterprise layer is where the insights generated by the IIoT system are used to make decisions and drive business outcomes.

Building trust in IoT ecosystems: A privacy-enhancing approach to cybersecurity

An IIoT system also requires a range of communication protocols to enable the different components to interact with each other. These protocols include Wi-Fi, Bluetooth, ZigBee, and other wireless standards, as well as wired standards such as Ethernet.

The architecture of an IIoT system can vary depending on the specific requirements of the application. However, regardless of the specific implementation, an effective IIoT architecture must be scalable, secure, and reliable, as it is often used to support mission-critical industrial processes.

Types of IIoT

There are several types of IIoT systems, each with its own unique characteristics and applications. Some of the most common types of IIoT include:

• Condition monitoring systems (CMS): These IIoT systems are designed to monitor the condition of equipment and machinery in real-time, using sensors and other IoT devices to collect data on factors such as temperature, vibration, and fluid levels. This data can be used to predict when maintenance is required, reducing downtime and increasing efficiency.
• Predictive maintenance systems: Similar to CMS, predictive maintenance IIoT systems use data from sensors to identify potential issues with the equipment before they occur. By analyzing patterns in data, these systems can predict when a component is likely to fail and schedule maintenance accordingly.
• Remote monitoring systems: IIoT systems can be used to monitor industrial equipment and processes remotely, allowing operators to keep an eye on their operations from anywhere in the world. This can be particularly useful for monitoring equipment in remote locations or for monitoring equipment that is difficult or dangerous to access.
• Process optimization systems: IIoT systems can also be used to optimize industrial processes by gathering data on factors such as temperature, pressure, and flow rate and using this data to adjust process parameters in real-time. This can improve efficiency, reduce waste, and improve product quality.
• Asset tracking systems: IIoT systems can be used to track the location of industrial assets, such as vehicles, containers, and equipment, using GPS and other tracking technologies. This can help to improve logistics, reduce the risk of theft, and improve asset utilization.
• Energy management systems: IIoT systems can be used to monitor and optimize energy usage in industrial facilities by gathering data on factors such as energy consumption, temperature, and lighting levels. This can help to reduce energy costs, improve sustainability, and comply with environmental regulations.

IIoT systems offer a wide range of benefits to industrial organizations, including increased efficiency, improved safety, and reduced costs. By leveraging the power of IoT technologies, organizations can transform their operations and gain a competitive advantage in today’s rapidly changing business landscape.

The benefits of IIoT

The Industrial Internet of Things (IIoT) is transforming the way that industrial organizations operate, offering a wide range of benefits and opportunities for businesses of all sizes. Some of the key benefits of IIoT include:

Improved operational efficiency and productivity

IIoT systems provide real-time data and analytics that can be used to optimize industrial processes, reduce waste, and improve efficiency. By using IIoT systems to monitor and control industrial processes, organizations can reduce the risk of errors and downtime, leading to improved productivity and profitability.

Increased safety and security

IIoT systems can be used to monitor industrial processes and equipment in real-time, detecting potential safety hazards and alerting operators to take action. Additionally, IIoT systems can be used to secure industrial facilities and equipment, preventing unauthorized access and reducing the risk of theft and vandalism.

Cost savings through predictive maintenance and reduced downtime

IIoT systems can be used to predict when maintenance is required, reducing downtime and increasing the lifespan of industrial equipment. By using IIoT systems to optimize maintenance schedules and reduce downtime, organizations can save significant costs over time.

Enhanced CX through personalized products and services

IIoT systems can be used to collect data on customer preferences and behavior, allowing organizations to offer personalized products and services that meet the unique needs of their customers. This can improve customer satisfaction and loyalty, leading to increased revenue and growth.

The benefits of IIoT are significant and wide-ranging. By leveraging the power of IoT technologies, industrial organizations can improve their operations, reduce costs, and gain a competitive advantage in today’s rapidly changing business landscape.

How can data science optimize performance in IoT ecosystems?

Challenges and risks of IIoT

While the Industrial Internet of Things (IIoT) offers many benefits, it also poses a number of challenges and risks that industrial organizations must be aware of. Some of the key challenges and risks of IIoT include:

Cybersecurity risks and concerns

As IIoT systems become more integrated into industrial processes, they also become more vulnerable to cyber attacks. Malicious actors can target IIoT systems to disrupt operations, steal sensitive data, or even cause physical harm. It is, therefore, essential for industrial organizations to implement robust cybersecurity measures, such as network segmentation, encryption, and access controls, to protect their IIoT systems.

Privacy and data protection issues

IIoT systems collect large amounts of data on industrial processes, equipment, and personnel. This data can include sensitive information such as personal data, trade secrets, and intellectual property. Industrial organizations must therefore ensure that they comply with data protection and privacy regulations, such as GDPR and CCPA, to protect this data from unauthorized access and misuse.

Lack of standards and interoperability

One of the challenges of IIoT is the lack of standardized protocols and interfaces. This can make it difficult for different IIoT devices and systems to communicate and work together. Industrial organizations must therefore ensure that their IIoT systems are designed with interoperability in mind and that they are able to integrate with other systems and devices as needed.

Need for specializedsSkills and expertise

The implementation and management of IIoT systems require specialized skills and expertise, including knowledge of networking, cybersecurity, data analytics, and industrial processes. Many organizations may not have these skills in-house and may need to partner with third-party vendors or consultants to implement and manage their IIoT systems.

The challenges and risks of IIoT require careful consideration and planning by industrial organizations. By implementing appropriate measures to address these challenges, organizations can minimize risks and reap the benefits of IIoT systems.

How industrial IoT market will shape the future?

The Industrial Internet of Things (IIoT) market is expected to continue to grow rapidly in the coming years, driven by advances in technology, increasing demand for connected devices, and the need for industrial organizations to improve efficiency and reduce costs. The IIoT market is expected to have a significant impact on the future of industrial operations and the global economy as a whole.

One of the key ways in which the IIoT market is expected to shape the future is through its impact on industrial efficiency and productivity. By providing real-time data and insights on industrial processes, IIoT systems enable organizations to optimize their operations, reduce downtime, and increase productivity. This can lead to significant cost savings and a competitive advantage for industrial organizations.

The IIoT market is also expected to have a major impact on safety and security in industrial environments. IIoT systems can be used to monitor and control industrial processes in real-time, detecting potential safety hazards and alerting operators to take action. Additionally, IIoT systems can be used to secure industrial facilities and equipment, reducing the risk of theft and vandalism.

Another way in which the IIoT market is expected to shape the future is through the development of new business models and revenue streams. IIoT systems enable organizations to collect and analyze vast amounts of data on industrial processes and customer behavior, providing insights that can be used to develop new products and services. This can lead to increased revenue and growth opportunities for industrial organizations.

Emerging technologies and advancements in IIoT

The IIoT market is constantly evolving, with new technologies and advancements emerging on a regular basis. Some of the key emerging technologies and advancements in IIoT include:

• 5G networks: The deployment of 5G networks is expected to revolutionize the IIoT market, enabling faster and more reliable data transmission and communication between IIoT devices.
• Artificial intelligence (AI): The integration of AI technologies with IIoT systems are expected to enable new levels of automation and decision-making, leading to further improvements in efficiency and productivity.
• Edge computing: Edge computing involves processing data at the edge of the network, closer to where the data is generated. This can help to reduce latency and improve real-time decision-making in IIoT systems.
• Digital twins: Digital twins are virtual representations of physical objects or systems. In the context of IIoT, digital twins can be used to simulate industrial processes and predict outcomes, enabling organizations to optimize their operations and reduce costs.

The emerging technologies and advancements in IIoT are expected to drive further growth and innovation in the market, enabling industrial organizations to achieve new levels of efficiency, productivity, and profitability.

The strategic value of IoT development and data analytics

Best IIoT solutions

There are many IIoT solutions available in the market, each with its own unique features and benefits. Some of the best IIoT solutions include:

Siemens MindSphere

Siemens MindSphere is a cloud-based IIoT platform that enables organizations to connect their machines, plants, and systems to the digital world. MindSphere provides a range of tools and services for data analytics, machine learning, and visualization, enabling organizations to optimize their operations and reduce costs. The platform is used in a variety of industries, including manufacturing, energy, and transportation.

Microsoft Azure IoT

Microsoft Azure IoT is a comprehensive IIoT platform that provides a range of tools and services for device management, data analytics, and visualization. The platform is built on Microsoft’s cloud infrastructure, enabling organizations to scale their IIoT systems as needed. Azure IoT is used in a variety of industries, including manufacturing, healthcare, and smart cities.

IBM Watson IoT

IBM Watson IoT is a cloud-based IIoT platform that provides a range of tools and services for device management, data analytics, and visualization. The platform is built on IBM’s cognitive computing technology, enabling organizations to analyze large amounts of data in real-time and make informed decisions. Watson IoT is used in a variety of industries, including manufacturing, energy, and transportation.

PTC ThingWorx

PTC ThingWorx is an IIoT platform that provides a range of tools and services for device management, data analytics, and visualization. The platform is built on PTC’s ThingModeler technology, enabling organizations to create digital twins of their industrial assets and optimize their operations. ThingWorx is used in a variety of industries, including manufacturing, energy, and transportation.

Amazon Web Services IoT

Amazon Web Services (AWS) IoT is a cloud-based IIoT platform that provides a range of tools and services for device management, data analytics, and visualization. The platform is built on AWS’s cloud infrastructure, enabling organizations to scale their IIoT systems as needed. AWS IoT is used in a variety of industries, including manufacturing, energy, and smart cities.

Overall, these IIoT solutions provide a range of tools and services that enable organizations to optimize their operations, reduce costs, and gain a competitive advantage in today’s rapidly changing business landscape. By leveraging the power of IoT technologies, industrial organizations can transform their operations and achieve new levels of efficiency and productivity.

Final words

Back to our original question: What is IIoT? IIoT systems provide real-time data and insights that enable organizations to optimize their operations, reduce costs, and gain a competitive advantage in today’s rapidly changing business landscape. From predictive maintenance to process optimization, IIoT offers a wide range of benefits that are driving its rapid adoption in industries such as manufacturing, energy, and transportation.

However, the challenges and risks of IIoT must also be considered, such as cybersecurity and data privacy concerns, lack of standards and interoperability, and the need for specialized skills and expertise. By addressing these challenges and leveraging the power of emerging technologies, industrial organizations can harness the full potential of IIoT and achieve new levels of efficiency, productivity, and profitability.

IoT development involves the use of tools and technologies to design, build, test, and deploy IoT devices and systems. In this article, we will explore the importance of IoT development and some of the challenges that come with it, along with the tools and technologies used in IoT development.

The Internet of Things (IoT) has rapidly become one of the most significant technology trends in recent years, with its potential to transform businesses, industries, and society. As IoT devices and systems become more prevalent, IoT development has become increasingly important to create and deploy these systems effectively.

The increasing importance of IoT in various industries

In recent years, the Internet of Things has emerged as a disruptive force in various industries, including manufacturing, healthcare, transportation, and retail. IoT technology enables the connection and exchange of data between devices, machines, and people, leading to new and innovative business models and opportunities.

The increasing importance of IoT lies in its ability to collect and analyze vast amounts of data, providing real-time insights and actionable intelligence to businesses. This data-driven approach has the potential to transform operations, improve customer experiences, and increase revenues across industries. According to Statista’s estimates, total consumer spending on smart home products and services worldwide will be $159 billion.

Advantages of IoT

IoT technology offers numerous advantages to businesses, including increased efficiency, productivity, and profitability. By connecting devices and machines, businesses can automate processes, reduce waste, and improve decision-making. Additionally, IoT data can be used to gain a deeper understanding of customer behavior, preferences, and needs, enabling businesses to tailor products and services to meet those needs. The ability to remotely monitor and control devices also increases safety and security, reducing the risk of accidents and theft.

Improving efficiency and productivity with IoT

IoT technology can significantly improve efficiency and productivity in industries such as manufacturing, logistics, and agriculture. By connecting machines and sensors, businesses can monitor production processes in real time, identify bottlenecks, and optimize workflows. IoT-enabled predictive maintenance also helps prevent equipment breakdowns, reducing downtime and maintenance costs. In addition, IoT sensors can be used to monitor and optimize resource usages, such as energy and water, leading to cost savings and sustainability benefits.

Reducing costs and increasing profitability with IoT

IoT technology can help businesses reduce costs and increase profitability by improving operational efficiency, reducing waste, and creating new revenue streams. By automating processes and collecting real-time data, businesses can optimize resource usage and reduce energy consumption, saving on costs.

IoT-enabled predictive maintenance also reduces the need for costly repairs and downtime. In addition, IoT data can be used to create new revenue streams, such as offering data analytics services to customers or using data to develop new products and services.

Enhancing safety and security with IoT

IoT technology can enhance safety and security in industries such as healthcare, transportation, and manufacturing. By connecting devices and sensors, businesses can monitor and control processes remotely, reducing the risk of accidents and ensuring compliance with safety regulations. IoT-enabled predictive maintenance also helps prevent equipment breakdowns that can pose safety risks. Additionally, IoT sensors can be used to monitor and detect potential security threats, such as unauthorized access or theft, enabling businesses to take proactive measures to prevent these incidents.

Building trust in IoT ecosystems: A privacy-enhancing approach to cybersecurity

Disadvantages of IoT

While the Internet of Things offers numerous benefits, such as increased efficiency and productivity, it also comes with its own set of disadvantages. In this article, we will explore some of the potential drawbacks of IoT, such as security vulnerabilities and privacy concerns.

Security risks associated with IoT devices

One of the most significant disadvantages of IoT technology is the potential security risks associated with connected devices. Since IoT devices are connected to the internet and can exchange data with other devices and systems, they can be vulnerable to cyber-attacks, data breaches, and hacking. IoT devices often lack the necessary security features, making them easy targets for cybercriminals. Additionally, IoT devices can be used to launch large-scale attacks, such as Distributed Denial of Service (DDoS) attacks, which can disrupt entire networks.

Privacy concerns with IoT data collection

Another significant disadvantage of IoT technology is the potential privacy concerns associated with the collection and use of IoT data. IoT devices can collect vast amounts of data about individuals, including their location, behavior, and personal information. This data can be used by businesses for various purposes, such as targeted advertising, personalized recommendations, and product development. However, the collection and use of this data can also raise privacy concerns, as individuals may not be aware of the data collected or how it is being used.

Interoperability and compatibility issues with IoT devices

IoT devices often use different communication protocols and standards, making it challenging to ensure interoperability and compatibility between devices from different manufacturers. This can lead to fragmentation and a lack of standardization, making it difficult for businesses to integrate and manage IoT devices. Additionally, the rapid pace of IoT development can result in older devices becoming obsolete quickly, leading to compatibility issues and potential security vulnerabilities.

Applications of IoT

The Internet of Things IoT is rapidly transforming various industries and sectors, from healthcare to agriculture to transportation. In this article, we will explore some of the most exciting and innovative applications of IoT, including smart homes, industrial automation, and healthcare monitoring.

• Smart homes: IoT devices can be used to automate and control various home appliances, such as thermostats, lighting, security systems, and entertainment systems, making homes more energy-efficient and convenient. For example, homeowners can use IoT-enabled thermostats to adjust the temperature in their homes remotely or use smart lighting to turn off lights automatically when no one is in the room.
• Industrial automation: IoT technology can be used to automate and optimize industrial processes, improving productivity and efficiency. For example, IoT-enabled sensors can monitor machine performance in real time, detecting issues before they lead to breakdowns or downtime. Additionally, IoT technology can be used to track inventory levels, monitor supply chains, and optimize logistics, reducing waste and costs.
• Healthcare monitoring: IoT devices can be used to monitor patients’ health remotely, providing doctors and caregivers with real-time data on vital signs and symptoms. For example, wearable IoT devices can track heart rate, blood pressure, and other metrics, alerting healthcare providers to potential issues. Additionally, IoT technology can be used to manage medication adherence, monitor chronic conditions, and track patient activity levels.
• Smart cities: IoT technology can be used to create smart cities, improving public services and enhancing sustainability. For example, IoT-enabled sensors can be used to monitor traffic flow, optimize public transportation, and manage energy usage in buildings. Additionally, IoT technology can be used to improve public safety, such as by detecting and responding to emergencies in real time.
• Retail analytics: IoT technology can be used to collect and analyze data on customer behavior, preferences, and buying patterns, improving marketing and sales strategies. For example, IoT-enabled sensors can track customer movements in stores, providing insights into how customers navigate and interact with products. Additionally, IoT technology can be used to personalize promotions and offers based on individual customer data.
• Environmental monitoring: IoT devices can be used to monitor and manage environmental conditions, such as air quality, water quality, and weather patterns. For example, IoT-enabled sensors can monitor air pollution levels, alerting authorities to potential health risks. Additionally, IoT technology can be used to manage irrigation systems, monitor soil conditions, and optimize crop yields in agriculture.

What is IoT data?

IoT data refers to the information collected by connected devices in the Internet of Things (IoT) ecosystem. IoT devices can collect a wide range of data, including sensor readings, user interactions, and environmental conditions. This data is typically transmitted over the internet and stored in cloud-based platforms or databases, where it can be processed and analyzed.

The importance of IoT data for insights and decision-making

IoT data plays a crucial role in providing businesses with real-time insights and actionable intelligence. By collecting and analyzing vast amounts of data, businesses can gain a deeper understanding of their operations, customers, and markets. IoT data can be used to optimize production processes, reduce costs, and improve customer experiences.

Additionally, IoT data can be used to predict and prevent equipment breakdowns, reducing downtime and maintenance costs. The ability to access and analyze IoT data in real time is a game-changer for businesses, enabling them to make data-driven decisions that can lead to significant improvements in efficiency, productivity, and profitability.

Understanding IoT data structures

IoT data comes in various formats and structures, depending on the type of device and data being collected. Understanding the different data structures is essential for businesses to effectively collect, store, and analyze IoT data. IoT data can be classified into three main types: structured, semi-structured, and unstructured.

Structured data refers to data that has a defined schema and is organized into tables, such as sensor readings. Semi-structured data refers to data that is organized but not as rigidly defined as structured data, such as JSON or XML files. Unstructured data refers to data that has no defined schema or organization, such as social media feeds or video streams. Businesses need to have a clear understanding of the data structures they are working with to ensure they are effectively managing and analyzing their IoT data.

The types of IoT data and their sources

IoT data can come from a variety of sources, including sensors, devices, and user interactions. There are three main types of IoT data:

• Descriptive data: This type of data provides information about the current state of a system or process, such as temperature readings or machine performance metrics.
• Diagnostic data: This type of data provides information about the causes of events or issues, such as sensor readings that indicate a machine is overheating.
• Predictive data: This type of data is used to predict future events or outcomes, such as machine failure or supply chain disruptions.

Here are some examples of sources of IoT data:

• Sensors and devices that collect data on temperature, humidity, pressure, and other environmental conditions
• User interactions with IoT-enabled devices, such as smart homes or wearables
• Machines and equipment that generate data on performance and usage
• Social media and online sources that provide data on consumer behavior and preferences

The challenges of managing and analyzing IoT data

While IoT data offers significant benefits, managing and analyzing this data can be challenging. Some of the main challenges include the following:

• Data volume: IoT devices can generate vast amounts of data, making it difficult to store, process, and analyze.
• Data variety: IoT data can come in many different formats and structures, making it challenging to manage and integrate with other data sources.
• Data velocity: IoT data is often generated in real-time, requiring businesses to process and analyze data quickly to gain insights and take action.
• Data quality: IoT data can be noisy or incomplete, making it challenging to ensure data accuracy and reliability.

How can data science optimize performance in IoT ecosystems?

Real life examples of IoT data

Here are some real-life examples of IoT data:

• Smart home data: Data collected from smart homes can include temperature readings, lighting usage, and energy consumption.
• Transportation data: Data collected from IoT-enabled vehicles can include fuel consumption, engine performance, and driving behavior.
• Manufacturing data: Data collected from machines and equipment in manufacturing plants can include machine performance metrics, maintenance schedules, and production yields.
• Agriculture data: Data collected from IoT-enabled sensors in agriculture can include soil moisture levels, temperature, and weather patterns.
• Healthcare data: Data collected from wearable IoT devices can include heart rate, sleep patterns, and physical activity.

Understanding IoT development

IoT development refers to the process of creating IoT-enabled devices and systems. The development process typically involves four stages: design, building, testing, and deployment.

The stages of IoT development

Here are brief explanations of each stage of IoT development:

Design

During this stage, developers use IoT development frameworks to design the architecture and user interface for the IoT device or system.

Building

This stage involves selecting the appropriate IoT development kit or board and assembling the hardware and software components of the IoT device or system.

Testing

IoT developers use IoT development frameworks and analytics tools to verify that the device or system meets the requirements specified during the design stage.

Deployment

This stage involves using IoT development boards and cloud platforms to deploy the IoT device or system to the market or within an organization.

The challenges of IoT development

Here are some of the main challenges of IoT development:

• Security: IoT devices and systems can be vulnerable to cyber-attacks and data breaches, making security a top priority during development. Developers must use IoT development frameworks and protocols that prioritize security.
• Compatibility: IoT devices and systems often use different communication protocols and standards, making it challenging to ensure compatibility between devices from different manufacturers. IoT development kits and frameworks can help address compatibility issues.
• Scalability: IoT devices and systems can generate vast amounts of data, making it challenging to scale the systems to accommodate this data and ensure real-time processing. IoT development boards and cloud platforms can provide the necessary processing power and storage capacity to handle large amounts of data.

The tools and technologies used in IoT development

Here are some of the common tools and technologies used in IoT development:

• IoT development kits: These kits contain the necessary components, such as sensors, microcontrollers, and actuators, to create IoT devices and systems.
• IoT development boards: These are computing platforms designed specifically for IoT development, providing connectivity and processing power.
• IoT development frameworks: These are software frameworks that provide the necessary tools and libraries to develop IoT applications.
• Cloud platforms and analytics tools: These are cloud-based platforms and tools that provide storage, processing, and analytics capabilities for IoT data.

IoT protocols 101: The essential guide to choosing the right option

Final words

IoT development is a critical aspect of creating and deploying IoT devices and systems. Developers must use IoT development frameworks, kits, and boards to design, build, test, and deploy these devices and systems effectively. While IoT development offers many benefits, it also comes with its own set of challenges, such as security, compatibility, and scalability. As the IoT ecosystem continues to grow and evolve, it is essential to prioritize IoT development to ensure that these devices and systems can fulfill their potential to transform industries and society.

The emergence of the Internet of Things (IoT) has led to the proliferation of connected devices and sensors that generate vast amounts of data. This data is a goldmine of insights that can be harnessed to optimize various systems and processes. However, to unlock the full potential of IoT data, organizations need to leverage the power of data science. Data science can help organizations derive valuable insights from IoT data and make data-driven decisions to optimize their operations.

Coherence between IoT and data science is critical to ensure that organizations can maximize the value of their IoT ecosystems. It requires a deep understanding of the interplay between IoT devices, sensors, networks, and data science tools and techniques. Organizations that can effectively integrate IoT and data science can derive significant benefits, such as improved efficiency, reduced costs, and enhanced customer experiences.

What is an IoT ecosystem?

An IoT (Internet of Things) ecosystem refers to a network of interconnected devices, sensors, and software applications that work together to collect, analyze, and share data. The ecosystem consists of various components, including devices, communication networks, data storage, and analytics tools, that work together to create an intelligent system that enables automation, monitoring, and control of various processes.

IoT protocols 101: The essential guide to choosing the right option

Some key characteristics of an IoT ecosystem include the following:

• Interconnectivity: IoT devices and applications are connected and communicate with each other to share data and enable coordinated actions.
• Data-driven: The ecosystem is built around data, and devices generate and share data that is used to enable automation, predictive maintenance, and other applications.
• Scalable: IoT ecosystems can be scaled up or down depending on the number of devices and the amount of data being generated.
• Intelligent: The ecosystem uses AI and machine learning algorithms to analyze data and derive insights that can be used to optimize processes and drive efficiencies.

What is an IoT ecosystem diagram?

An IoT ecosystem diagram is a visual representation of the components and relationships that make up an IoT ecosystem. It typically includes devices, communication networks, data storage, and analytics tools that work together to create an intelligent system.

The diagram provides a high-level overview of the ecosystem and helps to visualize the various components and how they are interconnected. It can also be used to identify potential areas for improvement and optimization within the system.

Understanding IoT ecosystem architecture

IoT ecosystem architecture refers to the design and structure of an IoT system, including the various components and how they are connected.

There are several layers to an IoT ecosystem architecture, including:

• Device layer: This layer includes the sensors and other devices that collect data and interact with the physical environment.
• Communication layer: This layer includes the communication networks that enable data to be transmitted between devices and other components.
• Data layer: This layer includes the data storage and management systems that store and process the data generated by the IoT system.
• Application layer: This layer includes software applications and tools that enable users to interact with and make sense of the data generated by the system.

Defining IoT ecosystems and their role in data science

IoT ecosystems play an important role in data science, as they generate vast amounts of data that can be used to drive insights and optimize processes.

Some ways that IoT ecosystems contribute to data science include:

• Enabling data collection: IoT devices generate large amounts of data that can be used to train machine learning algorithms and drive predictive models.
• Providing real-time data: IoT ecosystems can provide real-time data that can be used to identify trends and patterns and drive immediate action.
• Facilitating automation: IoT ecosystems can be used to automate various processes, reducing the need for manual intervention and enabling greater efficiency.

IoT ecosystems provide a rich source of data that can be used to drive insights and optimize processes, making them a valuable tool in the data science toolkit.

Components of IoT ecosystems

IoT ecosystems are composed of various components that work together to collect, process, and transmit data.

Hardware and software components of IoT ecosystems

IoT ecosystems consist of both hardware and software components that work together to enable automation, monitoring, and control of various processes. Some of the key hardware and software components of IoT ecosystems include:

• Hardware components: IoT hardware components include devices and sensors, communication networks, and data storage systems. These components are responsible for collecting, transmitting, and processing data.
• Software components: IoT software components include applications, operating systems, and analytics tools. These components are responsible for processing and analyzing the data generated by IoT devices and sensors.

Understanding the role of each component in IoT ecosystems

Each component in an IoT ecosystem plays a critical role in enabling the system to function effectively. Understanding the role of each component is essential in designing and optimizing IoT ecosystems. Some of the key roles of each component in IoT ecosystems include:

• Sensors and devices: IoT sensors and devices are responsible for collecting data from the physical environment. They play a critical role in enabling automation, monitoring, and control of various processes.
• Communication networks: Communication networks enable the transmission of data between IoT devices and other components in the ecosystem. They are responsible for ensuring that data is transmitted securely and reliably.
• Data storage: Data storage is essential in IoT ecosystems, as it is responsible for storing and managing the vast amounts of data generated by IoT devices and sensors. Data storage solutions need to be scalable, secure, and cost-effective.
• Analytics tools: Analytics tools are used to process and analyze the data generated by IoT devices and sensors. They play a critical role in enabling data-driven decision-making and identifying trends and patterns.

Importance of choosing the right components for IoT ecosystems

Choosing the right components for IoT ecosystems is essential in ensuring that the system functions effectively and efficiently. Some of the key reasons why choosing the right components is important to include:

• Scalability: IoT ecosystems need to be scalable, and choosing the right components can ensure that the system can be scaled up or down as needed.
• Reliability: IoT ecosystems need to be reliable, and choosing the right components can ensure that the system is resilient and can operate under various conditions.
• Security: IoT ecosystems need to be secure, and choosing the right components can ensure that data is transmitted and stored securely.

Challenges in designing IoT ecosystems

Designing and implementing IoT ecosystems can be challenging due to various factors, such as the complexity of the system, the diversity of devices, and the need for interoperability. Some of the common challenges in designing and implementing IoT ecosystems include the following:

• Data management: The vast amount of data generated by IoT devices can be overwhelming, making it challenging to store, process, and analyze the data effectively.
• Interoperability: IoT devices and sensors may come from different manufacturers, making it challenging to ensure that they are compatible and can communicate with each other.
• Security: IoT ecosystems are vulnerable to security threats, such as data breaches, hacking, and cyber attacks, making it essential to implement robust security measures.
• Scalability: As the number of devices in an IoT ecosystem increases, the system needs to be scalable and able to handle the increasing volume of data and traffic.
• Lack of standards: The lack of industry-wide standards makes it challenging to ensure that IoT devices and sensors are interoperable and can communicate with each other.
• Data security: IoT ecosystems are vulnerable to security threats, and organizations need to implement robust security measures to protect sensitive data.
• Data management: The vast amount of data generated by IoT devices can be challenging to store, process, and analyze effectively, making it essential to implement effective data management strategies.
• Integration with legacy systems: Integrating IoT ecosystems with legacy systems can be challenging, and organizations need to ensure that the systems are compatible and can work together seamlessly.

Solutions for overcoming IoT ecosystem design and implementation challenges

Overcoming the challenges of designing and implementing IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the solutions for overcoming IoT ecosystem design and implementation challenges include:

• Adopting standards: Adhering to industry-wide standards can help ensure that IoT devices and sensors are interoperable and can communicate with each other.
• Implementing robust security measures: Implementing robust security measures, such as encryption, firewalls, and intrusion detection systems, can help protect sensitive data.
• Leveraging cloud computing: Cloud computing can provide scalable and cost-effective data storage and processing solutions for IoT ecosystems.
• Implementing effective data management strategies: Implementing effective data management strategies, such as data analytics and visualization tools, can help organizations derive insights from the vast amounts of data generated by IoT devices.

Best practices for designing IoT ecosystems for data science

Designing IoT ecosystems for data science requires careful planning and execution. Some of the best practices for designing IoT ecosystems for data science include:

• Identifying use cases: Identifying use cases and defining clear objectives can help organizations design IoT ecosystems that meet specific business needs.
• Choosing the right components: Choosing the right components, such as sensors, communication networks, data storage, and analytics tools, is critical in ensuring that the system is effective and efficient.
• Ensuring interoperability: Ensuring that IoT devices and sensors are interoperable and can communicate with each other is essential in enabling data-driven decision-making.
• Implementing effective data management strategies: Implementing effective data management strategies, such as data analytics and visualization tools, can help organizations derive insights from the vast amounts of data generated by IoT devices.

Designing IoT ecosystems for data science requires a combination of technical expertise, strategic planning, and effective execution, and organizations need to adopt best practices to ensure success.

IoT and machine learning: Walking hand in hand towards smarter future

The role of data science in optimizing IoT ecosystems

Data science plays a critical role in optimizing IoT ecosystems by enabling organizations to derive insights from the vast amounts of data generated by IoT devices and sensors. Data science can help organizations identify trends and patterns, predict future events, and optimize processes.

Some of the key ways that data science can be used to optimize IoT ecosystems include:

• Predictive maintenance: Data science can be used to predict when equipment is likely to fail, enabling organizations to schedule maintenance proactively and avoid costly downtime.
• Optimization: Data science can be used to optimize processes, such as supply chain management, inventory management, and production scheduling, enabling organizations to operate more efficiently.
• Personalization: Data science can be used to personalize products and services, enabling organizations to deliver better customer experiences.

Leveraging data science to optimize IoT ecosystem performance

Leveraging data science to optimize IoT ecosystem performance requires a combination of technical expertise, strategic planning, and effective execution. Some of the key steps involved in leveraging data science to optimize IoT ecosystem performance include:

• Data collection: Collecting data from IoT devices and sensors is the first step in leveraging data science to optimize IoT ecosystem performance.
• Data management: Managing the vast amounts of data generated by IoT devices and sensors requires effective data management strategies, such as data cleansing, data normalization, and data modeling.
• Data analysis: Analyzing the data generated by IoT devices and sensors requires advanced analytics tools, such as machine learning algorithms and artificial intelligence.
• Insights and action: Deriving insights from the data generated by IoT devices and sensors is only useful if organizations can take action based on those insights. This requires effective communication, collaboration, and execution.

IoT ecosystem examples

There are several examples of data science applications in IoT ecosystems. Some of the key examples include:

• Predictive maintenance: Data science can be used to predict when equipment is likely to fail, enabling organizations to schedule maintenance proactively and avoid costly downtime. For example, General Electric uses data science to predict when its engines are likely to fail and schedule maintenance accordingly.
• Optimization: Data science can be used to optimize processes, such as supply chain management, inventory management, and production scheduling, enabling organizations to operate more efficiently. For example, Walmart uses data science to optimize its supply chain and reduce costs.
• Personalization: Data science can be used to personalize products and services, enabling organizations to deliver better customer experiences. For example, Amazon uses data science to personalize its recommendations for customers based on their browsing and purchase history.

Security and privacy concerns in IoT ecosystems

IoT ecosystems pose significant security and privacy challenges due to the sheer volume of data generated by numerous devices and sensors. The data can include highly sensitive information, such as biometric data, personal information, and financial details, making it critical to ensure that it is secured and protected.

One of the significant concerns is device security, where the devices are vulnerable to hacking, compromising their integrity and privacy. Network security is also a concern, where the data transmitted over the networks may be intercepted and compromised. Data privacy is another critical concern where there is a risk of unauthorized access to the vast amounts of sensitive data generated by IoT devices.

Devices and sensors are vulnerable to various types of attacks, including malware, distributed denial-of-service (DDoS) attacks, and phishing scams. These attacks can compromise the security of the devices and data generated, leading to devastating consequences.

Data breaches are another concern where the vast amounts of data generated by IoT devices need to be stored and transmitted securely. Any breach of the data can expose sensitive information, leading to privacy violations, identity theft, and other serious consequences.

Impact of security and privacy concerns on data science in IoT ecosystems

Security and privacy concerns can have a significant impact on data science in IoT ecosystems. Data quality can be compromised due to security and privacy concerns, leading to incomplete or inaccurate data that can affect the effectiveness of data science. The volume of data that is available for analysis may also be limited due to security and privacy concerns. Furthermore, security and privacy concerns can make it challenging to store and transmit data securely, increasing the risk of unauthorized access and misuse.

Building trust in IoT ecosystems: A privacy-enhancing approach to cybersecurity

Best practices for ensuring security and privacy in IoT ecosystems

Ensuring security and privacy in IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the best practices for ensuring security and privacy in IoT ecosystems include:

• Adopting security standards: Adhering to industry-wide security standards can help ensure that IoT devices and sensors are secure and can protect sensitive data.
• Implementing robust encryption: Implementing robust encryption, such as SSL/TLS, can help protect data transmitted between IoT devices and other components in the ecosystem.
• Implementing access controls: Implementing access controls, such as multi-factor authentication and role-based access control, can help ensure that only authorized users can access sensitive data.
• Conducting regular security audits: Conducting regular security audits can help organizations identify vulnerabilities and address security and privacy concerns proactively.

Ensuring security and privacy in IoT ecosystems are essential in enabling organizations to leverage data science to optimize their systems. Implementing best practices can help organizations minimize security and privacy risks and derive maximum value from their IoT ecosystems.

Final words

In closing, the combination of IoT and data science offers a world of endless possibilities for organizations looking to optimize their systems and processes. However, it also presents significant challenges, particularly around security and privacy.

To ensure the coherence of IoT and data science, organizations must take a comprehensive approach to data management and security, adopting best practices and adhering to industry standards. By doing so, they can unlock the full potential of their IoT ecosystems, derive valuable insights from their data, and make data-driven decisions that drive growth and success.

As IoT continues to evolve and expand, organizations that can effectively leverage data science to analyze IoT data will be well-positioned to thrive in the digital age.

As the Internet of Things (IoT) continues to grow in popularity, so does the need for IoT cybersecurity. IoT refers to the interconnected network of devices, vehicles, and appliances that can communicate with each other via the internet without the need for human intervention. This technology has the potential to revolutionize many aspects of modern life, from home automation to healthcare. However, with this increased connectivity comes an increased risk of cyber attacks. Therefore, it is critical to ensure that IoT devices are secured against cybersecurity threats to protect against the theft of sensitive data, system disruptions, and more.

What is IoT?

IoT is a technology that allows for the interconnectivity of devices and machines, which can range from smart homes and appliances to industrial systems and even vehicles. The idea is that these devices can be controlled and monitored remotely through the internet, using specialized software and sensors.

As technology has developed, it has gained in popularity and adoption. By 2025, it is estimated that there will be over 75 billion IoT devices worldwide. This massive growth has led to increased attention to the security of these devices, as they have the potential to be hacked and exploited by cybercriminals.

The importance of IoT cybersecurity

The importance of IoT cybersecurity cannot be overstated. As more and more devices become connected to the internet, the potential for cyber attacks grows. This can lead to a variety of consequences, from identity theft and financial loss to the compromise of sensitive data and even physical harm.

Therefore, it is critical that IoT devices are secured against cybersecurity threats. This means implementing appropriate security measures at every level, from hardware to software, as well as ensuring that users are educated about the risks and how to protect themselves.

Risks and threats to IoT cybersecurity

While IoT technology has the potential to make our lives easier and more convenient, it also comes with inherent security risks. Understanding these risks and threats is crucial to protecting against them.

Explanation of the different types of IoT cybersecurity risks and threats

One of the main risks associated with IoT devices is that they often lack sufficient security measures. This can make them vulnerable to attacks, including:

Unauthorized access

Hackers can gain access to IoT devices and networks, allowing them to steal sensitive data or take control of the devices.

Malware and viruses

IoT devices can be infected with malware or viruses, which can cause them to malfunction or be used for malicious purposes.

IoT protocols 101: The essential guide to choosing the right option

Data breaches

IoT devices can collect and transmit sensitive data, such as personal or financial information. If this data is not properly secured, it can be stolen by cybercriminals.

Denial of Service (DoS) attacks

Attackers can flood IoT networks with traffic, causing them to become overwhelmed and unavailable.

Examples of cybersecurity breaches that have occurred in the past

There have been several high-profile cybersecurity breaches involving IoT devices in recent years. For example, in 2016, the Mirai botnet attacked internet-connected devices, such as routers and security cameras, and used them to launch a DDoS attack on a major DNS provider, causing widespread internet disruptions.

In another incident, hackers were able to remotely take control of a Jeep Cherokee’s entertainment system, steering, and brakes, demonstrating the potential for IoT devices to be used to cause physical harm.

These incidents highlight the importance of securing IoT devices against cyber attacks, as the consequences can be severe.

Ensuring the security of IoT devices requires a multi-layered approach that addresses potential vulnerabilities at every level. This section will discuss the importance of securing devices at every level, as well as some key principles for securing IoT devices.

Securing devices at every level

Securing IoT devices requires a comprehensive approach that addresses potential vulnerabilities at every level, from the hardware to the software. This also covers identity and access management measures, which will help the right people access the right tools they need. This includes:

Hardware security

Ensuring that devices are designed and manufactured with security in mind, such as implementing secure boot processes and using tamper-proof hardware.

Network security

Securing the communication channels between devices and the network, such as implementing firewalls and using encryption.

Application security

Ensuring that the software and applications running on IoT devices are secure and free from vulnerabilities, such as implementing regular security updates and patches.

Key principles for securing IoT devices

Some key principles for securing IoT devices include:

• Device authentication: Verifying the identity of devices and ensuring that only authorized devices can access the network.
• Encryption: Using encryption to secure the communication channels between devices and the network, as well as protecting sensitive data.
• Secure boot: Ensuring that devices boot up in a secure and trusted state, preventing malicious software from being loaded.

Implementing these principles can help ensure the security of IoT devices and protect against cyber attacks.

Best practices for IoT cybersecurity

While securing IoT devices requires a multi-layered approach and implementing key security principles, including multi-factor authentication, there are also some best practices that can help ensure the security of these devices. This section will provide an overview of some best practices for IoT cybersecurity.

Some best practices for IoT cybersecurity include:

• Regular software updates: Keeping software and firmware up-to-date can help ensure that devices are protected against known vulnerabilities.
• Network segmentation: Segmenting IoT devices on a separate network can help prevent unauthorized access and limit the impact of potential cyber attacks.
• Strong password policies: Ensuring that passwords are strong, unique, and changed regularly can help prevent unauthorized access to devices and networks.

Importance of user education and awareness

In addition to implementing best practices and key security principles, user education and awareness are also crucial for ensuring the security of IoT devices. This includes:

• Educating users on how to use and secure their devices, such as providing guidance on setting strong passwords and regularly updating software.
• Raising awareness about common cybersecurity risks and threats, such as phishing and social engineering, and how to identify and avoid them.

By promoting user education and awareness, as well as implementing best practices and key security principles, organizations and individuals can help ensure the security of IoT devices and protect against potential cyber attacks.

Emerging technologies for IoT cybersecurity

As the Internet of Things continues to grow, new emerging technologies are being developed to help improve IoT cybersecurity. This section will discuss some of the emerging technologies for IoT cybersecurity, including blockchain, artificial intelligence, and machine learning.

Blockchain

Blockchain technology has the potential to enhance IoT security by providing a decentralized and immutable ledger for storing and sharing data. By using blockchain, IoT devices can securely authenticate and communicate with one another, and transactions can be securely recorded and verified. This technology can also help to prevent cyber attacks by providing a tamper-proof record of all transactions and data exchanges.

Artificial intelligence

Artificial intelligence (AI) can help improve IoT cybersecurity by automating threat detection and response. With AI-powered security solutions, IoT devices can be monitored in real time for anomalous behavior, and potential threats can be identified and addressed more quickly. AI can also help to improve the accuracy and speed of security analytics, allowing organizations to better identify and respond to potential cyber threats.

Machine learning

Machine learning (ML) can also help improve IoT security by enabling devices to learn and adapt to potential threats over time. By analyzing large amounts of data and identifying patterns and anomalies, machine learning algorithms can help to identify potential security threats and mitigate them before they become major issues. This technology can also help to improve the accuracy of security analytics and enable faster incident response times.

As these emerging technologies continue to evolve, they have the potential to greatly enhance the security of IoT devices and protect against potential cyber attacks. By incorporating these technologies into IoT cybersecurity strategies, organizations can better protect their devices, networks, and sensitive data.

Challenges in IoT cybersecurity

While there are many best practices, key principles, and emerging technologies that can help secure IoT devices, there are also many challenges and obstacles that organizations and individuals face in securing these devices. This section will discuss some of the challenges in IoT cybersecurity.

Some of the challenges and obstacles in IoT cybersecurity include the following:

• Large-scale deployment: With millions of IoT devices in use worldwide, it can be difficult to manage and secure all of them, especially as the number of devices continues to grow.
• Complexity: IoT devices can be complex and diverse, making it difficult to create a unified security framework or solution.
• Legacy devices: Many older IoT devices were not designed with security in mind, and they may not be able to support the latest security protocols and technologies.
• Cost: Securing IoT devices can be expensive, and many organizations may not have the resources to implement the latest security technologies or practices.

IoT security is an ongoing process

It’s important to remember that IoT cybersecurity is an ongoing process that requires constant attention and adaptation. As new threats and vulnerabilities are discovered, security strategies and technologies must be updated and improved to stay ahead of potential cyber attacks. This requires ongoing monitoring, risk assessment, and collaboration between stakeholders.

Unlocking the full potential of connected devices with IoT analytics

Conclusion

In conclusion, the security of IoT devices is of utmost importance as the number of these devices continues to grow. This article has discussed the importance of IoT cybersecurity, risks and threats to IoT security, key principles for securing IoT devices, best practices, emerging technologies, and challenges in IoT cybersecurity. It’s important to remember that IoT security is an ongoing process that requires constant attention and adaptation. By implementing best practices and key security principles, incorporating emerging technologies, and promoting user education and awareness, organizations and individuals can help ensure the security of IoT devices and protect against potential cyber attacks.

IoT machine learning has revolutionized the way businesses operate, by transforming vast amounts of data into actionable insights and decision-making tools. The era of technology is constantly evolving, with new advancements appearing almost every day. One such field that has gained immense popularity in recent times is the combination of Internet of Things (IoT) and Machine Learning (ML).

The introduction to IoT machine learning involves understanding the integration of two cutting-edge technologies: the Internet of Things and machine learning. For students and professionals alike, grappling with these concepts not only requires theoretical understanding but also practical application, often leading them to seek resources or assistance online, perhaps even searching for services to help write my research paper on such complex topics.

This innovative blend of technologies is opening doors to new business opportunities, and is poised to play a major role in shaping the future of our world. In a world that is becoming increasingly data-driven, IoT machine learning provides a new and exciting avenue for businesses to leverage the power of big data, and gain a competitive edge in the market. With its wide-ranging applications and limitless potential, IoT machine learning is set to be one of the key drivers of innovation and growth in the years to come.

Introduction to IoT machine learning

The introduction to IoT machine learning involves understanding the integration of two cutting-edge technologies: the Internet of Things and machine learning.

Definition of IoT

The Internet of Things (IoT) refers to the interconnected network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, and network connectivity, allowing these objects to collect and exchange data.

Definition of machine learning

On the other hand, machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, by learning from patterns and insights in data.

The concept of IoT machine learning

The concept of IoT machine learning combines the strengths of both technologies to bring about a new level of automation, optimization, and intelligence to various industries. By leveraging the vast amounts of data generated by IoT devices and using machine learning algorithms to analyze and interpret it, organizations can gain valuable insights, make informed decisions, and drive innovation. The integration of IoT and machine learning has the potential to transform how businesses operate, how products are designed and manufactured, and how services are delivered, leading to improved customer experiences and increased operational efficiency.

IoT protocols 101: The essential guide to choosing the right option

IoT and machine learning: How do they work together?

IoT and machine learning technologies complement each other in powerful ways, with IoT devices generating vast amounts of data that can be analyzed by machine learning algorithms to gain insights and drive innovation. By integrating these technologies, organizations can automate processes, improve efficiency, and make data-driven decisions in real-time.

The role of IoT in machine learning

The IoT network generates a massive amount of data that can be leveraged to train machine learning algorithms and improve their accuracy. IoT devices can collect data from various sources, such as sensors, cameras, and other connected objects, and transmit it to the cloud or edge devices for analysis. By using machine learning algorithms to process and analyze this data, organizations can gain valuable insights and automate decision-making processes, leading to improved efficiency and productivity.

The role of machine learning in IoT

Machine learning algorithms can enhance the capabilities of IoT devices by enabling them to process and analyze data in real-time, and take actions based on the insights they have gained. By integrating machine learning models into IoT devices, organizations can improve their performance, automate processes, and make data-driven decisions on the edge, reducing the need for cloud-based computing and reducing latency.

Examples of IoT and machine learning integration

There are many examples of IoT and machine learning integration across various industries, including:

• Predictive maintenance in manufacturing, where machine learning algorithms analyze sensor data from industrial machines to predict when maintenance is needed, reducing downtime and improving efficiency.
• Customer behavior analysis in retail, where IoT devices collect data on customer behavior, and machine learning algorithms analyze this data to provide insights and drive targeted marketing efforts.
• Real-time decision-making in agriculture, where IoT sensors collect data on soil moisture and crop growth, and machine learning algorithms analyze this data to optimize irrigation and fertilizer usage.

Below we will dig deeper about these examples.

The benefits of IoT machine learning

The integration of IoT and machine learning technologies offers a range of benefits to organizations across various industries. These benefits include:

Improved business efficiency

The use of IoT and machine learning can automate a range of business processes, freeing up time and resources for other tasks. For instance, predictive maintenance in manufacturing uses machine learning algorithms to predict when equipment needs maintenance, reducing downtime and improving efficiency.

Mastering the art of efficiency through business process transformation

Enhanced data analysis and predictive maintenance

Machine learning algorithms can analyze vast amounts of data generated by IoT devices, providing valuable insights that can drive decision-making. Predictive maintenance is one example of how machine learning can be used to improve business outcomes, by predicting equipment failure before it occurs and reducing downtime.

Real-time decision-making and problem-solving

By integrating machine learning algorithms into IoT devices, organizations can make data-driven decisions in real-time, without manual intervention. For example, IoT sensors and machine learning algorithms in agriculture can be used to optimize irrigation and fertilizer usage in real-time, improving crop yields and reducing waste.

Cost savings and increased ROI

IoT and machine learning technologies can lead to cost savings and increased return on investment (ROI) for organizations. For instance, predictive maintenance in manufacturing can reduce downtime and improve equipment performance, leading to cost savings and increased productivity.

In conclusion, the integration of IoT and machine learning technologies offers a range of benefits that can drive business efficiency, improve decision-making, and drive cost savings. These benefits are already being realized by organizations across various industries, and the trend is set to continue as technology advances.

Applications of IoT machine learning in various industries

The integration of IoT and machine learning has a wide range of applications across various industries. The combination of these technologies allows for real-time data analysis and improved decision-making, leading to increased efficiency and cost savings. Let’s take a look at how IoT machine learning is being used in the following industries.

Healthcare

In the healthcare industry, IoT machine learning can be used to monitor patients remotely and provide real-time health data to healthcare professionals. This information can be used to diagnose and treat patients more effectively, reducing the need for in-person visits and minimizing the spread of illness. IoT-powered devices such as wearable fitness trackers and smart inhalers can also provide valuable data for machine learning algorithms to analyze, helping healthcare professionals make more informed decisions.

Retail

IoT machine learning is also being utilized in the retail industry to enhance customer experiences and improve the efficiency of supply chain management. For example, retailers can use IoT sensors to track inventory levels in real-time, enabling them to make data-driven decisions about when to reorder products and minimize waste. Additionally, machine learning algorithms can be used to analyze customer purchase patterns, enabling retailers to offer personalized product recommendations and improve overall customer satisfaction.

Manufacturing

In the manufacturing industry, IoT machine learning can be used to optimize production processes, improve quality control, and reduce waste. For example, machine learning algorithms can be used to analyze data from IoT sensors on factory equipment, allowing manufacturers to identify areas for improvement and make proactive repairs before equipment breakdowns occur. This can lead to reduced downtime, improved productivity, and increased profits.

Monitoring and controlling digital manufacturing with AI

Agriculture

In the agriculture industry, IoT machine learning can be used to improve crop yields, minimize waste, and reduce the use of harmful chemicals. For example, machine learning algorithms can be used to analyze data from IoT-enabled sensors in soil and weather patterns to optimize crop irrigation and fertilizer application. This can lead to improved crop health, reduced costs, and increased profits for farmers. We highly recommend this academic paper regarding the future of agriculture: Machine learning in agriculture domain: A state-of-art survey.

Transportation and logistics

In the transportation and logistics industry, IoT machine learning can be used to improve delivery times and reduce waste. For example, machine learning algorithms can be used to analyze data from GPS-enabled vehicles to optimize delivery routes and reduce fuel consumption. This can lead to faster delivery times, reduced costs, and improved customer satisfaction.

The integration of IoT and machine learning is revolutionizing the way various industries operate, leading to increased efficiency, improved customer experiences, and reduced costs.

The future of IoT machine learning

The future of IoT and machine learning is an exciting and rapidly evolving field that holds great potential for many industries. With advancements in technology and increasing adoption by businesses of all sizes, we can expect to see significant growth and development in this area in the coming years. From the integration of 5G networks and edge computing to the potential challenges and solutions that will arise, the future of IoT and machine learning is sure to be full of exciting new opportunities and innovations.

Advancements in technology

As technology continues to advance, so does the potential for IoT and machine learning. With the development of new sensors, algorithms, and other components, the capabilities of these systems will continue to grow. For example, improvements in machine learning algorithms will allow for more accurate and sophisticated data analysis. This, in turn, will lead to more advanced predictions, improved problem-solving, and better decision-making.

Integration with 5G and edge computing

The integration of IoT and machine learning with 5G and edge computing technologies is expected to bring significant benefits to a range of industries. With 5G networks providing faster data transfer speeds, IoT systems can collect and transmit data in real-time, enabling more sophisticated machine learning algorithms to be used in real-world applications. Edge computing, which processes data closer to the source, will enable faster analysis of this data and help mitigate privacy concerns by reducing the amount of data that needs to be transmitted to the cloud.

Industrial operations will get a boost with the 5G time-critical services

Increasing adoption by small and medium-sized enterprises

IoT machine learning is becoming increasingly accessible to small and medium-sized enterprises (SMEs). As these systems become more cost-effective and easier to implement, more SMEs are expected to adopt them, leveraging their benefits to improve business operations and compete with larger enterprises.

Potential challenges and solutions

While the future of IoT machine learning is promising, there are also potential challenges that need to be addressed. These include privacy concerns, security risks, and the need for interoperability between different systems. However, these challenges can be addressed through the development of secure, interoperable standards and the adoption of best practices in data management and privacy protection. By working together, the industry can ensure that IoT machine learning reaches its full potential and provides benefits to businesses and society as a whole.

Conclusion

In conclusion, the integration of IoT and machine learning has brought about a revolution in the way businesses operate, by enabling them to harness the power of data and drive innovation. With advancements in technology and the increasing adoption by small and medium-sized enterprises, IoT machine learning holds immense potential for shaping the future. It is imperative for organizations to stay abreast of the latest developments in the field, and leverage their synergistic relationship for better decision-making, cost savings and increased ROI. As the world becomes increasingly interconnected, the convergence of IoT and machine learning is poised to play a significant role in shaping the future, and businesses that are ready to embrace this transformation will reap the benefits.

IoT protocols are the set of rules and guidelines that govern communication between devices in the Internet of Things (IoT), enabling the creation of smart and connected systems that can sense, collect, and exchange data in real-time.

The Internet of Things is a rapidly growing technology that is transforming the way we live and work. By connecting a wide range of devices and systems through the internet, the IoT enables the creation of smart and connected systems that can sense, collect, and exchange data in real-time.

We believe that it has the potential to revolutionize the way we interact with the world around us. From improving efficiency and productivity in the workplace, to enabling new levels of personalization and customization, the IoT is creating new opportunities for innovation and creativity.

One of the key benefits of the IoT is its ability to improve efficiency and productivity. By automating tasks and processes, the IoT can help to reduce the need for manual labor and improve the speed and accuracy of operations. For example, in manufacturing, the IoT can be used to optimize production lines and reduce waste, while in healthcare, it can be used to improve patient care and reduce the risk of errors.

The IoT is also playing a significant role in the development of smart cities, which are urban environments that use sensors, data analytics, and other technologies to improve the quality of life for citizens. For example, smart cities can use the IoT to optimize traffic flow, improve public safety, and reduce energy consumption.

However, as with any technology, there are also potential risks and challenges associated with the IoT. One of the main concerns is the issue of security, as the interconnected nature of the IoT makes it more vulnerable to cyber attacks. Ensuring the security of IoT devices and systems is therefore crucial in order to fully realize the benefits of this technology.

In conclusion, the IoT is a powerful and transformative technology that has the potential to revolutionize the way we live and work. While there are certainly challenges to be addressed, the benefits of the IoT are undeniable, and we believe that it will play an increasingly important role in our world in the years to come.

What are IoT protocols?

IoT protocols are the set of rules and guidelines that govern communication between devices in the Internet of Things. These protocols enable devices to communicate with each other and exchange data, allowing for the creation of smart and connected systems.

There are many different IoT protocols available, each with its own unique set of features and capabilities. Some IoT protocols are designed for specific purposes, such as wireless communication, data transmission, or device management, while others are more general-purpose and can be used in a variety of different contexts.

Unlocking the full potential of connected devices with IoT analytics

IoT protocols are essential for the operation of an IoT system, as they enable devices to communicate and exchange data, enabling the creation of smart and connected systems. The right protocol can ensure that devices are able to communicate effectively and exchange data efficiently, which is essential for the smooth operation of an IoT system.

How many protocols are there in IoT?

There are many different protocols that are used in the Internet of Things, and the number of protocols is constantly evolving as new technologies are developed and adopted. These are the main IoT protocols:

• Bluetooth
• Wi-Fi
• Zigbee
• Z-Wave
• 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks)
• MQTT (Message Queue Telemetry Transport)
• CoAP (Constrained Application Protocol)
• DDS (Data Distribution Service)
• AMQP (Advanced Message Queuing Protocol)

These protocols are used for a variety of different purposes, including wireless communication, data transmission, and device management. Some protocols are more suitable for specific applications or environments, while others are more general-purpose and can be used in a variety of different contexts.

In addition to these widely used IoT protocols, there are many other specialized protocols that are used in specific sectors or industries, such as industrial control systems, automotive, and healthcare. The exact number of IoT protocols is difficult to determine, as new protocols are constantly being developed and adopted.

Most commonly used IoT protocols

There are many different IoT protocols available, each with its own unique set of features and capabilities. Some of the most commonly used protocols include:

• Bluetooth is a wireless communication technology that allows devices to connect and exchange data over short distances.
• Wi-Fi is a wireless networking technology that enables devices to connect to the internet and communicate with each other.
• Zigbee is a wireless communication protocol designed specifically for IoT applications, with a focus on low power consumption and long range communication.
• Z-Wave is a wireless communication protocol designed for home automation and IoT applications, with a focus on low power consumption and security.

When choosing an IoT protocol, it is important to consider a number of factors, including the distance over which devices need to communicate, the amount of data that needs to be transmitted, and the power consumption requirements of the devices.

In conclusion, IoT protocols play a crucial role in enabling communication and data exchange between devices in the Internet of Things. With so many different protocols available, it is important to choose the right one for your specific application in order to ensure optimal performance and reliability.

Comparison of IoT protocols

When comparing IoT protocols, there are a number of factors to consider in order to choose the right one for your specific application. These factors include:

Distance

The distance over which devices need to communicate is an important consideration when choosing an IoT protocol. Some IoT protocols have a longer range than others, and the right protocol will depend on the specific requirements of your application.

IoT gateway: The veins of connected devices

Data rate

The data rate, or the amount of data that can be transmitted in a given time period, is also an important consideration. IoT protocols with a higher data rate are generally more suitable for transmitting large amounts of data, while protocols with a lower data rate may be more suitable for low-bandwidth applications.

Power consumption

Many IoT devices are battery-powered, and the power consumption of the protocol can have a significant impact on the lifespan of the device. Protocols that are more power-efficient may be more suitable for low-power devices.

Security

Some IoT protocols are more secure than others, and choosing a secure protocol can help to protect against hacking and other security threats.

Compatibility

Different IoT protocols are compatible with different devices and systems, and choosing a protocol that is compatible with your devices and infrastructure is essential for the smooth operation of an IoT system.

By considering these factors, you can choose the right IoT protocol for your specific application and ensure that your IoT system performs at its best.

Why choosing the right IoT protocol is very important?

Choosing the right IoT protocol is important for a number of reasons:

• Performance: The right protocol can ensure that devices are able to communicate effectively and exchange data efficiently, which is essential for the smooth operation of an IoT system.
• Reliability: A reliable protocol can minimize the risk of communication failures or data loss, ensuring that devices are able to operate smoothly and effectively.
• Power consumption: Many IoT devices are battery-powered, and using the right protocol can help to minimize power consumption, extending the life of the device.
• Security: Some IoT protocols are more secure than others, and choosing a secure protocol can help to protect against hacking and other security threats.
• Compatibility: Different IoT protocols are compatible with different devices and systems, and choosing a protocol that is compatible with your devices and infrastructure is essential for the smooth operation of an IoT system.

By choosing the right IoT protocol, you can ensure that your IoT system performs at its best and meets the needs of your specific application.

Final words

In conclusion, the selection of an appropriate IoT protocol is crucial for the smooth operation and success of an IoT system. With a range of protocols available, each offering unique capabilities and features, it is important to carefully consider the specific needs of the application and the available devices in order to choose the right protocol.

IoT analytics involves using specialized tools and techniques to analyze the vast amounts of data generated by connected devices in order to extract meaningful insights and inform decision-making. It helps organizations to better understand their operations, identify patterns and trends, and optimize their systems and processes.

What is IoT analytics?

IoT (Internet of Things) analytics refers to the process of collecting, storing, and analyzing data generated by the Internet of Things (IoT) devices. IoT devices are connected to the internet and are able to collect and transmit data about their environment, usage, and other characteristics. This data can be used to improve the performance and efficiency of the device, as well as to gain insights into the behavior and characteristics of the device’s users.

IoT analytics often involves the use of specialized software and tools to process and analyze the data generated by IoT devices. This may include data visualization tools, machine learning algorithms, and other advanced analytics techniques. The goal of IoT analytics is to extract valuable insights and knowledge from the data generated by IoT devices and to use this information to make informed decisions and optimize the performance of the device and its related systems.

IoT analytics architecture

Effectively implementing IoT architecture will guarantee that you have access to useful insights from IoT analytics whenever you need them. Think about each of the following IoT architecture components.

• Data generation: Continuous data is produced by sensors, IoT devices, and smart gadgets.
• IoT message broker and MQTT protocol: The MQTT protocol and an IoT message broker are used by IoT devices to communicate since internet connectivity is frequently intermittent. In order to communicate with other services that subscribe to particular topics within the message broker in order to access device data, the message broker employs a publish and subscribe technique.
• A streaming service: To provide dependable ingestion and delivery to a staging table in the cloud data warehouse, real-time device data is ingested and buffered using a streaming service.
• Cloud object storage: Cloud object storage is used to stage batch data prior to ingestion when the application calls for it. For instance, minute-by-minute data might be kept in cloud object storage, while longer-term aggregated data might be kept in the cloud data warehouse.
• Streaming data support: Make sure your cloud data warehouse supports JSON and other semi-structured data types natively for simple device data ingestion.
• IoT rules engine: The business logic needed by the application is hosted by an IoT rules engine, which runs on data from the message broker and the cloud data warehouse. The rules engine communicates with the controls through messages.

What are the types of IoT analytics?

There are several types of IoT analytics, including:

Descriptive analytics

Descriptive analytics involves summarizing and visualizing data to understand what has happened in the past. This type of analysis is useful for understanding trends and identifying patterns in the data.

Predictive analytics

Predictive analytics involves using data and machine learning algorithms to make predictions about future events. This type of analysis is useful for forecasting demand, identifying potential problems, and making informed decisions.

Prescriptive analytics

Prescriptive analytics involves using data and machine learning algorithms to not only predict what will happen in the future but also recommend actions to take in response. This type of analysis is useful for automating decision-making and optimizing processes.

Real-time analytics

Real-time analytics involves analyzing data as it is generated by IoT devices in order to make immediate decisions or take immediate actions. This type of analysis is useful for detecting and responding to problems in real time.

Historical analytics

Historical analytics involves analyzing data over a longer period of time, typically months or years. This type of analysis is useful for understanding long-term trends and identifying patterns that may not be immediately apparent in real-time data.

How does IoT analytics work?

IoT analytics typically involves several key steps. Let’s review them step by step.

Data collection

The first step in IoT analytics is to collect data from IoT devices. This may involve installing sensors or other data collection devices on the device or connecting the device to a network that allows it to transmit data.

Data storage

Once the data has been collected, it needs to be stored in a central repository or database. This may be done using a cloud-based storage solution or by using on-premises servers or storage devices.

NB-IoT will shape the future of smart cities

Data processing

Once the data has been collected and stored, it needs to be processed and analyzed. This may involve using specialized software and tools to filter, clean, and transform the data, as well as to extract insights and generate reports.

Data visualization

To make the insights and analysis generated by IoT analytics more easily understood, it is often helpful to use data visualization techniques such as charts, graphs, and maps. These can help to highlight trends, patterns, and relationships in the data that may not be immediately apparent from raw data alone.

Data-driven decision making

The final step in IoT analytics is to use the insights and analysis generated from the data to inform decision-making. This may involve adjusting the performance or behavior of the IoT device or making changes to related systems and processes to optimize their performance and efficiency.

Overall, the goal of IoT analytics is to extract valuable insights and knowledge from the data generated by IoT devices and to use this information to improve the performance and efficiency of the device and its related systems. By analyzing data from IoT devices, organizations can gain a deeper understanding of how the device is being used, identify opportunities for optimization, and make informed decisions that drive business value.

The use cases of IoT analytics

There are many different business use cases for IoT analytics, depending on the specific industry and needs of the organization. Some common examples include:

• Supply chain optimization: IoT devices can be used to track the movement and status of goods throughout the supply chain, allowing organizations to optimize logistics and improve efficiency.
• Predictive maintenance: By analyzing data from IoT sensors on equipment and machines, organizations can identify potential problems before they occur, allowing for proactive maintenance and reducing the risk of equipment failure.
• Customer experience: IoT analytics can be used to track customer interactions with products and services, allowing organizations to identify opportunities to improve the customer experience and increase customer satisfaction.
• Asset tracking: IoT devices can be used to track the location and status of assets, such as vehicles or equipment, allowing organizations to optimize the use of these assets and reduce the risk of loss or theft.
• Environmental monitoring: IoT sensors can be used to monitor environmental conditions, such as temperature, humidity, and air quality, allowing organizations to optimize energy usage and improve sustainability.

IoT analytics jobs and opportunities

There are a variety of job roles related to IoT analytics, including:

• Data scientist: Data scientists are responsible for designing and implementing algorithms and models to analyze and interpret IoT data. They may use techniques such as machine learning and statistical analysis to extract insights and identify patterns in the data.
• IoT data engineer: IoT data engineers are responsible for designing and building the infrastructure and pipelines necessary to collect, store, and process IoT data. This may involve working with databases, cloud platforms, and data processing tools to ensure that data is available for analysis in a timely and reliable manner.
• IoT solution architect: IoT solution architects are responsible for designing and implementing end-to-end IoT solutions. They may work closely with data scientists and data engineers to ensure that the necessary data is collected, processed, and analyzed to meet the needs of the business.
• BI analyst: Business intelligence analysts are responsible for using data to inform business decisions. They may work with IoT data to understand trends, identify opportunities, and make recommendations to stakeholders.
• IoT project manager: IoT project managers are responsible for planning and executing IoT projects. They may work closely with cross-functional teams to ensure that projects are delivered on time and within budget.

IoT sensors smarten everyday objects with awareness and cognition

Best IoT analytics companies and platforms

There are many different companies and platforms that offer IoT analytics solutions, and it can be difficult to determine which one is the best fit for a particular organization. Some of the top IoT analytics companies and platforms include:

AWS IoT Analytics

AWS IoT Analytics is a fully-managed service that makes it simple to run complicated analytics on enormous volumes of IoT data without worrying about the expense and complexity involved in developing your own IoT analytics platform.

SensorCloud

The SensorCloud platform from MicroStrain is a cutting-edge platform for storing, visualizing, and remotely managing sensor data. It makes use of strong cloud computing technologies to offer exceptional data scalability, quick visualization, and user-programmable analysis.

Exosite ExoSense IoT

ExoSense is a remote condition monitoring tool that may give operational insight into industrial assets, systems, and equipment. It can be set up in a matter of minutes. Any firm may now gather and visualize sensor data to give new features and services to consumers, estimate maintenance needs, and warn users of dangerous operating situations.

TrendMiner

Industrial Analytics Solutions for Process Manufacturing with Self-Service Self-service data analytics are provided by TrendMiner, a Software AG firm that is a part of the IoT & Analytics division, to optimize process performance in sectors like chemical, petrochemical, oil & gas, pharmaceutical, metals & mining, and other process manufacturing industries. With the help of no data scientists, consumers can directly query time-series data using the high-performance analytics engine at the foundation of TrendMiner software.

Google Cloud IoT Core

In order to connect, monitor, and consume data from widely scattered devices in an easy and safe manner, Google has developed a completely managed service.

Conclusion

From a business perspective, IoT analytics can offer significant risks, rewards, and benefits. Some potential risks include data security and privacy concerns, as well as the risk of investing in technologies that may become obsolete or unsupported.

However, the rewards of IoT analytics can also be significant. By collecting and analyzing data from IoT devices and systems, businesses can gain insights into their operations, improve efficiency, and make informed decisions. This can lead to cost savings, increased productivity, and competitive advantage.

Additionally, the benefits of IoT analytics extend beyond the business itself, as the insights gained through analysis can be used to improve products and services and to solve problems in a variety of industries and sectors. Overall, while there are risks associated with IoT analytics, the potential rewards and benefits make it a valuable tool for businesses looking to improve their operations and drive growth.

Lithuanian data tech and AI startup has closed a pre-seed funding round to help millions of users worldwide improve their health. 

Spike, makers of the API aggregation and an ETL solution for data from wearables and IoT devices, today announced the closing of a $700,000 pre-seed round to help digital health firms improve their clients’ lives. Spike allows companies to easily integrate biomarker data from 200+ wearable sensors and IoT devices into their applications. The funding round was led by Geek Ventures of New York City, with participation from CEAS Investments and APX.

Data collected from wearable sensors, smartwatches, and IoT devices are actively used throughout the healthcare industry. However, only a fraction of the market participants know how to utilize the data to its potential. Using Spike, businesses can integrate data from commercially used optical, electrical, electrochemical, and electromechanical sensor types via a single API. These devices extract biometric data such as heart rate variability (HRV), glucose and cortisol levels, calories, sleep depth, blood pressure, and more. Spike imports these metrics directly into clients’ cloud via a managed ETL service while providing AI tools for health-related prediction, recommendation, and prevention. 

“Wearable sensors are becoming much smaller, more accurate, and accessible to a broader user base. Our solution helps digital health companies easily integrate biosensor data, process it, and apply AI models. Developing this tech in-house is complicated, expensive, and resource-heavy. Spike aims to make this data management journey effortless and open opportunities to the next-generation of healthcare and disease prevention,” said Povilas Gudzius, co-founder and CEO.

Spike’s pre-seed funding round was led by Geek Ventures in NYC, with participation from Florida-based CEAS Investments and APX, an early-stage investor based in Berlin and backed by Axel Springer and Porsche. Angel investors from Austin, Texas, NYC and Silicon Valley also participated in the round. “Receiving backing from these experts is a vote of confidence for our team and further solidifies our mission, to bring benefits of cross-sensor data fusion to real-life challenges such as improving health”, said Povilas Gudzius.

It is estimated that over the next eight years, a person, on average, will use up to 30 different IoT devices – three times more than we use now. The growth in wearable sensors and biomarker data has resulted in hundreds of new applications for professional fitness, diabetes prevention, nutrition, cardiovascular health, mental health, longevity, and others. The data we generate will only increase, and businesses need to leverage it accordingly. Spike is built to gather and analyze this type of data securely and effectively.

About Spike

Spike Technologies, Inc., is a B2B data technology and artificial intelligence (AI) startup founded in 2022 and headquartered in Vilnius, Lithuania and Palo Alto, California. Spike provides API aggregation, data pipeline management (ETL), and an AI-backed solution to empower health data from wearables and IoT devices. Spike services clients in the digital health, professional fitness, insurance, and automation sectors.

To learn more about Spike, please visit https://spikeapi.com/.

NB-IoT (Narrowband Internet of Things) is a low-power technology that is designed for Internet of Things (IoT) applications and other low-data rate communication requirements. It uses narrowband radio frequency spectrum and advanced power management techniques to enable efficient use of the available spectrum and extend the battery life of IoT devices. NB-IoT is based on the LTE (Long-Term Evolution) cellular wireless technology and has been standardized by the 3rd Generation Partnership Project (3GPP) as a global wireless communication standard for IoT applications.

What is NB-IoT?

NB-IoT is a low-power wide-area network (LPWAN) technology that is designed for Internet of Things (IoT) devices and other applications that require a low data rate and long battery life. It is a type of cellular network technology that uses a narrowband radio frequency spectrum to provide secure and reliable communication for IoT devices.

NB-IoT operates in the licensed spectrum and uses advanced modulation and multiple access techniques to enable efficient use of the available spectrum and support a large number of connected devices. It also uses advanced power management techniques to extend the battery life of IoT devices, which is critical for applications such as remote sensors and other devices that are not easily accessible for maintenance or battery replacement.

In addition to its low power and long-range capabilities, Narrowband Internet of Things also offers improved security and reliability compared to other IoT technologies. It uses a dedicated network infrastructure and robust signaling mechanisms to provide reliable communication, even in challenging environments such as dense urban areas or deep underground.

Overall, NB-IoT is a key technology for enabling the IoT and supporting the growing number of connected devices that are being deployed in a wide range of industries, from agriculture and manufacturing to smart cities and healthcare.

Is NB-IoT secure?

Because the underlying technology is less complex than conventional cellular modules, OEMs can design, produce, and deploy their products more easily.

The same tried-and-true security and privacy protections of LTE mobile networks are also available, including support for;

• user identity confidentiality, 
• entity authentication, 
• data integrity, 
• and mobile device identification.

NB-IoT use case requirements

NB-IoT addresses the needs of many IoT use cases because it:

• Efficiency: Half-duplex communications are used by NB-IoT, which means that neither the module nor the cellular base station can simultaneously transfer data. This usage of half-duplex communications, along with NB-slower IoT’s data rates, use of a single antenna, and lower Radio Frequency (RF) bandwidth, minimize the complexity and, consequently, the cost of NB-IoT devices. When compared to standard LTE Cat-1 cellular modules, these simplifications allow NB-IoT modules to cost as much as 50% less.
• Power consumption: Thanks to functions like Power Savings Mode (PSM) and eDRX (Extended Discontinuous Reception), as well as NB-capacity IoT’s to optimize the amount of energy used for small data transmissions, battery-powered edge modules, it can transmit data with up to 75% less power than conventional LTE Cat-1 modules. As a result, manufacturers of IoT applications can create gadgets with a battery life of ten years or longer.
• More capacity: Up to one million NB-IoT devices can connect to the network per square kilometer because of the utilization of narrowband transmission, signaling optimization, adaptive modulation, and hybrid automated repeat request (HARQ).
• Better coverage: Narrowband Internet of Things makes use of a lot of signal repetition. Large signal repetition enhances NB-coverage IoT’s by 5–10X over competing cellular technologies. However, it reduces data throughput and increases power consumption. NB-IoT devices can now connect to cellular networks even if they are underground, far within a building, or in a rural area because of this improved coverage.

How much does NB-IoT cost?

The cost of Narrowband Internet of Things can vary depending on a number of factors, including the specific implementation, the type of equipment and infrastructure used, and the scale of deployment. As such, it is difficult to provide a precise cost estimate for NB-IoT.

The cost of implementing NB-IoT can include the cost of acquiring and installing the necessary equipment and infrastructure, such as base stations, antennas, and other hardware. It can also include the cost of operating and maintaining the network, as well as the cost of purchasing or leasing the necessary spectrum.

In addition, the cost of NB-IoT can also depend on the type of deployments, such as a standalone NB-IoT network or a network that is integrated with an existing cellular network. The cost can also vary depending on the scale of deployment, with larger deployments typically requiring more equipment and infrastructure.

Overall, the cost of implementing NB-IoT will depend on a range of factors and can vary depending on the specific implementation and requirements.

Is NB-IoT bidirectional?

Yes, NB-IoT is a bidirectional communication technology, which means it supports both uplink and downlink communication.

In bidirectional communication, data can be transmitted from the device to the network (uplink) and from the network to the device (downlink), allowing for two-way communication. This is in contrast to unidirectional communication, where data can only be transmitted in one direction (either from the device to the network or from the network to the device).

Rounding up: The importance of having the optimal IoT connectivity

Narrowband Internet of Things uses advanced multiple access techniques, such as time-division multiple access (TDMA) and frequency-division multiple access (FDMA), to enable efficient use of the available spectrum and support bidirectional communication. This allows IoT devices to send data to the network and receive data from the network, enabling a wide range of applications such as remote sensor monitoring, asset tracking, and other IoT use cases.

Overall, the bidirectional communication capabilities of NB-IoT are an important part of its design and enable a wide range of IoT applications.

NB-IoT and Massive IoT

In practice, you will frequently hear comparisons between NB-IoT and LTE-M, even though these are the two options that the mobile industry ecosystem of manufacturers, service providers, and network operators are advocating.

Suppose you’ve never heard of LPWAN or someone else described it as new. In that case, it’s not because it falls under a brand-new category of wireless IoT communications, but rather because cellular LPWA networks have finally been deployed in many regions after a long delay caused primarily by various competing technologies that were being proposed and the business decisions made by the mobile industry.

Massive IoT, another industry term that simply refers to those “billions” of devices that typically transmit small data volumes, frequently sent at intervals (so no constant transmission but now and then), and with the need for long battery lives as with NB-IoT and others, is also sometimes used to classify LPWAN.

Before discussing specs, use cases, innovations, and other topics, a few words about standards and standardization might be helpful if you’re interested in understanding certain specifics. The mobile (cellular) industry’s standardization body, 3GPP (3rd Generation Partnership Project), uses numbered “releases” in which the details of new standards and improvements are set at a certain point in time. These updates include a number of mobile technologies that members of the mobile industry have developed.

Is NB-IoT 5G or 4G?

Narrowband Internet of Things is not considered as a 4G or 5G technology. It is a low-power wide-area network (LPWAN) technology that is designed specifically for Internet of Things (IoT) applications and other low-data rate communication requirements.

4G and 5G are cellular network technologies that are used to provide high-speed mobile broadband and other advanced communication services to mobile devices such as smartphones and tablets. These technologies use a wideband radio frequency spectrum and advanced modulation, and multiple access techniques to support high data rates and support a large number of connected devices.

In contrast, NB-IoT is designed to support a low data rate and long battery life for IoT devices and other applications that do not require the high data rates and capabilities of 4G or 5G. It uses a narrowband radio frequency spectrum and advanced power management techniques to enable efficient use of the available spectrum and extend the battery life of IoT devices.

Overall, while 4G and 5G are focused on providing high-speed mobile broadband services, NB-IoT is designed to support a wide range of IoT applications and enable the growth of the IoT.

What is the difference between NB-IoT and LTE?

NB-IoT (Narrowband Internet of Things) and LTE (Long-Term Evolution) are both wireless communication technologies, but they are designed for different purposes and have some key differences.

Narrowband Internet of Things is a low-power wide-area network (LPWAN) technology that is designed specifically for Internet of Things (IoT) applications and other low-data rate communication requirements. It uses a narrowband radio frequency spectrum and advanced power management techniques to enable efficient use of the available spectrum and extend the battery life of IoT devices.

IoT sensors smarten everyday objects with awareness and cognition

In contrast, LTE is a cellular network technology that is used to provide high-speed mobile broadband and other advanced communication services to mobile devices such as smartphones and tablets. It uses a wideband radio frequency spectrum and advanced modulation, and multiple access techniques to support high data rates and support a large number of connected devices.

Some key differences between NB-IoT and LTE include:

• Data rates: NB-IoT is designed for low data rate applications and can support data rates of up to 200 kbps, while LTE can support data rates of up to several hundred Mbps.
• Spectrum: NB-IoT operates in the licensed spectrum, while LTE can operate in both licensed and unlicensed spectrum.
• Range: NB-IoT has a longer range than LTE due to its use of a narrowband spectrum and advanced power management techniques.
• Power consumption: NB-IoT is designed for low power consumption and long battery life, while LTE devices typically have higher power requirements.

Overall, while LTE is focused on providing high-speed mobile broadband services, NB-IoT is designed to support a wide range of IoT applications and enable the growth of the IoT.

The latency of NB-IoT

In comparison to LTE-M, Narrowband Internet of Things is not as well suited for situations that require very low network latency. Because of this, it is not as commonly used in applications that need near-real-time data. In these cases, LTE-M is a better fit. Both NB-IoT and LTE-M play a role in the development of 5G, where extremely low network latency is necessary for applications that require speed and are typically critical. The choice of communication standard is not the only factor to consider here.

In addition to the differences between NB-IoT and LTE-M in terms of network latency, it is worth noting that edge computing and IoT can also play a role in analyzing sensor data quickly. Edge computing allows for data analysis to take place closer to the source without the need for communication with a cloud or data center.

As for the latency of NB-IoT specifically, it is typically equal to or less than 10 seconds, with a range of 1.6 to 10 seconds. In comparison, LTE-M has delays of 100 to 150 ms.

The mobility of NB-IoT

One key difference between NB-IoT and LTE-M is that NB-IoT does not fully support mobility, whereas LTE-M does. This means that Narrowband Internet of Things may not be as effective in situations where handover between cells is necessary. However, there have been improvements to this in 3GPP Release 14, which has made some enhancements to the characteristics of NB-IoT. In contrast, LTE-M also supports voice.

Despite the limitations of NB-IoT in terms of mobility, it is still widely used in applications and cases involving fixed assets and devices. This is evident in the types of applications and uses cases mentioned earlier. It is important to note that this does not mean that NB-IoT cannot be used for mobile assets and devices but rather that its capabilities in this area are limited.

There are real-world NB-IoT applications with trackers, shared bicycle services, environmental applications with a moving component but low data throughput, smart logistics, and more.

More specifically, Narrowband Internet of Things requires the device to periodically reselect the cell while in motion, whereas LTE-M does not. So it’s far less appropriate for mobile (and the reselection of cells has an impact on battery life as it consumes power). Fixed assets, such as smart meters and point-of-sale terminals, are typically the focus of NB-IoT, although they are not the only ones. For “real seamless mobility,” LTE-M can be counted as the preferred technology.

NB-IoT vs LoRa

NB-IoT (Narrowband Internet of Things) and LoRa (Long Range) are both low-power wide-area network (LPWAN) technologies that are designed for Internet of Things (IoT) applications and other low data rate communication requirements. They both use a narrowband radio frequency spectrum and advanced power management techniques to enable efficient use of the available spectrum and extend the battery life of IoT devices.

There are some key differences between NB-IoT and LoRa, including:

• Spectrum: Narrowband Internet of Things operates in the licensed spectrum, while LoRa can operate in both licensed and unlicensed spectra.
• Data rates: NB-IoT can support data rates of up to 200 kbps, while LoRa can support data rates of up to 50 kbps.
• Range: LoRa has a longer range than Narrowband Internet of Things due to its use of a proprietary spread-spectrum modulation technique.
• Power consumption: NB-IoT is designed for low power consumption and long battery life, while LoRa devices typically have higher power requirements.
• Network infrastructure: Narrowband Internet of Things uses a dedicated network infrastructure, while LoRa uses a distributed network architecture with decentralized control.

Overall, while both NB-IoT and LoRa are designed for low-data rate IoT applications, they have some key differences in their technical implementation and capabilities.

The future NB-IoT applications

For the widespread deployment of sensors, affordable modems are essential. It is necessary to improve the procedures for monitoring and to report various variables, such as temperature and humidity. Data rate and latency should be decreased for applications that involve a lot of sensors. The claim that NB-IoT would increase efficiency is supported by the fact that this solution can meet these standards. With single-tone devices, Narrowband Internet of Things devices have shown they can handle peak physical layer data rates as low as 100–200kbps or even much lower.

We can also take a look at other aspects of device optimization. For instance, LTE MBB requires two antennas, whereas NB-IoT devices just require a single receiver antenna. Therefore, there is just one receiver chain required for the ratio and baseband demodulator.

IIoT and edge computing are gaining traction in many industries

One benefit of narrow bandwidth is the difficulty of analog-to-digital and digital-to-analog conversion, channel estimate, and lower buffering (200kHz from NB-IoT vs. 1.4MHZ to 20MHZ from other technologies).

Agriculture

Farmers will have advanced tracking options thanks to Narrowband Internet of Things connectivity, so a sensor with a u-blox NB-IoT module can send an alert if an animal’s movement is unusual. These sensors could be used to track environmental characteristics, including pollution, noise, rain, and soil characteristics like temperature and humidity.

Smart metering

Gas and water meter monitoring can be accomplished with NB-IoT using frequent, tiny data transmissions. Rollouts of smart meters face significant network coverage challenges. Meters frequently appear in challenging locations, such as cellars, subterranean tunnels, or isolated rural areas. To solve this problem, NB-IoT has outstanding coverage and penetration.

Smart cities

Local government can use Narrowband Internet of Things to manage street lights, decide when trash cans need to be emptied, locate open parking spaces, keep an eye on the weather, and assess the state of the roads.

Smart apartments

Sensors with NB-IoT connectivity can manage lighting and temperature and transmit notifications about building maintenance problems. Additionally, NB-IoT can serve as the building’s backup broadband connection. In some security solutions, sensors may even be directly connected to the monitoring system using LPWA networks, as this configuration is both simpler to install and maintain and more difficult for an intruder to disable.

Consumers

Wearable technology will receive long-range connectivity through NB-IoT, which is especially useful for tracking people and animals. Similar to this, NB-IoT can be used to track the health of people with age- or chronic-related diseases.

Nowadays we can connect everyday objects to the internet, which is only possible thanks to IoT sensors. Last month we talked about “the veins of IoT devices,” but today, we are here to discuss “the brain of IoT devices,” which are IoT sensors, because no human-made device can work without a perception mechanism.

Nowadays, practically every entity is connected to the network to collect data and use the information for various purposes, including homes, offices, factories, and even cities. Data is one of the most valuable assets nowadays; many experts refer to it as “the new gold.”

When using the Internet of Things to build solutions, sensors are crucial. Sensors are tools that gather information from the environment and replace it with signals that both humans and machines can recognize.

The usage of IoT sensors has expanded to include a wide range of industries, including healthcare, nursing care, industrial, logistics, transportation, agriculture, emergency planning, tourism, local businesses, and many more.

What are IoT sensors?

IoT sensors are linked devices that capture real-time data, translate it, and format it so that other instruments may use it. The nature of the sensor is that it is susceptible to outside stimuli, such as temperature.

Contrary to popular belief, IoT sensors are not recent inventions. Similar technologies have been created in the past specifically to gather and transform analog data into digital data assets. It’s fascinating, right?

For instance, radar creates and transmits radio waves while listening for reflections that, if detected, are sent to an electronic device for processing. Did you know that “radar” stands for radio direction and range? 

To put it briefly, the development of the Internet of Things has made it possible to connect common objects to the internet. Everything seems to utilize IoT technology to collect and analyze data, from houses and offices to manufacturing facilities and smart cities.

Wireless IoT sensors

Wireless IoT sensors collect information about the environment and transmit findings to more potent platforms or components for additional processing. Typically, sensors are dispersed over a wide geographic region and are configured to connect with centralized hubs, gateways, and servers.

One significant benefit of wireless IoT sensors is that they require less maintenance and power. Before requiring a battery replacement or recharge, sensors can run IoT applications for years.

Organizing wireless sensors in the field is one of the most challenging issues developers confront when creating wireless networks. The distribution of IoT sensors, or “nodes,” must support the ultimate goal of the network creator.

How do IoT sensors work?

The Internet of Things (IoT) ecosystem comprises web-enabled smart devices that use embedded systems, such as processors, sensors, and communication devices, to gather, send, and act on the data they get from their surroundings.

By connecting to an IoT gateway or other edge device, which either sends data to the cloud for analysis or analyzes it locally, IoT devices exchange the sensor data they collect. These gadgets occasionally converse with other similar devices, acting on the data they exchange.

Although individuals can engage with the devices to set them up, give them instructions, or retrieve the data, the hardware accomplishes the majority of the job without their help. Without further ado, we would like to introduce you to different types of IoT sensors:

Types of IoT sensors

There are plenty of different IoT sensors for specific purposes. Below we will explain each and every one of them in detail.

IoT sensors list:

• Temperature sensors
• Proximity sensors
• Pressure sensors
• Water quality sensors
• Smoke sensors
• Chemical sensors
• Gas sensors
• Image sensors
• IR sensors
• Level sensors
• Motion detection sensors
• Optical sensors
• Acceleration sensors
• Gyroscopic sensors
• Humidity sensors

Temperature sensors

A temperature sensor is, by definition, a device used to evaluate the amount of heat energy that enables the user to detect a physical change in temperature from a specific source and transforms the data for a device or user.

The hyperconnectivity era: Why should organizations adopt network as a service model?

These sensors have been used in many devices for a very long time. However, as the Internet of Things (IoT) has grown, they have more opportunities to be present in more gadgets.

These IoT sensors were mostly utilized for controlling air conditioning, refrigerators, and other environmental control devices just a few years ago. But as the Internet of Things (IoT) has developed, temperature sensors have discovered their place in the manufacturing, agricultural, and healthcare sectors.

Numerous machines in the production process need certain environmental and device temperatures. This evaluation enables the production process to be continually at its best.

Contrarily, in agriculture, the soil temperature is important for crop growth by increasing the output, this aids in plant production.

Proximity sensors

A proximity sensor is a non-contact device that detects the presence of an object when it enters its field of view, also known as the “target.” Depending on the type of proximity sensor, the sensor may detect a target via sound, light, infrared radiation (IR), or electromagnetic fields. Phones, recycling facilities, self-driving cars, anti-aircraft systems, and production lines all use proximity sensors. There are numerous varieties of proximity sensors, and they all detect targets differently. The inductive proximity sensor and the capacitive proximity sensor are the two types of proximity sensors most frequently utilized.

Pressure sensors

A pressure sensor is a device that detects pressure and turns it into an electric signal. Here, the amount is influenced by the amount of pressure used.

Several gadgets depend on liquid pressure or other types of pressure. Systems that monitor pressure-driven systems and devices can be built using these IoT sensors. The device alerts the system administrator to any deviations from the normal pressure range and any issues that need to be corrected.

Due to their ease of use in spotting pressure changes, these sensors are deployed in production and in the maintenance of complete water and heating systems.

Water quality sensors

In water distribution systems, water quality sensors are generally used to detect water quality and monitor ions.

These IoT sensors are crucial because they keep track of the water’s purity for various uses. They are employed throughout numerous industries.

Smoke sensors

A smoke sensor is a tool that detects smoke (airborne gases and particles) and its level.

They have been utilized for a very long time. They are now even more effective thanks to the Internet of Things, as they are connected to a system that instantly alerts the user to any issues that arise across several businesses.

Smoke sensors are widely utilized by the manufacturing sector, HVAC, construction, and hospitality infrastructure to detect fire and gas incidents. Since the entire system is significantly more effective than the older ones, it protects those who operate in hazardous environments.

Chemical sensors

Numerous industries use chemical sensors. Their objective is to detect liquid or chemical changes in the air. In larger cities, where it is required to monitor changes and protect the populace, they play a significant role.

Chemical sensors are mostly used in industrial environments monitoring and process control, explosive and radioactive detection, and recycling operations on space stations, pharmaceutical industries, and laboratories, among other applications.

Gas sensors

Similar to chemical sensors, gas sensors are used to monitor changes in air quality and identify the presence of different gases. Similar to chemical sensors, they are employed in a variety of sectors, including manufacturing, agriculture, and health to monitor air quality, identify toxic or combustible gases, monitor hazardous gases in coal mines, the oil and gas industry, conduct chemical laboratory research, and manufacture goods such as paints, plastics, rubber, pharmaceuticals, and petrochemicals.

Image sensors

Image sensors are devices that turn optical images into electronic signals that can be displayed or stored electronically.

Digital cameras and modules, medical imaging and night vision equipment, thermal imaging devices, radar, sonar, media houses, biometric & IRIS devices, and other items all make extensive use of IoT sensors.

IR sensors

An infrared sensor is a sensor that detects or emits infrared radiation to sense specific aspects of its environment. It can also measure the heat that the objects are emitting.

They are currently being used in many IoT projects, particularly in healthcare, because they make monitoring blood flow and pressure easier. They are also utilized in a vast assortment of common smart products, including smartphones and smartwatches.

Home appliances and remote controls, breath analysis, infrared vision (used to see heat leaks in electronics, monitor blood flow, and allow art historians to see beneath layers of paint), wearable electronics, optical communication, non-contact-based temperature measurements, and automotive blind-angle detection are some other common uses.

Their uses don’t stop there; they are also an excellent instrument for assuring high levels of security in your house. Additionally, because they may find a range of chemicals and heat leaks, their application covers environment assessments. They have a variety of uses; thus, they will play a significant part in the smart home sector.

Level sensors

Level sensors are a type of sensor that measure the amount or level of fluids, liquids, or other substances flowing through an open or closed system.

Level sensors are utilized in a variety of industries, just like IR sensors. They are employed by companies that deal with liquid materials and are primarily renowned for measuring fuel levels. For instance, these sensors are used by the recycling sector, the juice and alcohol industries, and others to count their liquid assets.

Rounding up: The importance of having the optimal IoT connectivity

The best applications for level sensors include measuring fuel and liquid levels in open or closed containers, monitoring sea level and tsunami warnings, water reservoirs, medical devices, compressors, hydraulic reservoirs, machine tools, processing of beverages and pharmaceuticals, and high- and low-level detection, among others.

As a result, their operations are more efficiently streamlined. Sensors constantly gather all the relevant data. Any product manager may monitor exactly how much liquid is available for distribution and whether production needs to be increased with the help of these IoT sensors.

Motion detection sensors

A motion detector is an electrical device that detects physical movement (motion) in a specific region and converts that motion into an electric signal. It may detect the movement of any object or person.

In the security sector, motion detection is crucial. Businesses use these IoT sensors in locations where no movement should be noticed, and it is simple to detect anyone’s presence when these sensors are deployed.

Intrusion detection systems, automatic door controls, boom barriers, smart cameras (i.e., motion-based capture/video recording), toll plazas, automatic parking systems, automated sinks/toilet flushers, hand dryers, energy management systems (i.e. Automated Lighting, AC, Fan, Appliances Control), etc. are some of the applications for which they are primarily used.

In other businesses, however, where a client can interact with the system by waving a hand or making a similar motion, these sensors can also recognize various actions, making them very useful for different scenarios. For instance, a customer can wave to a sensor in a store to ask for help choosing the best item to buy.

Although the security business is their main application, there are a growing number of other uses for these sensors as technology develops.

Optical sensors

Optical sensors provide an electrical signal in response to light that is reflected off of an object, which can then be used to detect or measure a state. These IoT sensors detect the interruption or reflection of a light beam brought on by the presence of an object. Among the several optical sensor types are:

• Through-beam sensors use the interruption of a light beam as an object moves in front of a transmitter and remote receiver to detect items.
• Retro-reflective sensors combine the transmitter and receiver into a single unit and reflect the light back to the device using a different reflective surface.
• Diffuse reflection sensors use the detected object as the reflective surface in contrast to retro-reflective sensors.

Acceleration sensors

Devices that measure acceleration brought on by movement, vibration, collision, etc., are known as linear acceleration sensors, sometimes known as G-force sensors. Newton’s second law of motion is applied to a spring-mass system in order for all acceleration sensors to work. The base of the acceleration sensor is attached to a mass by an identical spring.

A measurement of the relative position of the mass or force on the spring as it changes over time can be used to calculate the acceleration because the force between the mass and base is proportional to the acceleration of the mass and their distance from one another is linearly related to the force caused by the spring. Piezoelectric, piezoresistive, variable capacitance, and variable reluctance are the four most prevalent acceleration sensors.

Gyroscopic sensors

Gyroscopes or gyroscopic sensors are used to track the rotation of an item and calculate its angular velocity using a three-axis system. With the use of these IoT sensors, it is possible to detect an object’s orientation without actually seeing it.

Humidity sensors

Relative humidity, a measurement of the amount of water vapor in the air or other gases, can be found using humidity sensors. In order to manufacture materials, it is essential to regulate environmental factors, and humidity sensors enable measurements and corrections to be performed to prevent rising or dropping levels. HVAC systems are a common application to maintain desired comfort levels.

IIoT and edge computing are gaining traction in many industries

Friend or foe: Is the future looking bright for IoT devices?

IoT is growing in many contexts, with competing technologies fighting for dominance. This will lead to issues while connecting devices and require the installation of extra hardware and software.

Fragmented cloud services, an absence of standardized M2M protocols, and variations in IoT device operating systems and firmware bring other compatibility issues.

In the upcoming years, several of these technologies will be rendered obsolete. This is important because IoT gadgets, such as smart TVs and refrigerators, will survive far longer than traditional computing devices and should continue functioning even if their manufacturer goes out of business.

Conclusion

There is no doubt that sensors play a significant role in both our personal and professional lives. IoT sensors are already widely used in many industries to address social needs.

IoT sensors are crucial components of our future workstyle improvements since they use technology that does not require humanitarian aid.

And as the Internet of Things becomes more prevalent in almost everything we do, the sensors will only become more prevalent in terms of availability and production throughout the years to come.

Due to the fragmented nature of IoT deployments, organizations can select from a wide range of IoT connectivity standards. IoT enables the creation of new business models, and product offers as well as digital transformation. Billions of IoT devices can be connected by expanding on current cellular networks, which will help you remain relevant to your customers, generate new revenue streams, and provide your clients with a competitive edge.

Although IoT is becoming widely used and connected devices are increasing quickly, the market environment is still fragmented. In order to provide the network performance required for a wide range of evolving IoT use cases, applications, and device types, connectivity for new business models must be flexible and agile.

What is IoT connectivity?

The methods used to link IoT devices—methods such as applications, sensors, trackers, gateways, and network routers—are often referred to as IoT connectivity. The phrase “IoT connectivity” is also frequently used in the IoT sector to refer to the specific IoT network solutions that can support this form of connectivity. These include, but are not limited to, WiFi, cellular, and LPWAN.

We frequently break down IoT connectivity options into three factors when determining which one is best for you or your company: bandwidth capacity (speed), coverage area, and power consumption. Finding a connectivity option for an IoT connection that prioritizes all three elements can be challenging. Therefore it’s crucial to know your connectivity needs before choosing an IoT connectivity option.

The era of IoT devices

The Internet of Things has made it possible for the physical and digital worlds to interact and collaborate. Enabling firms to automate and streamline their regular processes, it provides them with a host of advantages.

The workplace is changing due to technology. Our daily lives already contain billions of devices. They converse with one another. They communicate with corporate data streams. Additionally, they are increasingly shaping and controlling the environment in real-time to deliver unthinkable outcomes only a few years ago.

IoT gateway: The veins of connected devices

The Internet of Things (IoT), which involves objects communicating with one another either directly or through the cloud, is quickly developing. In addition, data streams are becoming less exclusive and more open, while sensor technology is becoming smaller, more effective, and more affordable. These developments in software, data mining, machine learning, and artificial intelligence (AI) promise to deepen our understanding of the relationship between the facility and organizational performance as well as that between the facility and human performance.

Building an IoT ecosystem

In order to shape, manage, and commercialize a mega-digital platform like IoT, digital ecosystems made up of numerous companies and technologies are required. Macro trends like IoT are not only driven by a single company or invention. IoT ecosystems and platform providers have a very synergistic connection, with new opportunities being created as a result of the overall benefits to all parties.

The massive amounts of data generated by connected devices, which are expected to number 13.7 billion by 2021, are already leaving an enduring imprint on the digital landscape and driving up demand for platforms like 5G high-speed mobile networks and real-time data management and analytics in the cloud.

A network of businesses known as an IoT ecosystem powers the development and provision of IoT goods and services. The interdependencies between IoT ecosystem players are becoming more crucial and complex as IoT systems become more complex and specialized.

The advantages of being a part of a robust IoT ecosystem are numerous:

• Collaboration enables a business to set itself apart from rivals by utilizing the skills and assets of its partners. Selling into brand-new verticals that would not otherwise be possible, for instance.
• Third-party offerings can address product portfolio gaps, enhancing a company’s ability to launch IoT systems more quickly, mitigate risk, and lower upfront Capex by spreading the investment across various market participants.
• Because each partner brings a unique set of consumer ties to the table, partnerships frequently boost market reach and adoption rate. Making the proper alliances also frees up time and funds that can be used to advance IoT innovation and value generation.

Enterprises frequently seek a full end-to-end IoT solution, necessitating the involvement and integration of technologies and/or services from numerous industry participants. To cut down on IT overhead while deploying new IoT systems, many major IoT deployments can entail interactions between more than a dozen actors across one or perhaps several ecosystems. It is significantly simpler for a business to adopt and operate comprehensive IoT systems for a stronger competitive edge when these partner ecosystems are strategically established.

Data processing in IoT

Although the data processing cycle begins in the input stage, we should first consider the desired result. What queries, in other words, ought to be addressed with the aid of IoT? What kind of data are we looking for?

One example of a use case is receiving an alert whenever the temperature of a manufacturing machine exceeds a threshold limit.

Once we are certain of the desired outcome, we must figure out how to get there. In order to be transformed into the information we need, the data gathered by the sensor devices need to be stored in a suitable format.

As an illustration, when a machine is operating, we could periodically receive data (for instance, every 10 minutes). We might wish to utilize that information to determine how many hours have passed since the last service on the machine. By seeing trends in that data, we could predict when a certain number of hours will be reached if consumption stays constant.

We should spend money on a scalable cloud service to be able to store the data due to the possibly enormous amount of data that our sensors capture. Having said that, we must also create a data retention strategy and acknowledge that it is illogical to retain all IoT data indefinitely. The cost of storing data increases as we accumulate more of it and hold it for longer. On the other hand, less data equals fewer historical allusions and insights. As a result, we must set priorities and strike a balance between the amount of data we want to store and our budget.

Before implementing data processing, it’s crucial to determine a suitable balance between resource use and update frequency (e.g., calculation capacity, power). The IoT use case dictates exactly what constitutes a “good balance.”

The history of data processing technology

In certain use scenarios, it’s crucial to understand immediately how the result is affected by the data gathered. Real-time data processing in IoT is necessary for this. However, it can be quite resource-intensive. In certain other use cases, processing the gathered data just once a day, for instance, is sufficient.

So far, we have employed sensor devices to gather the data, a network solution to transfer the data to a cloud service, and a data transformation process to turn the data into usable information. It’s now time to show the findings to the user.

IoT connectivity examples

The type of IoT connectivity a company makes will determine whether a new IoT project succeeds or fails. When an organization needs an IoT connectivity mechanism, factors like battery life, network coverage, and cost all come into play.

IoT connectivity technologies

Without connectivity, the Internet of Things (IoT) would not be possible. A device within an IoT ecosystem will only function if it is connected to other devices and IoT technologies, similar to how a sink is connected to a water line or a lamp plugged into an outlet. Fortunately, there are a variety of IoT connectivity technologies available. Understanding your needs for bandwidth, range, and power consumption will help you choose the best one.

Let’s explore the most popular IoT connectivity technologies and application cases.

Fixed connection with Ethernet

Ethernet connections were one of the earliest techniques businesses used to connect an IoT device to a network. They remain a good option for hefty devices that don’t need to move from a fixed place. The quantity of cabling used in a wired deployment can be reduced by using Power over Ethernet, a technique that transports electrical currents through data cables rather than power cords.

LPWAN’s ability to support lots of sensors

In the Internet of Things, low-power WAN IoT protocols are used to wirelessly transmit small quantities of data from specialized base stations to sensors and devices.

The LPWAN technologies LoRa, short for long range and supported by chipmaker Semtech and Sigfox, are most frequently used by businesses. Both LoRa and Sigfox support their bidirectional communications using unlicensed industrial, scientific, and medical bands, which include the 868 MHz band in Europe, the 915 MHz spectrum in North America, and the 433 MHz frequency in Asia.

In urban deployments, both LoRa and Sigfox offer a realistic range of about 10 kilometers (km), and in open rural areas with fewer skyscrapers, their range is more than twice that.

IIoT and edge computing are gaining traction in many industries

Both Sigfox and Semtech provide gateways to make the deployment of their own technologies as private networks simple and affordable. These LPWAN technologies have received a lot of attention for usage in IoT deployments in commercial buildings and industrial sites that house thousands of sensors and other IoT devices.

In terms of LPWAN technologies, LoRa is in the lead, partly because a startup named Helium has launched a DIY IoT network based on the LoRaWAN technology across North America and Europe.

IoT cellular connectivity

IoT cellular connectivity has gained significant traction globally, with 2G and 3G enabling several pioneering IoT applications. With 4G services, more devices per cell may enable more bandwidth, lower latency, and improved device density. The introduction of 5G networks, initially made possible by the 5G New Radio (NR) standard, will further improve these by enabling Ultra-Reliable Low Latency Communications (URLLC), which will support more and more crucial applications.

Therefore, cellular IoT is able to fulfill both the comparatively straightforward needs of the Massive IoT market and the highly unique, delicate requirements of complicated surroundings and applications.

In addition to mobile network operators, the cellular IoT ecosystem is quickly growing and is supported by an increasing number of device, chipset, module, and network infrastructure vendors. It is founded on world 3GPP standards. It performs better than other Low Power Wide Area (LPWA) network technologies in terms of unparalleled global coverage, Quality of Service, scalability, security, and the flexibility to handle the different requirements for a wide range of use cases.

Satellite makes it possible to access inaccessible zones

For IoT devices, satellites provide genuinely omnipresent coverage since they may connect to devices with little to no ground-based IoT connectivity. A satellite link is required if a company needs IoT coverage in the middle of the ocean. IoT devices can already be connected globally using geostationary satellites, which orbit 23,000 miles above the globe. As businesses like SpaceX begin to launch enormous constellations of mini-satellites that are aimed at the IoT sector, low earth orbit satellites are likewise becoming more and more popular.

WiFi for enterprises

WiFi serves as a connection method for Internet of Things (IoT) devices such as sensors, security cameras, and IoT units for homes and businesses.

One of the most widely used wireless networking choices worldwide is WiFi. Real-world ranges in the unlicensed 2.4 GHz and 5 GHz bands peak at about 410 feet from the access point. 2.4 GHz connections are capable of 150 Mbps data rates and have improved throughput through solid things like walls. For IoT devices in offices or other structures, businesses would utilize 2.4 GHz. Data speeds of about 1 Gbps can be supported via 5 GHz links. The WiFi signal’s range is reduced by around 50% unless the company strengthens the signal on the 5 GHz band.

For the 2.4 GHz or 5 GHz bands, the typical WiFi battery life is eight to nine hours. Office computers and smartphones can use that battery lifespan, but sensors and Internet of Things (IoT) devices need weeks, months, or even years of battery life.

IoT connectivity protocols

When constructing a network to support an IoT ecosystem, technology professionals have a variety of communication protocols to choose from. The following are the most typical.

Bluetooth and BLE

Short-wavelength, ultrahigh-frequency radio waves are used in the short-range wireless technology known as Bluetooth. Although it was initially primarily used for audio streaming, it has since evolved into a crucial enabler of wireless and linked devices. As a result, both IoT installations and personal area networks frequently use this low-power, short-range IoT connectivity option.

The new form of Bluetooth that is best for Internet of Things connections is Bluetooth Low Energy, also referred to as Bluetooth LE or BLE. True to its name, BLE uses less power than standard Bluetooth, making it especially appealing in many use cases, including smart home and health and fitness trackers for consumers and in-store navigation for businesses.

LoRa and LoRaWAN

Long-range communication capabilities are provided via the noncellular wireless technology known as LoRa, also known as long-range. For M2M and Internet of Things deployments, it has low power consumption and secure data transmission. It is currently a part of Semtech’s radio frequency platform and is a proprietary technology. Semtech was a founding member of the LoRa Alliance, which today oversees LoRa technology. Additionally, the LoRa Alliance created and currently manages LoRaWAN, an open cloud-based protocol that permits LoRa communication between IoT devices.

WiFi

WiFi is a popular IoT protocol due to its widespread use in residential, commercial, and industrial facilities. It can analyze massive volumes of data and allows quick data transfer. With short- to medium-range distances, LAN situations are particularly well-suited for WiFi. In addition, WiFi has a number of standards, the most popular of which is 802.11n, which gives technicians different alternatives for deployment.

For various IoT use cases, particularly those involving low-power/battery-powered devices, many WiFi standards, including the one frequently used in houses, consume too much power. Because of this, WiFi is not an option for all deployments. WiFi’s low range and poor scalability are additional factors that make it impractical for use in many IoT deployments.

Cellular

Cellular is one of the most popular and commonly used solutions for IoT applications. It is also one of the finest deployment options where communications span greater distances. The 2G and 3G legacy cellular standards are currently being phased out, but 4G/LTE and 5G, which are newer high-speed standards, are quickly expanding their coverage areas thanks to telecommunications companies. High bandwidth and dependable communication are provided by cellular. It has the ability to send large amounts of data, which is crucial for many IoT deployments. However, these features are more expensive and use more energy than alternative options.

CoAP

Constrained Application Protocol, or CoAP, was introduced in 2013 by the Internet Engineering Task Force Constrained RESTful Environments Working Group after it was developed to function with HTTP-based IoT systems. User Datagram Protocol is used by CoAP to create secure connections and permit data transmission between numerous sites. CoAP enables constrained devices to join an IoT environment, even in the presence of low bandwidth, low availability, and/or low-energy devices, and is frequently used for machine-to-machine (M2M) applications.

AMQP

The abbreviation AMQP stands for Advanced Message Queuing Protocol, an open standard protocol used for middleware that is more message-oriented. As a result, independent of the message brokers or platforms being utilized, it facilitates messaging compatibility between systems. Even over distances or via subpar networks, it provides stability, security, and interoperability. Even when systems are not simultaneously available, it facilitates communications.

LWM2M

Lightweight M2M (LWM2M), according to OMA SpecWorks, is “a device management protocol designed for sensor networks and the demands of an M2M environment.” This communication protocol is a viable choice for low-power devices with constrained processing and storage capabilities because it was created expressly for remote device management and telemetry in IoT contexts and other M2M applications.

MQTT

Originally created in 1999 and called Message Queuing Telemetry Transport, it is now simply known as MQTT. This protocol no longer uses message queuing. A publish-subscribe architecture is used by MQTT to support M2M communication. Its straightforward messaging architecture facilitates communication between numerous devices and operates with restricted hardware.

It was created to function in low-bandwidth environments, including those where sensors and mobile devices are connected to shaky networks. Due to its ability to connect small-code devices, it is frequently chosen for wireless networks with varying levels of latency brought on by bandwidth restrictions or erratic connections. The most popular open-source protocol for tying together IoT and industrial IoT devices is MQTT, which originally existed as a proprietary protocol.

DDS

Data Distribution Service was created by the Object Management Group (OMG) for real-time systems. A middleware protocol and API standard for data-centric communication, DDS is described by OMG as “integrating the components of a system together, providing low-latency data connectivity, extreme reliability, and scalable architecture that business and mission-critical IoT applications need. Using a publish-subscribe structure, this M2M standard provides high-performance and highly scalable real-time data communication.

XMPP

Extensible Messaging and Presence Protocol (XMPP), originally developed by the open-source Jabber community in the early 2000s for real-time human-to-human conversation, is currently utilized for M2M communication in lightweight middleware and for routing XML data. XMPP is most frequently used for consumer-focused IoT implementations, such as smart appliances, and facilitates the real-time exchange of structured yet extensible data between numerous entities on a network. The XMPP Standards Foundation supports it as being open source.

Zigbee

One of the most widely used mesh network protocols in IoT contexts, Zigbee, was created for use in building and home automation. Zigbee is a low-power, short-range protocol that can be used to spread communication over numerous devices. Although it has a lesser data rate than BLE, its range is greater than BLE’s. It offers a flexible, self-organizing mesh, ultralow power, and a selection of applications and is governed by the Zigbee Alliance.

Z-Wave

Z-Wave is a wireless mesh network communication protocol based on low-power radio frequency technology, which is another exclusive choice. Z-Wave, like Bluetooth and WiFi, enables encrypted communication between smart devices, adding a layer of security to the IoT deployment. It is frequently utilized in commercial applications, including energy management technology, home automation goods, and security systems. In the United States, it uses a radio frequency of 908.42 MHz, albeit its frequencies differ from country to country. The Z-Wave Alliance, a member consortium dedicated to improving the technology and interoperability of Z-Wave-using devices, supports Z-Wave.

IoT connectivity options

Because there are so many options and they are so varied, the constantly changing IoT connectivity landscape is currently concentrating on meeting the demands of data-intensive environments found in both consumer and industrial Internet of Things applications. The ideal universal IoT connectivity solution would have extremely low power consumption for the devices, be capable of swiftly transmitting enormous amounts of data over great distances, and be offered at costs that would allow smart businesses to continue to be profitable.

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The sad reality is that no current or near-future communication protocol will be able to accommodate all potential smart applications without granting them any compromises in terms of the aforementioned essential IoT connectivity factors, given the inherent heterogeneity of use cases within the Internet of Things.

So, in order to identify the optimum solution for a particular project, it is always necessary to compromise between the three basic connection factors of range, bandwidth, and power consumption. In order to choose the ideal connection network for your smart organization, you must be able to recognize your project’s requirements at every stage of its deployment and have a thorough understanding of your IoT use case’s details. In order to help you understand the trade-offs provided by the most common network technologies, here is a list of the most widely used connectivity solutions used in the Internet of Things.

How to choose the right IoT connectivity option?

The term “IoT connectivity” refers to the interconnection of every component of the IoT ecosystem, including sensors, gateways, routers, platforms, applications, and other systems. It often refers to various network systems classified according to their bandwidth, range, and power requirements. The requirements for IoT projects vary, and many of them use various connectivity methods depending on their requirements.

You can better define your needs, make more informed decisions, and start your smart project on the right foot with the assistance of this succinct overview of IoT connectivity options.

The importance of choosing the right IoT connectivity option

Because there is so much at stake, is the straightforward response? With its limitless potential, the Internet of Things, a massive technology that combines the digital and physical worlds to offer a smarter future, continues to draw market participants and investments. There is a growing need to look for killer market differentiators that would give an extra edge to if only slightly, get ahead of the competing crowd as the competition among commercial and industrial IoT-driven enterprises soars.

While there is always room for improvement in an Internet of Things deployment that involves the fusion of information and operational technology, it sometimes seems like connectivity is taken for granted. Many Internet of Things actors disregards the significance of having proper IoT connectivity due to the presence of widely used wireless technologies like cellular, WiFi, and Bluetooth. This can ultimately result in project failure due to the inability to control the rapidly expanding data deluge.

Therefore, carefully choosing the proper IoT connectivity solution is essential to any IoT project’s success because it lets you to control your smart data streams fully. In the end, connecting your assets through effective and interoperable IoT connectivity solutions may be the differentiating element needed to tip the scales of success in your favor.

IoT connectivity challenges

IoT has had some impact on how people live, interact, and conduct business. Billions of web-enabled devices are transforming the entire planet into a gigantic digital hub. IoT connectivity runs into a number of issues while attempting to create smarter homes, offices, cars, and other operations, including the following.

Scalability

As more devices are connected, the underlying infrastructure must be scalable. According to projections, 35.82 billion IoT devices will be installed globally in 2021 alone. As enterprises add more IoT connectivity devices, such as sensors, gateways, routers, or cameras, IoT growth also unleashes a tsunami of new types of data.

The scaling problem’s multiple facets include cost, complexity, and bandwidth effectiveness. Every second, 127 devices are connecting for the first time to the internet, according to McKinsey Digital. As a result, as the network expands, service providers, network operators, and other digital enablers need to implement an IoT connectivity solution to handle the maintenance and management workload.

Compatibility

As IoT grows, various solutions fight to become the standard, making integration challenging. Diverse operating systems, disjointed cloud services, and a dearth of standardized machine-to-machine (M2M) protocols can all cause compatibility problems.

For sustained compatibility, users must maintain their devices updated and patched. Performance problems can arise, for instance, when two IoT devices connect with each other and have various software versions. In relation to this, enabling data synchronization and interoperability between various smart devices in an IoT platform is challenging.

Security

Can you imagine an average Internet of Things device being targeted five minutes after going live? This trend is only anticipated to increase as more devices connect to the internet. According to Symantec, network routers account for the majority of cyberattacks against IoT devices, with each router receiving an average of 5,200 attacks per month.

In addition to the scalability and interoperability issues already highlighted, typical network security issues would also need to be solved for the IoT deployment to be successful. Access control, distributed denial of service (DDoS) attacks, device identity, personal data protection, authentication, and other confidentiality concerns are some of these. In order to provide end-to-end security, it is necessary to be able to make adjustments fast so that issues can be rectified before being exploited.

IoT connectivity providers

According to Gartner, these are the top 5 IoT connectivity providers:

• Vodafone Managed IoT Connectivity Services
• Verizon Managed IoT Connectivity Services
• AT&T Managed IoT Connectivity Services
• KORE Wireless Managed IoT Connectivity Services
• Itron Managed IoT Connectivity Services

The bottom line

The fragmented nature of IoT deployments means organizations can choose from many IoT connectivity standards. How to connect to IoT is one of the most important decisions when it comes to IoT. IoT connectivity should be selected based on carefully assessing each deployment’s characteristics. For some very high speeds, ultra-low latency connectivity is required.

This may lead to the adoption of 5G or 4G cellular IoT connectivity, but this decision must be balanced against the likely cost and power usage these technologies require. Low-speed connections that are not always on can be ideal for some simpler deployments, requiring smaller batteries and delivering IoT connectivity cost-effectively.

An IoT gateway is a hardware or software component that connects controllers, sensors, and other intelligent devices to the cloud. An IoT gateway, either a specialized hardware appliance or software program, is where all data traveling between IoT devices and the cloud is routed through. An intelligent gateway or control tier is another name for an IoT gateway.

What is an IoT gateway?

Data is sent between IoT devices and the cloud using IoT gateways, which also serve as network routers. Early on, the majority of gateway devices just transmitted traffic from IoT devices to the cloud. A gateway device now frequently manages both inbound and outgoing traffic. IoT data is sent to the cloud using outbound traffic streams, and device management operations like firmware updates use incoming traffic.

Some IoT gateways do tasks other than traffic routing. Sometimes the data can be preprocessed locally at the edge using a gateway device before being sent to the cloud. In order to reduce the amount of data that needs to be sent to the cloud, the device may deduplicate, summarize, or aggregate data as part of this process. This may significantly impact response times and network transmission expenses.

An additional layer of security for the IoT network and the data it transfers is another advantage of an IoT gateway. The security of IoT devices has frequently been proven to be lacking, despite improvements. For instance, the TCP/IP library, which is used by hundreds of millions of IoT devices, was found to include the Ripple20 vulnerability in 2020, rendering such devices vulnerable to attack.

Adopting an IoT gateway with capabilities like tamper detection, encryption, hardware random number generators, and crypto engines is one method a business may use to secure its IoT network. These and other characteristics can help protect individual devices from assault and secure an IoT gateway.

How does an IoT gateway work?

A straightforward IoT gateway performs similar duties to a Wi-Fi router. The gateway receives a Wi-Fi connection from an IoT system and then routes the data from the IoT device to the cloud. IoT gateways, however, are typically much more complicated.

The fact that IoT devices use a variety of different protocols contributes to the fact that IoT gateways are typically more complicated than Wi-Fi routers. These protocols include Bluetooth Low Energy, BACnet, Zigbee, and Z-Wave. As a result, in order to support all the IoT devices in an enterprise, an IoT gateway may need to handle a wide range of protocols.

The gateway must be able to route every form of IoT traffic to the right location in addition to supporting these protocols. While data from building security sensors may need to be transmitted to a SaaS provider that runs a cloud-based security interface, data from a group of industrial sensors may need to be transferred to a database in the cloud.

IIoT and edge computing are gaining traction in many industries

IoT gateways may need to locally cache data in the event of an internet outage or if the gateway is inundated with more data than it can process, which is another reason why they may be more complex than Wi-Fi routers.

IoT gateways frequently offer failover clustering or the capacity to scale out in order to handle growing workloads.

Edge computing and IoT gateways

Some IoT devices generate enormous amounts of data. If a company has a sizable IoT ecosystem of devices and wants to send data from all of those devices to the cloud, this could be an issue. The IoT gadgets might possibly use up all of the company’s internet capacity and cost a lot to store data in the cloud.

Using edge computing for at least some of the essential data processing can help prevent these kinds of issues. By lowering the amount of data that must be transferred to the cloud, this method aids in lowering costs and bandwidth usage.

Let’s say a company has a collection of IP-enabled security cameras that are all transmitting real-time data. Sending all of the unprocessed surveillance footage to the cloud for data processing definitely wouldn’t make much sense. This is particularly true if some of the cameras mainly focus on vacant spaces.

It is more effective to process the video footage at the edge rather than uploading all of the surveillance footage instantly. The edge device may distinguish between security film that the company deems worthy of keeping and footage that is inconsequential, such as video of an empty room. The footage that has to be evaluated can be sent from the edge device to a gateway device, which uploads the information to the cloud.

IoT gateways are crucial for controlling and protecting IoT devices, and they could also aid a company in lowering the bandwidth usage associated with IoT.

IoT gateway architecture

Four architectural layers can be found in IoT infrastructure. These consist of the following:

• Network layer: At this layer, data is compiled from many sources and sent to processing systems in a secure manner. Data aggregation and format conversion are tasks performed by data acquisition systems (DAS). At this level, IoT gateways work to connect IoT devices and processing infrastructure securely.
• Sensor layer: Devices gather data at the sensor layer for later processing.
• Data preprocessing layer: IoT sensor data is preprocessed and subjected to fundamental data analytics at this layer in order to limit the amount of data before it is transferred to cloud-based infrastructure. Operating at this layer are IoT edge devices.
• Cloud application layer: Applications and users have access to data and the findings of in-depth data analytics thanks to cloud-based infrastructure. At this tier, data warehousing or storage is also possible.

IoT gateway hardware

An IoT gateway device fills the communication gap between cloud-based IoT platforms, sensors, equipment, and systems. IoT gateway devices offer local processing and storage solutions as well as the capability to autonomously manage field devices based on data input from sensors by linking the field and the cloud in a systematic manner.

AIoT: The much-needed convergence of AI and IoT

An edge gateway is located at the junction of edge systems, where the local intranet used by the other devices in your ecosystem meets the public Internet. It serves as the primary access point for network connectivity both inside and outside of the ecosystem of your devices.

Is a router an IoT gateway?

Routers are perhaps the most widely used of all IoT gateways. They transmit IP packets in and out of a local network and to and from IoT devices, making it possible for your smart sensors or other devices to connect to the Internet and share the data they generate.

IoT gateway software

The digital age has not yet seen the arrival of many machines and plants. To take advantage of the advantages of the Industrial IoT, they must first be connected. The solution is the IoT gateway software, which you may use for both new and currently installed units.

The IoT gateway software increases the transparency of your machine and process data. Monitoring process data in real-time, including temperature, pressure, vibration, etc., ensures that your output is consistent with a high standard. The analysis of specific data using rules makes forecast and planned plant maintenance easier.

Advantages of using IoT gateways

Getting set up with IoT gateways will offer both short-term and long-term benefits, regardless of whether your business is prepared to invest extensively in IoT or wants to start with a few devices here and there.

Connecting devices to each other 

IoT gateways are similar to universal remote controls. A universal remote allows you to operate all of your gadgets from one area, saving you time and effort. While you can have several remote controls that each function with a particular device, this is not as convenient. Your devices can still function and are controlled without IoT gateways, but doing so is more difficult, and they can’t be set up to cooperate.

An IoT gateway serves as a central hub for data transmission to and from connected devices. Through the cloud, people and other devices are able to communicate with one another. IoT gateways and cloud-enabled software are used to communicate with IoT devices when you send or receive information to them, such as a change in protocol.

In order to expand and streamline their functioning throughout a physical space and when used with an increasing number of IoT devices and smart sensors, IoT gateways can also connect to one another. Universal IoT gateways can be installed early on in your technology plan, allowing you to add devices quickly and with no effort.

Data filtering

Your IoT gateway not only enables the communication between your devices but also streamlines communication by filtering data into valuable information. Seeing every record would be useless and slow down how quickly the IoT devices can perform and communicate because they can capture new bits of data in a split second. In order to improve communication and reaction times, IoT gateways are intelligent and capable of working at the edge. This means that each gateway may consider and examine the data provided before sending just the necessary, filtered data to the cloud.

Translating communication between IoT devices

Despite the fact that IoT devices are quickly integrating into our daily lives and that new services and products are constantly emerging to improve almost every area of our lives, there are currently no standards for a universal device language.

For instance, even though the lighting and climate control in your office may be motion-sensor equipped, it’s doubtful that they will be able to interact with one another unless they are made by the same firm, or unless you have IoT gateways that can translate data between them.

As more IoT devices are added, the more important hubs become in simplifying how they work together.

Security mitigation

The security concerns posed by IoT devices grow as their number increases. You’ve probably heard horror tales about the Internet of Things gone awry, like smart cars turning rogue or Wi-Fi baby monitors allowing hackers to listen in. All IoT devices are susceptible to outside interference and hacking, but an IoT gateway adds another layer between the Internet and the actual device. Even if your business won’t be substantially investing in IoT, a gateway allows for future investment while enhancing security for IoT devices you already own.

Intelligence edge

IoT gateways are an illustration of “intelligence at the edge” or “intelligent edge,” it’s crucial to remember. This means that data can be handled and understood by the IoT gateway itself rather than needing to be translated and processed by a third party or person. IoT gateways are a prime example of an active, intelligent edge.

IoT gateway examples: Use cases

Below you can see some real-life use cases of IoT gateway; we explained two different examples:

Video surveillance systems

IoT companies now provide HD video surveillance solutions with superb picture quality. Without IoT gateways that preprocess, filter, and compress data, it would not be possible.

There are a ton of records produced by these systems that need huge be kept and examined in some way. IoT gateways may have the additional computing power to preprocess the data, as we have already discussed. The video feed is automatically evaluated locally, thanks to edge computing. Consequently, the system doesn’t keep pointless records.

Industrial refrigerators monitoring

This system focuses on managing the collected data, showing the various indicators in the industrial freezers, and monitoring them. Let’s focus on the component we need, information collecting, rather than the technical intricacy of the solution.

Industrial refrigerator monitoring sensor nodes should be able to withstand extremely low temperatures. In such a severe environment, it is not best to connect sensors directly to the Internet via 4g.

IoT gateways use low-range protocols to collect data from sensors, which they then send to the cloud. The requirement to handle low temperatures vanishes because they are situated outside of the severe environment.

IoT edge security

One example of an intelligent IoT device installed at the network edge is an IoT gateway. Both advantages and disadvantages for IoT security can be provided by these devices.

Security as a service leaves cybersecurity to the experts, but it is a double-edged sword

Decentralized infrastructure

IoT gateways’ modest data processing power has both advantages and disadvantages for security. Edge computing for distributed data processing aids in resiliency and data minimization, but distributed architectures may be more challenging to secure because they cannot be shielded by perimeter-based defenses.

Data minimization

Before being sent over the Internet, data generated by IoT devices must be filtered by IoT gateways. This aids in lowering both the amount of data exchanged and the potential for network communications or exploited cloud-based servers to leak critical data.

Edge security

Between IoT devices and the open Internet, IoT gateways can have built-in security features. Because the IoT helps to eliminate the security gaps that are frequently present in IoT devices, this can assist in safeguarding an organization’s IoT devices and the sensitive data they gather from cyber threats.

Conclusion

IoT devices are frequently deployed by businesses, which can make it challenging to manage and monitor them. A centralized hub known as an IoT gateway links IoT gadgets and sensors to cloud computing and data processing.

Bidirectional data transmission between the cloud and IoT devices is frequently made possible by modern IoT gateways. As a result, IoT sensor data may be uploaded for processing, and commands can be delivered to IoT devices from cloud-based apps.

Why is payment automation a vital tool for your company? Well, big data, artificial intelligence, and machine learning are having a positive impact on businesses. Aversion to cutting-edge technology has always existed in the finance sector due to security issues, yet modern data science can empower businesses and help them avoid risk. Only a few of the potentials brought about by data science include stronger fraud detection, predictive risk management, anomaly identification, better sales and projections, and data-backed insights.

All contemporary enterprises use IoT (Internet of Things) devices to complete tasks. Modern businesses cannot function without this technology for a good reason. Most of the hassles businesses have dealt with for millennia can now be automated because of technology.

It seems reasonable to try to automate this procedure to speed things up since payments are now made digitally. The idea of payment automation is employed specifically for this.

What is payment automation?

The procedure of paying suppliers can be automated thanks to software solutions. Payment automation, in general, encompasses both automating the payment approval procedure as well as the payment’s actual sending. These automated payments can be transmitted via wire transfer, check cross-border or FX payment, virtual card payment, ACH transaction, or another payment method.

Payment automation speeds up vendor payments and streamlines the overall AP workflow by getting rid of time-consuming, error-prone manual AP operations. Employees in AP are then free to focus their efforts on more challenging and imaginative jobs.

How does payment automation work?

The concept of payment automation goes far beyond the use of electronic invoices. They need to be processed manually as well, despite being better than paper. The invoice arrives in a variety of formats, even when sent by email. Has a Word doc or PDF attachment been made? It might also be put into the email’s body (another format still). AP frequently finds it to be an excessive time commitment to manage the data from all of these numerous channels. Data input mistakes as a result of this may cause delays and late vendor payments.

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Optical character recognition is a technique that is used in the automated invoice processing payment method (OCR). The document is scanned, and the information is put into electronic tables. As a result, AP is spared from doing tiresome activities that can involve human error. Additionally, it expedites the payment procedure.

The invoice is automatically sent to the appropriate parties for approval after being received. Payment automation allows AP to specify certain parameters and filters to ensure that the invoice reaches the desk of the correct employee, unlike the manual method that bottlenecks. For instance, the leader of a particular sector may automatically receive goods ordered for that sector. Or a series of approvals can be necessary if the invoice exceeds a certain threshold. The system will then distribute to various users.

Payment automation solutions guarantee proper account ownership, eliminate bottlenecks, and fortify operations.

Why using payment automation is important?

Automating your regular payment acceptance procedures can greatly lower risks in your business. Disparities are much less likely to go undetected than they would on paper due to enhanced visibility and transparency. Your data won’t be lost due to a fire, flood, or burglary because it will be kept in the cloud.

Advantages of using payment automation

Here are a few of the many advantages of using a payment automation system for your business:

• Faster processing
• Efficiency
• Better CX
• Security against fraud 
• A complete service
• A flexible experience
• Everlasting bonds

Faster processing

A typical person can only process so much data at once. Computer systems are impervious to this weakness and are constantly functional. You obtain quicker processing times & more consistency when you hand off the payment procedure to the computer system.

Efficiency

Businesses now have a reliable solution that can function without pay in place of paying an employee or several people to manage the payment system. The expense of maintaining the automated system far outweighs the initial investment made in constructing it in the long term.

Better CX

The payment process is smooth because no human emotions or inefficiencies are involved. Customers, therefore, have a much more consistent experience. Another factor that supports this is that an automated system typically has lower transaction costs than a human employee.

Security against fraud 

Payment fraud has long been a serious issue for companies. Employee theft, client overcharging, and corporate scam attempts have become all too typical. Since the payment system is safeguarded between the client and the business system, a well-automated system makes fraud far more difficult to occur. It is simple to track all payments because every transaction is listed and kept in a database.

A complete service

Humans are built in such a way that mistakes are inevitable. Contrary to what is generally believed, an automated payment system is less prone to make mistakes. Errors not only take up a significant amount of important business and customer time, but they also damage vendor/supplier relationships and make it difficult for companies to flourish.

A flexible experience

A good payment automation system is particularly adaptable and can let businesses alter product prices on the fly. Additionally, they enable companies to offer specials and discounts that draw in additional clients.

Contactless payment usage has significantly increased in the past three years

Everlasting bonds

Faster payments, fewer risks of error, and a more reliable experience are all benefits of an automated system. All of this affects how the vendor and customer perceive you. In other words, effective payment automation results in improved interactions and higher levels of satisfaction.

Why do businesses need payment automation?

Face-value transactions are only one aspect of payment automation. It also includes the automation of all payment-related check-writing, ACH, virtual card, and wiring activities. As its name would suggest, payment automation is a whole system rather than just a process.

By 2024, according to studies, half of the world’s population will switch to entirely digital wallets. Payment automation will greatly enhance workflow for all firms as they must eventually convert to digital processes.

When your company is ready, you may transition from your present payment solution to payment automation by having it sit on top of it.

More than 82% of firms experienced payment fraud in 2018. It should be more crucial than ever for businesses to install a system like a payment automation system to stay secure.

With a competent payment automation system, your company can receive discounts on a variety of payment methods, including premium ACH and virtual cards.

The highly developed tools used in modern payment automation can totally replace humans. You may even create payment automation systems that grow with your company and learn on their own, thanks to the strength of AI and dynamic learning algorithms.

Best payment automation tools

Below we’ve listed some of the most important payment automation tools and solutions:

Chrome River

With a modern interface, you can rapidly capture, store, match, and approve invoices using Chrome River INVOICE, a worldwide, future-ready solution. Put an end to your difficulties with growing paper piles, late payment penalties, and visibility issues. With optimized AP automation, say yes to optimum efficiency, insight, and agility.

Klippa

Do you want to cut the time it takes to process invoices by 80%? From the moment an invoice is received and moves through the authorization procedure, we promise you a quick process. Leading AI and optical character recognition technologies are used by Klippa to speed up this procedure. You can easily see the status of your invoices with Klippa.

Procurify

Eliminate bottlenecks in the purchasing process and allow teams to purchase what they require when they require it. Your finance team can evaluate, approve, and track your purchases using the Procurify Platform, which interfaces seamlessly with your current accounting software. Utilize thorough procure-to-pay procedures, 3-way matching, and custom approvals to boost operations efficiency and improve expenditure visibility and management.

Conclusion

The future of payment systems for all enterprises is payment automation. All firms, provided they have the cash to pay for the system upfront, stand to gain significantly by implementing a robust and efficient payment automation system.

A more strategic future is being created by the explosion of automation technology. Accounts payable teams are transformed into strong hubs for business intelligence. Automation of AP has greatly increased during the last ten years. It’s not only about increasing productivity anymore. Every new tool that is released has a few more capabilities than the previous one. The cash management and financial health of a company can now be directly supported by the accounts payable department, thanks to platforms.

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Get AP in order immediately if business growth is a priority. The digital transformation era has arrived, and if you continue to hold back on modernization, your company will fall behind the competition—especially those businesses that are seizing the chance to rev up the financial engine.

Many businesses concentrate too much on one-size-fits-all projects when creating a growth plan. A business solution that may be used in various areas of the organization is needed. It’s impossible to overlook the opportunities that AP automation presents. Take the first step by controlling your AP system. Please spend some time explaining to the management team the advantages of these activities and how they fit within the company’s scaling plans. It not only improves morale but also establishes a standard methodology that allows for scaling.

• The Eclipse Foundation has revealed the results of its 2022 IoT and Edge Developer Survey.
• Agriculture (23%), industrial automation (22%), automotive (20%), and energy & smart cities (17%) have emerged as the most important industries utilizing IIoT and edge computing technology.
• Despite their continued dominance, Amazon AWS (36% use (-8% in 2022), Microsoft Azure (18% usage (-11% in 2022), and Google Cloud Platform (16% usage (-4% in 2022) have all lost ground in an increasingly competitive industry.
• Edge computing workloads, developer concerns, and market divides are all included in the study data.

The Eclipse Foundation has released its 2022 IoT and Edge Developer Survey findings. The survey, which the Eclipse IoT Working Group administers, the Eclipse Edge Native Working Group, and the Eclipse Sparkplug Working Group, provides critical insights into the IoT and edge computing industry landscapes, the challenges that developers face, and the opportunities for enterprise stakeholders in the IoT and edge open source ecosystem.

AIoT: The much-needed convergence of AI and IoT

The study, now in its eighth year, is the main technical survey for the IoT and edge industries. “IoT and edge computing are arguably the most important technologies today, particularly for industries like industrial automation, agriculture, and automotive. The insights detailed in the 2022 IoT and Edge Developer Survey report can help guide internal developer teams and technology decision-makers as they seek to bring the Industrial IoT to life,” said Mike Milinkovich, executive director of the Eclipse Foundation.

Key takeaways from the 2022 IoT and Edge Developer Survey

From April 1, 2022, to June 15, 2022, 910 worldwide developers, committers, architects, and decision-makers from a variety of businesses and organizations took part in an online poll. Among the key results are:

• The most popular programming languages for restricted devices are Java, C, and C++. According to developers, Java is the preferred language for IoT gateways and edge nodes.
• MQTT is the most frequently used IIoT communication protocol, despite signs of growing fragmentation. HTTP/HTTPS and REST usage are down somewhat compared to 2021, but alternative communication protocols (TCP/IP, AMQP, in-house/proprietary) are up significantly.
• Agriculture (23%), industrial automation (22%), automotive (20%), and energy & smart cities (17%) have emerged as the major industries using IIoT and edge computing technologies.
• In this year’s poll, security concerns nearly doubled, making it one of the top three difficulties developers face, along with connectivity and data collecting and analytics.

• As top edge computing workloads all show substantial gains in usage, edge computing is finding momentum in real-world applications.
• The big three are being challenged as public cloud fragmentation grows. Despite their sustained dominance, Amazon AWS (36% use (-8% in 2022), Microsoft Azure (18% (-11% in 2022), and Google Cloud Platform (16% (-4% in 2022) have all lost momentum in an increasingly competitive field.
• The most often used edge computing artifact (49%) is container images.

Data from the 2022 IoT and Edge Developer Survey also includes information regarding edge computing workloads, developer concerns, and market splits. The whole study, including significant findings, may be obtained here.

Conductors of the 2022 IoT and Edge Developer Survey, Eclipse IoT, has over ten years of experience in edge computing and the Internet of Things. Eclipse IoT is the birthplace of open source innovation, which has resulted in some of the most popular IoT protocols in the market. Eclipse IoT projects underpin CoAP (Eclipse Californium), DDS (Eclipse Cyclone DDS), LwM2M (Eclipse Leshan), MQTT (Eclipse Paho, Eclipse Mosquitto, and Eclipse Amlen), and OPC UA (Eclipse Milo).

Eclipse zenoh, a new native protocol created from the ground up for edge computing, is also included in the Eclipse IoT toolbox. Other prominent Eclipse IoT production-ready systems include digital twins (Eclipse Ditto), contactless payments (Eclipse Keyple), industrial applications (Eclipse Kura), Eclipse Kapua — a modular IoT cloud platform that manages data and devices, Eclipse Kanto, and much more.

Are you looking for the best home automation companies? Well, it is not surprising. Using a home automation system to operate appliances wirelessly is popular right now. Home automation is not a new concept around the globe, but its relevance and relativity are growing rapidly these days.

The future of home automation has altered thanks to customized solutions. By 2024, the home automation industry will be anticipated to increase to 151.4 billion USD. As the industry evolves, big tech companies have already begun investing in home automation solutions. But which one is the best? First, briefly remind what is home automation.

What is home automation?

What is the meaning of home automation? The automatic and computerized control of household activities, features, and appliances is called “home automation.” Simply said, you can simply control the appliances and features in your house online to improve convenience, increase security, and even reduce household expenses.

Home automation is popular. Especially in the USA, 22 million houses already utilize IoT or smart technology to ease daily life. According to a CTA report, 83 million households have adapted to home automation, and the figures are anticipated to fluctuate over the coming years.

Advantages of home automation

Smart-home automation is the new hype. Are you wonder why people love it? Let’s find out the advantages of home automation:

• Accessibility
• Energy saving
• Better security
• Instant notifications
• Indoor system control

Let’s take a closer look at how home automation affects our lives positively.

Accessibility

If you have a smart home, accessibility is a significant advantage. You may control things remotely using the voice-activated system or an app in case you forget to switch off your geyser after a hot shower or lose the AC remote and can’t find it.

Home automation technologies can help you do tasks quickly, even when you are not at home.

Energy saving

Due to the automated monitoring systems installed in your home and allowing you to utilize technology effectively, you can save more money on your electricity and utility bills.

Most smart devices are powered by human technology and turn themselves off when they detect your absence. Therefore, you help conserve energy sources using heaters and air conditioning systems.

Better security

One of the key arguments in favor of automation among homeowners is sophisticated security systems. Your best bet is technology, particularly if you reside in a bad-reputation neighborhood. Biometrics and voice recognition are typically used to operate the electronic locking mechanism.

Your protection from theft and break-ins will be increased with home automation.

Indoor system control

The majority of smart houses are equipped with internet-based security systems and security apps. In an emergency, you can receive warnings about accidents or events on your smartphone and take corrective action to limit the damage. A smart home is the best method to safeguard you and your family from unknown risks, from panic buttons to fire alarms.

On the other hand, you can just want to learn when the home temperature falls. Thanks to home automation systems, you will get notified immediately.

Comfort

For those with disabilities or lazy ones, performing simple tasks is difficult. Now you can grab items effortlessly, which is one of home automation’s biggest benefits. You may lock your house, close the blinds, or turn on/off lights or fans from the comfort of your couch.

You gain from home automation, especially if you live alone.

Disadvantages of home automation

What are the disadvantages of home automation? No matter how effective and efficient a technology is, doing your research is always preferable. Check out the disadvantages of home automation:

• Implementation costs
• Cybersecurity
• Maintenance costs
• Power supply

Considering several aspects, including expenses and technological constraints, you should consider home automation’s drawbacks too.

Implementation costs

Home automation systems’ biggest drawbacks are the up-front equipment price and installation. While using smart technology might help you cut costs, you could be startled by the upfront expense.

Your modest home can be automated at an average cost of $2,000 to $7,000. The price can potentially increase to around $2,000, depending on the style and structure of your property.

Cybersecurity

Can smart homes be hacked? Based on internet technologies, smart homes are susceptible to hacking. Most systems also require password-based authentication, which puts your security at risk if your credentials are compromised. To make matters worse, setting up private WiFi is also expensive.

Maintenance costs

The maintenance price can be high because you might occasionally need to hire an expert to maintain and repair your system.

Ensure that maintenance is performed correctly by a qualified, competent company. If your system is not set up properly, you can deal with various issues, from device malfunctions to system failure. If it does, be prepared for additional charges.

Power supply

In an automated home, devices need a consistent, uninterrupted power source that is always available. Internet access and electricity work together to make your home completely smart.

Ensure power fluctuations or outages do not impact your home before installing the home automation equipment. These conditions can harm smart home products or lockdown your home.

Is automation jeopardizing our future?

However, some companies have solutions for these disadvantages and want to make your home “smart.”

Top home automation companies in the world

While some businesses manufacture, market, and give services directly to customers, others focus solely on providing services based on their experience and talent in selecting the best items for client needs. These are some of the best home automation companies in the world:

• Google LLC (Nest)
• Amazon Inc. (Echo)
• Apple HomeKit
• Samsung (SmartThings Hub)
• ABB Ltd.
• LG Electronics (LG ThinQ)

Google LLC (Nest)

Google, founded in 1998, is among the greatest home automation businesses. It is regarded as the industry leader in digital goods and services due to its broad product lineup. Google offers more than just the top search engine in the world; it also offers cloud computing, hardware, software, online advertising tools, and much more.

Google’s entry into home automation systems, Google Nest, is the outcome of the purchase of Nest Labs. An extraordinary amount of alarm integration is available with the Google Nest Hub. Use the Nest Hub to control your smart thermostats, smoke detectors, and home security devices.

Amazon Inc. (Echo)

One of the earliest ecosystems for virtual assistants may have been Amazon’s Alexa. Amazon offers more than only its delivery platform, which you are undoubtedly already aware of. You may trust this reputable and astute home automation company for technology like artificial intelligence, e-commerce, cloud computing, digital streaming, and consumer electronics. Amazon is one of the best home automation companies.

The Echo from Amazon is a smart home hub that works with Alexa. The Astro Household Robot, the Ring Always Home Cam, the Echo Show, and many other gadgets are among those that work with Echo.

The most intriguing feature is the ability to use voice commands to control your devices. For sophisticated home automation, these commands are relayed to the central hub.

Apple HomeKit

Apple is a top manufacturer of smartphones, laptops, tablets, and wearable technology. The company’s growth into one of the leading home automation companies looks like a logical next step.

Your devices, including the HomePod, Mac Book, Apple TV, iPad, and other appliances, are managed by the Apple HomeKit, which serves as a smart hub. You must download the Home app from Apple and log in with your Apple ID.

Apple HomeKit, which enables other devices, is now available on the iOS and Mac OS interfaces. For instance, doorbells, smart plugs, and motion detectors all have Apple connections.

Samsung SmartThings Hub

Samsung has been around electronics, building, and engineering for over 80 years. One of the most well-liked smartphones globally is the Samsung model. The Samsung SmartThings Hub is a popular product under the brand.

It is now among the top sellers in the home automation industry. The SmartThings app is available for download to any mobile device, including your phone and tablet. With the help of this app, you can manage a variety of home electronics and appliances, including smart locks, smart lighting, and thermostats.

You can use the interactive phone app to remotely access other small IoT devices. With a few touches on your phone’s touch screen, you may switch between controlling them.

ABB Ltd.

One of the best and most well-known firms for home automation is ABB Ltd., a multinational Swedish-Swiss corporation. The corporation primarily deals with automation, large electrical machinery, and robotics. You can count on a network that is affordable and effective if you decide to invest in ABB equipment. Y

The hub may be used to control all of your home electronics, including security cameras, for total peace of mind. Thermostats, remote temperature control, smart lighting systems, and voice control are more examples of typical integrations.

LG Electronics (LG ThinQ)

LG Electronics is a well-known household name established in 1958 and does business from its corporate headquarters in Seoul, South Korea. Global consumers are familiar with the LG line of products, which includes mobile phones, home appliances, and other household goods.

Additional environmental control options include heaters, air conditioners, and HVAC systems. With the LG ThinQ®, LG has recently joined the ranks of home automation providers.

This hub easily connects to common household appliances like laundry machines, refrigerators, and thermostats, to mention a few. With the LG ThinQ, you can even program your vacuum cleaners.

Best home automation companies in the USA

Since it is mostly used in America, it would be right to examine home automation companies in the USA separately. These are some of the best home automation companies in the USA:

• Control4 home automation
• Savant Systems LLC
• Canary
• August Home
• HomeSeer

So, let’s take a closer look at them.

Control4 home automation

Control4, a well-known manufacturer of smart home automation systems, is active throughout the UK and independently offers consulting services, services, and products. Control4 is one of the best home automation companies in the USA.

In 2003, Eric Smith, Will West, and Mark Morgan developed Control4 to offer simple-to-use networking and home automation software solutions for residential and commercial buildings. Over 400,000 homes in 100 countries have been automated and secured using Control4 systems and products.

It has specialized automation solutions for lighting, video, audio, camera security, etc., and its integrated automation systems for the complete house.

Savant Systems LLC

Savant is another outstanding business serving European consumers by providing the greatest consulting services and goods.

Audio, video, controllers, touchscreens, and many other brands held by Savant can be integrated with most goods on the market that is relevant to smart solutions.

Canary

Adam Sager, Chris Rill, and Jon Troutman created Canary, a firm with headquarters in New York City, in 2014. Their most recent fundraising round received 66.1 million USD in 2018.

Systems for home security are their area of expertise. There aren’t many Canary models that combine a camera, siren, and air monitor in one setup, like the Canary Pro security camera.

August Home

Smart Locks are a cutting-edge product line offered by August Home that is committed to the safety and modernization of smart lock systems.

They provide doorbell cameras and their trademark smart locks, among other accessories. In 2012, Jason Johnson and Yves Behar established August Home. In the Locks, Keys, Windows, and Doors domain, August Home has approximately 55 patents.

HomeSeer

For the past 20 years, HomeSeer has been one of the top technology providers in the home automation industry. HomeSeer offers a specialized product line called Smart Home Hubs.

It offers integrated automation and security controls for lighting, garage doors, temperature, door locks, water valves, and security cameras. According to the corporation, its products are simple to handle, automate, and set up. Richard Helmke introduced HomeSeer in 1999, and it has been steadily advancing.

German home automation companies

Let’s look at the heart of Europe. These are the best German home automation companies:

• Siemens AG
• BOTECH GROUP
• WIR ELEKTRONIK GMBH & CO. KG

So, what do they offer?

Siemens AG

Founded in Munich, Germany, in 1847, Siemens is a well-known name in technology for electronics and digitization. It has recently shown that it can keep up with the development of cutting-edge home technology.

The Siemens LOGO! Hub, which manages your smart home needs and appliances, is a secure investment. And that includes automatic switch-offs, irrigation pump control for fountains and gardens, swimming pools, Jacuzzis, gates, and access control. The typical lighting and dimmer settings, climate control, and more are accessible in addition to these functions.

BOTECH GROUP

Founded in 2007, the Distributor BOTECH GROUP GMBH works in the automatic locking and closing systems sector.

Additionally, it engages in the sectors of home automation, automatic gate controls, automatic controls and accessories, and automatic garage door openers. Its headquarters are in Frankfurt, Germany.

WIR ELEKTRONIK GMBH & CO. KG

The manufacturer/producer WIR ELEKTRONIK GMBH & CO. KG was established in 2013 and worked in the home automation sector. Its headquarters are in Stadtlohn, Germany.

How does home automation work?

A home automation system comprises three major components:

Sensors: Sensors can observe variations in temperature, sunshine, or motion. Then, according to your preferences, home automation systems can change those settings and more.

Controllers: Computers, tablets, and smartphones used to send and receive messages regarding the status of automated systems in your house are referred to as controllers.

Actuators: The actual mechanism or function of a home automation system is controlled by actuators, which can be light switches, motors, or motorized valves. They are set up to be activated by a controller’s remote instruction.

A network of hardware, communication, and electronic interfaces called home automation connects commonplace devices to one another over the Internet. Whether at home or thousands of miles away, you can control each gadget from your smartphone or tablet because they all have sensors and WiFi connectivity. No matter where you are, you may use this to turn on the lights, lock the front door, or even lower the heat.

Best home automation products/systems

While we haven’t yet achieved the Jetsons lifestyle, the aforementioned products and technologies have helped us come closer. These are the best home automation products/systems:

• Nest
• Echo
• Apple HomeKit
• SmartThings Hub
• LG ThinQ

Home automation examples/applications

What are the common uses of home automation? Systems for home automation provide a range of services and capabilities. The following are some of the most popular features offered by these platforms:

• Live video surveillance
• Alarm systems
• Real-time text and email alerts
• Digital personal assistant integration
• Keyless entry
• Voice-activated control
• Fire and carbon monoxide monitoring
• Remote lighting control
• Thermostat control
• Appliance control
• Home automation security systems and cameras

What is hyperautomation, and how it works?

How much is a fully automated house?

An average 4-bedroom, 3-bath home might cost up to $15,000 to automate fully. The cost of a luxury, wired home ranges from $10,000 to $150,000.

Installation of wired systems requires $85 per hour of labor. Lighting, security, locks, thermostats, and entertainment are all included in home automation.

Top home automation companies

To sum up, these are the top home automation companies that we talked about:

• Google LLC (Nest)
• Amazon Inc. (Echo)
• Apple HomeKit
• Samsung SmartThings Hub
• ABB Ltd.
• LG Electronics (LG ThinQ)
• Control4 home automation
• Savant Systems LLC
• Canary
• August Home
• HomeSeer
• Siemens AG
• BOTECH GROUP
• WIR ELEKTRONIK GMBH & CO. KG

Conclusion

With home automation, you may access and manage your house’s appliances from any global location using a mobile device.

Home automation refers to homes where practically everything—smart light switches, appliances, outlets, heating and cooling systems—hooks up to a remotely controllable network instead of isolated programmable items like smart thermostats and sprinkler systems. So, from Siemens to Apple, many global firms are already invested in and trying to be the best.

The process of copying data to guarantee that all information remains similar in real-time between all data resources is known as data replication, also known as database replication. Consider database replication as a net that prevents your information from slipping through the cracks and disappearing. Data rarely remain constant. It changes constantly. Thanks to an ongoing process, data from a primary database is continuously replicated in a replica, even if it’s on the opposite side of the world.

The common goal of data replication is to reduce latency to sub-millisecond periods. Pressing the refresh button on a website and waiting for what seems like an eternity (seconds) to see your information refreshed is a scenario we have all experienced. A user’s productivity is reduced by latency. The objective is achieving near-real-time. For whatever use scenario, zero time lag is the new ideal.

What is data replication?

Data replication is the process of creating numerous copies of a piece of data and storing them at various locations to improve accessibility across a network, provide fault tolerance, and serve as a backup copy. Data replication is similar to data mirroring in that it can be used on both servers and individual computers. The same system, on-site and off-site servers, and cloud-based hosts can store data duplicates.

Modern database solutions frequently leverage third-party tools or built-in features to replicate data. Although Microsoft SQL and Oracle Database actively provide data replication, some traditional technologies might not come with this feature by default.

Data replication in distributed database

Data replication is the process of making several copies of data. These copies, also known as replicas, are then kept in some places for backup, fault tolerance, and enhanced overall network accessibility. The replicated data might be kept on local and remote servers, cloud-based hosts, or even all within the same system.

Data replication in a distributed database is the process of distributing data from a source server to other servers while keeping the data updated and in sync with the source so that users can get the data they need without interfering with the work of others.

What do you mean by data replication?

For instance, your standby instance should be on your local area network in case you need to recover from a system outage (LAN). You can then replicate data synchronously from the primary instance over the LAN to the secondary instance for essential database applications. Because it is now in sync with your active instance and “hot,” your backup instance is prepared to take over immediately in case of a breakdown. High availability (HA) is the term used for this action.

Make sure your secondary instance is not positioned where your primary instance is in the event of an emergency. This means that you should locate your backup instance in a cloud environment connected by a WAN or at a location far from the first instance. Data replication on a WAN is asynchronous to prevent adversely affecting throughput performance. As a result, updates to standby instances will be made later than updates to active instances, delaying the recovery process.

A study paper titled “Efficient privacy-preserving data replication in fog-enabled IoT” outlines the dependency of cloud computing on network performance:

“According to research, 41.6 billion Internet of Things (IoT) devices will be generating 79.4 zettabytes of data by the year 2025. A high volume of data generated by IoT devices is processed and stored in cloud computing. Cloud computing has a strong dependency on network performance, bandwidth, and response time for IoT devices’ data processing and storage. Data access and processing can be a bottleneck due to long turnaround delays and high demand for network bandwidth in remote cloud systems.”

What is the purpose of data replication?

You should duplicate your data to the cloud for five reasons:

• As we described previously, cloud replication keeps your data off-site and away from the business’s site. Although a significant disaster, such as a fire, flood, storm, etc., can destroy your primary instance, your secondary instance is secure in the cloud. It can be utilized to restore any lost data and applications.
• Replicating data to the cloud is less expensive than doing so in your own data center. You may eliminate the expenses of running a second data center, such as hardware, upkeep, and support fees.
• Replicating data to the cloud for smaller firms can be safer, especially if you don’t have security expertise on staff. The network and physical security offered by cloud providers are unrivaled.
• On-demand scalability is provided by replicating data to the cloud. You don’t have to spend money on more hardware to maintain your secondary instance if your business expands, contracts, or let that hardware lie idle if the business picks up. You don’t have any long-term contracts either.
• You have a wide range of geographic options for replicating data to the cloud, including having a cloud instance in the next city, across the country, or another country, depending on your business’s requirements.

How do you replicate a database?

Database replication can be a one-time event or a continuous procedure. It involves every data source in the dispersed infrastructure of a company. The data is replicated and fairly distributed among all the sources using the organization’s distributed management system.

Data mature businesses are more profitable than others

DDBMS, or distributed database management systems, generally make sure that any alterations, additions, or deletions made to the data in one place are automatically reflected in the data kept at all the other locations. The system that oversees the distributed database, which results from database replication, is known as a distributed database management system, or DDBMS.

One or more applications that link a primary storage location with a secondary location—often off-site—represent the traditional situation of database replication. Individual source databases like Oracle, MySQL, Microsoft SQL, and MongoDB, as well as data warehouses that combine data from these sources and provide storage and analytics services on greater amounts of data are most frequently used as the primary and secondary storage locations. Cloud hosting is frequently used for data warehouses.

What are the types of data replication?

There are three main types of data replication.

Data replication types

Data replication can be categorized into 3 main types: Transactional, snapshot, and, merge data replication.

Transactional replication

Transactional replication automatically distributes frequent data changes amongst servers. Changes are replicated from the publisher to the subscriber almost instantly. It captures each stage of the transaction and the sequence in which the changes occur, rather than just copying the outcome.

For instance, in the case of ATM transactions, the replication from the publisher to the subscriber includes all of the individual transactions made between the start and end balances. The fact that data changes at the publisher are duplicated at the subscriber but not the other way around is another important aspect of transactional replication. By default, data updates don’t take place at the subscriber level.

If only you knew the power of the dark data…

Snapshot replication

Data is synchronized between the publisher and subscriber at a specific moment via snapshot replication. A single transaction transfers data chunks from the publisher to the subscriber. As opposed to transactional replication, updates in a snapshot replication happen less often. To create a baseline state for the two servers before transactional replication is possible though. Both the order of data changes and every transaction between servers are not updated.

Data that changes over time is synchronized via this process. For instance, many businesses clone information from a cloud CRM to a local database for reporting purposes, such as accounts, contacts, and opportunities. Depending on how frequently the data changes, this might be done once every 15 minutes, once every hour, or once every day. Instead of taking a complete snapshot at each replication period, the replication process may recognize when data has changed in the publisher and duplicate only the changes.

Merge replication

Merge replication is a little more complicated than standard replication. Snapshot replication is used for the initial synchronization from the publisher. However, data modifications can take place at the publisher and subscriber levels in this fashion. The merging agent, installed on all servers, receives the updated data after that. The merging agent uses algorithms for resolving conflicts to update and distribute the data.

For instance, transactional replication would occur if a worker was online and revising a document directly stored on a cloud server (publisher) on their laptop or phone (subscriber). This is possible since the content is saved almost instantly. However, since the data was updated at the subscriber’s end, there would be contradictions if the document was downloaded from the cloud server and updated offline on the laptop or phone. Once online again, it would travel through a merging agent, which would compare the two files to update the document at the publisher using a conflict resolution procedure.

Merge replication is utilized in a variety of situations where a user does not always have direct access to the publisher, such as with mobile users who may go offline while the data is being updated. It would also be utilized if many subscribers had access to, updated, and synced the same data with the publisher or other subscribers at different periods. It might also be utilized when numerous subscribers are simultaneously updating the same publication data in pieces.

What is the data replication strategy?

The majority of database-based solutions monitor all database changes from the very beginning. Additionally, it creates a record for the same that is referred to as a log file or changelog. Every log file functions as a collection of log messages, each of which contains information such as the time, user, change, cascade effects, and manner of the change. The database then gives each of them a distinct position ID and keeps them in a chronological sequence depending on their IDs.

Enabling customer data compliance with identity-based retention

What are the three data replication strategies?

Although businesses may use many methods for replicating data, the following are the most typical replication strategies:

Full-table replication

Every transaction involves full-table replication, which ensures that all data, including new, updated, and existing data, is replicated. Hard-deleted data can be successfully recovered using this replication strategy, as can data from databases without replication keys.

Key-based incremental replication

Data that has changed since the last update is captured incrementally using keys. Keys are components found in databases that start data replication. This method works for databases that hold data records on distinct elements and concentrate on recent changes rather than historical values.

Log-based incremental replication

Log-based data replication is a technique in which essential changes are made and recorded in a log file or changelog. Only the backend databases for MySQL, PostgreSQL, and MongoDB support this data replication mechanism.

Database replication schemes

For database replication, the following replication schemes are employed:

Full replication

Full replication entails replicating the entire database across all nodes in the distributed system. This plan improves worldwide performance and data accessibility while maximizing data redundancy.

Partial replication

Based on the importance of the data at each site, partial replication happens when specific portions of a database are duplicated. As a result, the number of replicas in a distributed system can be anywhere between one and the precise number of nodes.

No replication

There is no replication when there is only one fragment on each distributed system node. The easiest data synchronization may be accomplished with this replication strategy, which is also the fastest to execute.

What are some advantages and disadvantages of data replication?

Data replication enables extensive data sharing among systems and divides the network burden among multisite systems by making data accessible on several hosts or data centers.

Data replication advantages 

Consistent access to data can be provided by using data replication. Additionally, it expands the number of concurrent users with data access. By combining databases and updating slave databases with partial data, data redundancies are eliminated. Additionally, databases are accessible faster with data replication.

• Reliability: A different site can be used to access the data if one system is unavailable due to malfunctioning hardware, a virus attack, or another issue.
• Better network performance: Having the same data in various places might reduce data access latency since the data is retrieved closer to the point where the transaction is being executed.
• Enhanced support for data analytics: Replicating data to a data warehouse enables distributed analytics teams to collaborate on shared business intelligence projects.
• Performance improvements for test systems: Data replication makes it easier to distribute and synchronize data for test systems that require quick data accessibility.

Data replication disadvantages

Large amounts of storage space and equipment are needed to maintain data replication. Replication is expensive, and infrastructure upkeep is complicated to preserve data consistency. Additionally, it exposes additional software components to security and privacy flaws.

Cloud costs have started to become a heavy burden for the IT sector

Organizations should balance the advantages and disadvantages of replication, even though it has many advantages. Limited resources are the key barrier to maintaining consistent data across an organization:

• Costs: It costs more to store and process data when copies are kept in several places.
• Time: A team within the organization must commit time to setting up and maintaining a data replication system.
• Dense network: Consistency across data copies necessitates new processes and increases network traffic.

Data replication tools

Data replication on your end may occur for a variety of reasons. You might require application migration to the cloud or be searching for a hybrid cloud solution. It depends on whether you need real-time setup analysis or replication for synchronization in your instance Understanding why you want to perform a replication in the first place is the first step.

Database replication technologies frequently provide a variety of replication functions, as well as other ancillary functionality. Your needs and expectations for the tool must be written down. It might depend on how many sources and targets are involved, how much data you’ll be dealing with, etc.

You must choose the best mix of appropriate elements for your circumstance. Your choice of database replication tools may be influenced by cost, features, and accessibility factors. A business should invest in its budget. Finding the best Database Replication solutions for the job within your allocated budget is the objective.

These are some of the best data replication tools:

Rubrik

Rubrik is a solution for managing and backing up data in the cloud that provides quick backups, archiving, immediate recovery, analytics, and copy management. It provides streamlined backups and incorporates cutting-edge data center technologies. You may assign tasks to any user group with ease, thanks to an intuitive user interface. The integration of different clusters into a single dashboard, which is necessary depending on the use case, has some limitations.

SharePlex

Another database replication tool that uses real-time replication is called SharePlex. The program is very flexible and works with many different databases. Fast data transport is available and extremely scalable thanks to a message queuing mechanism. Both the tool’s change data collecting process and its monitoring services have some shortcomings.

Hevo Data 

Data teams are essential to driving data-driven decisions as firms’ capacity to collect data grows exponentially. Even yet, they find it difficult to combine the disparate data in their warehouse to create a single source of truth. Data integration is a headache because of faulty pipelines, problems with the data quality, glitches, errors, and a lack of control and visibility over the data flow.

Hevo’s Data Pipeline Platform is used by 1000+ data teams to quickly and seamlessly combine data from more than 150 sources. With Hevo’s fault-tolerant architecture, billions of data events from sources as diverse as SaaS apps, databases, file storage, and streaming sources may be duplicated in almost real-time.

Key takeaways

• By synchronizing cloud-based reporting and facilitating data migration from many sources into data stores, such as data warehouses or data lakes, to provide business intelligence and machine learning, data replication supports advanced analytics.
• Users can retrieve data from the servers nearest to them and experience lower latency because data is stored in various locations.
• Data replication enables businesses to distribute traffic over several servers, improving server performance and reducing the strain on individual servers.
• Data replication offers effective disaster recovery and data protection. Millions of dollars can be lost each hour that a critical data source is down due to data availability.
• Depending on the use case and the current data architecture, businesses can employ a variety of data replication approaches.
• A data replication method can be expensive and time-consuming to invest in. To obtain a competitive edge and protect their data from downtime and data loss, it is crucial for businesses that wish to use data for a variety of analytical and business use cases.

FAQ

What is the difference between replication and backup?

For many companies that must maintain long-term data for compliance reasons, backup remains the go-to option.

But data replication focuses on business continuity—providing mission-critical and customer-facing programs with uninterrupted operations following a disaster.

What is data replication in DBMS?

The technique of storing data across multiple sites or nodes is known as data replication. It helps increase the accessibility of data. It merely entails copying data from a database from one server to another so that every user can get the same information without any discrepancies. As a result, users can access data pertinent to their duties without interfering with the work of others using a distributed database.

Data replication includes the continuous copying of transactions to keep the replicate up to current and synced with the source. In contrast, data replication makes use of several locations for data availability, but each relation only needs to be stored in one place.

What is data replication in SQL?

A group of technologies known as replication is used to distribute and replicate data and database objects from one database to another, then synchronize data between databases to ensure consistency. Replication allows you to send data through local and wide area networks, dial-up connections, wireless connections, and the Internet to many locations as well as distant or mobile users.

What is data replication in AWS?

By automatically transforming the source database schema, AWS SCT facilitates heterogeneous database migrations. The majority of custom code, including views and functions, are also converted by AWS SCT into a format appropriate for the target database.

There are two steps involved in heterogeneous database replication. You can duplicate data between the source and target using AWS DMS, or you can use AWS SCT to convert the source database schema (from SQL Server) to a format compatible with the target database, in this case, PostgreSQL.

Conclusion

Instances of database replication defined as master-slave configurations in the past are now more commonly described as master-replica, leader-follower, primary-secondary, and server-client configurations.

With the development of virtual machines and distributed cloud computing, replication techniques originally focused on relational database management systems have been broadened to cover nonrelational database types. Once more, different non-relational databases like Redis, MongoDB, and others use different replication techniques.

While horizontally scaling distributed database configurations, both on-premises and on cloud computing platforms, as well as remote office database replication may have emerged as drivers of replication activity, and remote office database replication may have been the canonical example of replication for many years. Relational databases like IBM Db2, Microsoft SQL Server, Sybase, MySQL, and PostgreSQL all have different replication specifications.

Data replication design always involves striking a balance between system performance and data consistency. At least three methods exist for database replication. In snapshot replication, data from one server is simply moved to another server or a different database on the same server. Data from two or more databases are merged into one database during merging replication. In addition, user systems receive full initial copies of the database in transactional replication and periodic updates as data changes.

• 12th Gen Intel Core SoC processor for IoT Edge’s availability was announced.
• The SoC processors include manageability features from top to bottom, including Intel vPro choices for best-in-class remote control and management, which is critical for maintaining and servicing IoT edge devices.
• Compared to 10th Gen Intel Core desktop CPUs, SoC processors with up to 14 cores and 20 threads achieve up to 1.32 times higher single-thread performance and up to 1.27 times faster multithread performance.

The availability of Intel’s 12th Gen Intel Core SoC processor for IoT Edge has been announced. This unique socketed system-on-chip (SoC) delivers great performance with integrated graphics and media processing for visual compute workloads.

12th Gen Intel Core SoC processor delivers big upgrades

Also, the minimal footprint to enable smaller ingenious form factor designs and a wide operating thermal design power (TDP) that enables designs without fans and helps clients achieve product sustainability goals, as part of a new roster of purpose-built edge products specifically developed for the Internet of Things (IoT) applications.

According to Jeni Panhorst, Intel vice president and general manager of the Network and Edge Compute Division, “As the digitization of business processes continues to accelerate—fueled by workforce demand, supply chain constraints, and changing consumer behavior—the amount of data created at the edge and the need for it to be processed and analyzed locally continues to explode. Intel understands the challenges that businesses face — across a wide range of vertical industries — and is committed to helping them continue to deliver innovative use cases.”

U.S. cracks down on AI chip export to China

To future-proof AI workloads, digital transformation at the edge necessitates higher processing power and AI inference capability. 12th Gen Intel Core SoC processor for IoT Edge address these performance demands while also increasing implementation configurability and overall solution flexibility, allowing original equipment manufacturers and original design manufacturers to integrate quickly – and ship – solutions that address a wide range of unique vertical market and edge-specific use cases.

Furthermore, the SoC processors include manageability features from top to bottom, including Intel vPro choices for best-in-class remote control and management, which is critical for maintaining and servicing IoT edge devices.

12th Gen Intel Core SoC processors for IoT Edge include Intel Thread Director, which competently directs the operating system to assign the correct workload to the right core and deliver up to 4 times quicker graphics and up to 6.6 times faster GPU image classification inference performance when compared to 10th Gen Intel Core desktop processors in a 12W to 65W design.

Compared to 10th Gen Intel Core desktop CPUs, SoC processors with up to 14 cores and 20 threads achieve up to 1.32 times higher single-thread performance and up to 1.27 times faster multithread performance.

AIoT: The much-needed convergence of AI and IoT

The 12th generation Intel Core SoC processors for IoT Edge enable high-performance AI for inference and machine vision. Up to 96 graphics execution units provide strong parallelization in AI applications, while Intel Deep Learning Boost (Intel DL Boost) on the CPU delivers extra inferencing speed. These CPUs are compatible with Intel Distribution’s OpenVINO toolkit optimizations and cross-architecture inference.

• As of the end of June 2022, there were 17.1 connected devices and smart home appliances on average per home worldwide, up 10% over the previous year in the same period.
• The highest penetration of connected devices to date was discovered in US homes powered by Plume, with an average of 20.2 connected devices per home.
• A significant increase trend in the typical number of cyber threats that were successfully stopped was also reported by Plume.

There is increasing demand for smart home products. Businesses have been adjusting to post-pandemic hybrid working over the past year or so, with knowledge workers gradually returning to the office after working almost exclusively from home for many months. During that time, they were crucial in boosting sales of smart home devices, according to a study.

There is great interest in smart home appliances in Europe

The personalized communications services provider, which recently inked agreements with top service providers like Virgin Media, analyzed anonymized and aggregated data from a cross-section of the 41 million homes across the US, Europe, and Japan that is managed by the Plume Cloud, comparing the periods from January to June 2021 with that of January to June 2022.

The growing demand for the internet of things (IoT) and smart home technologies among consumers of home technology was one of the significant trends that emerged. According to the report, there were 17.1 connected devices on average per home worldwide as of June 2022, an increase of 10% over the same time last year.

Europe saw the most significant change with a 13% increase to 17.4 connected devices on average per Plume home. With an average of 20.2 connected devices per home, US homes powered by Plume have the highest penetration of connected devices to date.

There was an increase (11%) in data usage across the Plume Cloud, with up to 10% more devices in households using Plume. However, fitness bikes saw the highest drop in data use, down 23%, which likely indicates a shift in consumer behavior when individuals start working again and exercising outside or in the gym as they get used to the post-pandemic world of hybrid working.

“Plume’s data reflects two interesting periods – the first half of 2021, when consumers were gradually emerging from the pandemic restrictions that had kept them homebound, and the first half of 2022, when people started going back into the world. Despite the radically different circumstances, it’s evident that smart homes and IoT devices remain very much key to our connected lifestyles,” explained the CMO at Plume, Todd Grantham.

The study discovered a rise in device types and a significant growth in device volumes. With an average of more than six smartphones per household across all locations, including “guest” devices permitted to access the network, smartphones continue to be the most common device in Plume-powered homes.

AIoT: The much-needed convergence of AI and IoT

Apple goods dominated device brand loyalty, with a “presence” (defined as one or more same brand devices) in more homes and in houses that Plume believes to be “brand-devoted” (that is, have five or more same brand devices) or “brand-obsessed” (defined as having at least five same brand devices) (with 10 or more same brand devices).

Plume pointed out that the “devotion” and “obsession” categories had seen greater advances in the percentage shift between the two time periods. According to Plume, this shows the relative power of the iOS device ecosystem. Apple acquired 24% more homes with 10 or more devices, and Samsung and Amazon followed suit with 17% and 18%, respectively.

Secure by Design: Keeping IoT security in mind all down the line

Plume also noted an important upward trend in the average number of cyber threats successfully prevented. According to the report, over the first half of the year, the average number of cyber threats Plume has successfully prevented increased dramatically (by 51%) internationally. Threats against botnets were found to have increased by 84%, with malware threats increasing by 58% and spyware and adware threats increasing by 40%. Plume hypothesized that the Russo-Ukrainian War might be to blame for these tendencies.

The artificial intelligence of things (AIoT) is a great example of technology convergence. AIoT integrates artificial intelligence (AI) technologies and the internet of things (IoT) infrastructure. The primary goal of AIoT is to make IoT operations more efficient, enhance human-machine communication, and boost data management and analysis.

What is AIoT: A game-changing convergence

AIoT is a game-changing convergence that has the potential to create enormous value for both AI and IoT industries. AI adds value to IoT through integration, signaling, and data sharing. Through integration, signaling, and data exchange, AI improves IoT’s worth, whereas IoT enhances AI’s worth by providing connectivity, signaling, and data sharing. AIoT may help companies improve their operations and services by generating more value from IoT-generated information. AI is a computational approach that can dramatically improve the capabilities of IoT devices by allowing them to use big data to analyze, learn, and make decisions without the need for human intervention.

The core principles of AIoT

AI may be found in the infrastructure components of AIoT devices, such as programs and chipsets. IoT networks are used to connect all these pieces together. APIs are then utilized to ensure that any hardware, software, or platform components can interact and communicate with one another without the need for the user to intervene.

IoT devices collect data, and then AI analyzes it to provide insights and improve efficiency and production when operational. Data learning is one of AI’s methods for extracting insights. Analytics can also occur at the edge, which means IoT data may be processed as soon as possible, minimizing bandwidth usage and avoiding potential data analysis delays.

IoT

Internet of Things (IoT) refers to the concept that everything is becoming connected via the internet. This includes a network of objects and devices with sensors, software, and other technologies that may communicate and exchange data with other devices over the internet. Smart locks, cameras, mobile phones, medical devices, and other gadgets are just a few items that fall under the IoT umbrella. There are currently around 30 billion IoT-connected devices on the market, with that number expected to rise to around 75 billion connected objects by 2025. These things have a significant societal function and will continue to play an even bigger role when combined with AI.

AI

The other half of AIoT is artificial intelligence. Artificial intelligence involves employing computers to perform tasks previously done only by people. This implies using algorithms to categorize, analyze, and predict outcomes from data. It also includes reacting to information, learning from new data, and improving over time. Machine learning, deep learning, and natural language processing are the most important AI technologies. Chatbots, face recognition, identification, autocorrection, digital assistants, and search recommendations are just a few examples of how humans employ AI regularly.

How is AIoT used today?

There are several examples of AIoT used in many sectors. Many business office buildings apply sensor technology to help them save money on energy and electricity expenses. These sensors can tell whether anybody is present and adjust the temperature and lighting levels accordingly. In the business world, sensors and smart cameras may assist with office security. Smart cameras can identify employees based on real-time data and pictures, allowing only authorized people entry to the building. The retail sector is seeing the advantages of AIoT too. Shoplifting might be prevented and deterred by smart security cameras. The cameras can identify faces and track repeat offenders, much like office buildings.

The AIoT is used in self-driving cars. The AIoT utilizes a combination of radar sensors – both inside and outside the car and GPS and cameras – to obtain information on driving conditions, obstacles, and what other vehicles do. The AI algorithm can then utilize the data obtained from the sensors to make judgments.

Smart cities are becoming more popular, with more and more people flocking to cities and living in urban areas. Because of this, smart cities are growing increasingly fashionable. With the ever-growing number of people living in cities, traffic has become a major issue. Traffic monitoring and alerting based on real-time data can help to reduce congestion. Sensors may be placed at chokepoints to detect traffic flow. AI can then use the information given to decide, such as redirecting traffic, changing speed limits, or changing signal lights, depending on the situation.

Benefits of AIoT

The tangible benefits AIoT will bring to organizations can vary from application scenarios to the setup where the technology is deployed. However, it is possible to discuss a few widespread benefits that AIoT deployments promise to institutions.

Real-time monitoring of assets and employees is critical, especially in sectors where equipment failure might be costly or even deadly. This enables constant oversight of all assets, including devices, modules, and personnel, and the ability to take appropriate action when things don’t go according to plan.

AIoT’s seamless predictive maintenance is one of its biggest advantages. Machines in a smart factory, for example, will be able to recognize when they need servicing themselves and can therefore be recalled before disaster strikes. The expensive trouble of unexpected machine failure may soon become a thing of the past.

AIoT increases system scalability in an IoT ecosystem by enabling the addition of more connected devices and optimizing current processes to work with them. The data gathering process becomes extremely precise, as the user receives only relevant data.

Companies use predictive analytics to anticipate potential risks and shield themselves with anticipatory actions. Before a probable event, such as equipment failure, cyber assaults, or workplace accidents, rapid response procedures can be used to defend against possible occurrences.

Patterns may be identified from data with AI in IoT to provide insights that would otherwise go unnoticed. Predictive and preventative analytics can be used to detect faulty machinery or pain spots, which will increase efficiency and employee happiness. Industrial automation systems can become proactive rather than reactionary after being automated by AIoT.

While everyone understands the ever-changing nature of consumer behavior and how difficult it is to provide excellent client service consistently, no one knows what AIoT can do. AIoT assists by providing additional data points that allow analysts to get a more comprehensive view of customer requirements and behaviors. AIoT’s potential is enormous. From determining what sells and fails to comprehend stringent market requirements, research like this provides useful insights for companies, resulting in significant revenue increases.

The healthcare industry, like many sectors, is undergoing a substantial data-driven transformation. New technologies like telehealth platforms and the internet of things (IoT) generate more granular medical data and make it more accessible. While this has many benefits, it also raises considerable healthcare data security concerns.

There were 714 healthcare data breaches of 500 or more records in 2021, almost doubling 2018’s figure. Personal health information (PHI) is highly sensitive, making it a tempting target for cybercriminals. As the industry becomes increasingly data-centric and embraces new data-sharing technologies, security must evolve alongside it.

Here’s a closer look at the future of healthcare data security.

Changing regulatory landscape

One of the most substantial changes taking place is an evolving regulatory landscape. Laws like HIPAA provide little specific guidance for today’s data transfer and security needs, so new legislation will likely replace or amend them. Data professionals in the sector must prepare to adapt to these changing regulations.

The Trusted Exchange Framework and the Common Agreement (TEFCA) is one such new regulation. While TEFCA is a non-binding agreement, many healthcare organizations will likely join it to enable easier cross-country medical data sharing. Participants’ data workers must then ensure their processes don’t fall under new definitions for information blocking and meet TEFCA’s security standards.

Even regulations that aren’t necessarily about security will impact data privacy considerations. The No Surprises Act, which applies to virtually all health plans in 2022, prohibits billing for emergency services by out-of-network providers. This will likely require more remote data sharing, which data professionals must ensure is secure.

Increased patient access and control

Another trend that’s reshaping healthcare data security is increasing patient access. Consumers demand more transparency and control over their medical information, and technologies like telehealth provide it. Balancing this accessibility with privacy may prove challenging.

Limiting access privileges is crucial in data security, so expanding access to patients who may lack thorough cybersecurity awareness raises concerns. Basic human error accounted for 31% of all healthcare data breaches in 2019, and medical organizations can’t train consumers as they can employees. Therefore, data professionals must design a data access platform that accounts for users who will likely make mistakes.

By default, medical apps and consumer IoT devices should enable security measures like two-factor authentication and encryption. Teams can also lean into increasing user control by informing users of relevant security concerns and letting them choose how these apps use their data.

The rise of synthetic data

Machine learning is also gaining rising prominence in healthcare applications. Intelligent algorithms can help make faster and more accurate diagnoses and enable hyper-individualized healthcare, but training them poses a problem. Data scientists must ensure they don’t accidentally expose sensitive medical information while building these models.

The answer lies in synthetic data. Using this artificially generated information instead of real-world PII eliminates the risk of accidental exposure during training. The Office of the National Coordinator for Health Information Technology (ONC) has recognized this need, leading to the creation of Synthea this year.

Synthea is a healthcare data engine that generates synthetic medical records based on publicly available health information. Similar resources could arise in the near future, too. As machine learning in healthcare rises, data scientists must embrace these tools to train models on synthetic data instead of the riskier but potentially more relevant real-world PII.

Healthcare data security is evolving

The rise of data-centric technologies and processes presents both a boon and a challenge for data professionals. This evolution in industries like healthcare offers new, promising business opportunities, but it comes with rising security concerns. As data scientists help the sector capitalize on digital data, they must ensure they don’t increase cyber vulnerabilities.

These three trends represent some of the most significant changes in the future of healthcare data security. Data professionals must monitor these developments to adapt as necessary, providing optimal value while improving safety and compliance.

Cisco coined fog computing to describe extending cloud computing to the enterprise’s edge. It’s a decentralized computing platform in which data, computation, storage, and applications are stored somewhere between the data source and the cloud.

What is fog computing?

The cloud is connected to the physical host via a network connection in fog computing. The storage capacity, computational power, data, and applications are located in this middle space. These functionalities focus on the host, place close to it, and make processing faster as it is done close to where data is created.

Fog, like edge computing, brings the benefits and power of the cloud closer to where data is generated and utilized. Many people confuse fog and edge computing since both imply bringing smarts and processing closer to the data’s source. This is frequently done to enhance productivity, but it can also be used for security and regulatory motivations.

The origins of fog computing

The term fog computing was coined by one of Cisco’s product line managers, Ginny Nichols, in 2014. As we know from meteorology, fog describes clouds close to the ground. This computing method is called “fog” because it focuses on the network’s edge. After fog computing gained traction, IBM coined a similar term for a similar computing method, edge computing.

The OpenFog Consortium was formed as a joint venture between Cisco, Microsoft, Dell, Intel, Arm, and Princeton University. Other organizations that contributed to the consortium include General Electric (GE), Foxconn Technology Group, and Hitachi. The primary objectives of the consortium were to both promote and standardize fog computing. The OpenFog Consortium merged with the Industrial Internet Consortium (IIC) in 2019.

How does fog computing work?

Fog computing is not a substitute for cloud computing; it works in tandem with cloud technology. Although fog networking complements cloud processing, it does not entirely replace it. Edge analytics is possible using fogging, but the cloud performs resource-intensive, longer-term analyses.

Edge devices and sensors collect data, but they sometimes lack the compute and storage capabilities to execute sophisticated analytics and machine learning algorithms. However, cloud servers are generally too far away to handle the data and respond promptly.

Furthermore, having all endpoints connected to and delivering raw data to the cloud over the internet may have privacy, security, and legal implications, especially when dealing with sensitive data subject to various country regulations. Smart grids, smart cities, smart buildings, vehicle networks, and software-defined networking are just a few popular fog computing systems.

What is the difference between fog computing vs edge computing?

Surprisingly, fog computing doesn’t aim to replace edge computing either. According to the OpenFog Consortium of Cisco, fundamental differences exist between these two methods. The intelligence and processing power location distinguish edge computing technology from fog computing. Intelligence is at the local network (LAN) in foggy environments with little visibility. Data is sent from endpoints to a fog gateway, which transmits it to sources for processing and returns transmission.

In edge computing, intelligence and power can exist in either the endpoints or gateways. Edge computing is said to have several advantages, one of which is that it eliminates points of failure because each device independently executes and determines which data to store locally and which data to send to a gateway or the cloud for further analysis.

Both methods identify the other as a subtype of themselves

Some defend that fog computing over edge computing is more scalable and provides a better big-picture perspective of the network as data from numerous sources is integrated. However, many network engineers argue that fog computing is simply a Cisco brand for one form of edge computing.

The OpenFog Consortium, on the other hand, defines edge computing as a component or a subset of fog computing. Consider fog computing to be how data is handled from its inception to its final storage location. Edge computing entails processing data as close to its creation as possible. Fog computing refers to everything from the network connections that bring data from the edge to its endpoint to the edge processing itself.

Benefits of fog computing

Fog computing platforms provide organizations with more options for data processing wherever it is most efficient to do so. For specific purposes, such as in a manufacturing scenario, when connected devices must be able to react to an emergency as soon as possible, data needs to be processed as quickly as feasible.

Fog computing enables low-latency networking connections between devices and analytics endpoints. The architecture minimizes bandwidth requirements compared to if that data had to be transferred back to a data center or cloud for analysis. Organizations can also utilize it when there is no bandwidth connection to send data, so it must be handled promptly. Users can utilize fog networks to create security functions such as segmented network traffic, virtual firewalls, and more.

Use cases of fog computing

The field of fog computing is still in its nascent phases. Still, there are a variety of possibilities for utilizing it. It has been shown that fog computing can be used for various tasks.

The rise of semi-autonomous and self-driving cars will only exacerbate the massive amount of data created by automobiles today. To operate autonomous cars effectively, you need the ability to evaluate certain data in real-time, such as weather, driving conditions, and instructions. Other data may be needed to help improve vehicle maintenance or monitor vehicle use. A fog computing environment would allow these data sources’ communications to occur both at the edge (in the vehicle) and the endpoint (the manufacturer).

Utility systems are also increasingly using real-time data to run processes efficiently. Because this data is frequently located in remote areas, it must be processed near where it was generated. Other times, the data must be aggregated from many sensors. Both of these difficulties can be addressed by fog and edge computing architectures.

From manufacturing systems that must be able to react to events as they occur to financial institutions that use real-time data to guide trading decisions or detect fraud. Fog computing solutions may help make data transfers easier by connecting places generated with destinations where it needs to go.

How does fog computing affect the Internet of Things?

Fog computing is frequently employed in IoT applications since cloud computing isn’t suitable for many. The distributed approach addresses the demands of IoT and industrial IoT (IIoT) as well as the massive amounts of data generated by smart sensors and IoT devices, which would be costly and time-consuming to send to the cloud for processing and analysis. IoT systems require a lot of data to function correctly, so there’s a significant amount of traffic on the network. The fog computing approach reduces bandwidth consumption and back-and-forth communication between devices and the cloud, lowering IoT performance.

What does 5G connectivity mean for fog computing?

Fog computing is a type of architecture in which data from IoT devices is transmitted via a network of nodes in real-time. The information gathered by distributed sensors is usually processed at the sensor node, with a millisecond response time. The nodes send analytical summary data to the cloud regularly. That isn’t all. The data from the various nodes is then processed in cloud-based software, aiming to offer practical information.

Fog computing needs more than just computing functions. It necessitates the fast transfer of data between IoT devices and nodes. The aim is to be able to process data in milliseconds. Of course, different connectivity choices are available depending on the scenario. A connected factory floor sensor, for example, may require a wired connection. On the other hand, a mobile resource, such as an autonomous car or a wind turbine in the middle of a field, will necessitate another kind of connection. The 5G is a compelling wireless technology that offers gigabit connectivity, crucial for data analysis in near-real-time.

IoT security is a subset of information technology that focuses on securing connected devices and internet of things networks. When bad actors search for IoT security flaws, they have a high probability of hacking vulnerable devices. Industrial and equipment connected to them robots have also been hacked. Hackers can alter control-loop settings, interfere with manufacturing logic, and change the robot’s status of those devices.

While the Internet of Things revolution benefits manufacturers and consumers, it also comes with significant security concerns. As more devices are connected, the difficulty of securing them all increases dramatically. IoT devices require physical security, software, and network integrity to function correctly. Any connected object, from refrigerators to industrial robots, can be hacked without end-to-end security mechanisms.

What is IoT security?

IoT security refers to the various techniques used to secure connected devices. The term “Internet of Things” is comprehensive. With technology continuing to advance, the term has only grown more so. Today, almost every technological device can connect to the internet or other gadgets, from timepieces to thermostats, refrigerators, and video game consoles. IoT security is a collection of methods, tactics, and tools for securing these devices from being hacked.

IoT security is much more extensive than just protecting the Internet of Things devices. This has led to many IoT security solutions falling under the category. API security, public key infrastructure authentication, and network security are just a few methods that IT executives may utilize to combat the increasing danger of cybercrime and terrorism based on insecure IoT devices.

IoT Security by design

Security by design is a way to ensure that security is a primary consideration at every stage of product development and deployment. By keeping security in mind from the start, you can deliver a secure application or system. Products developed with this approach are called “secure by design.”

Security by design entails building security into software and hardware from the ground up rather than as a post-hacking measure. As technology firms continue to produce a slew of IoT goods for customers and businesses, the need for security by design has never been more critical. Because these internet of things gadgets are linked to the internet, they are vulnerable to remote hacking. Furthermore, most of these gadgets were built without any security measures, making them ideal targets for hackers.

Historically, security requirements in hardware deployments and IoT design instances used to be postponed to late phases of development processes. The secure by design approach changes this by favoring security in every development phase, instead prioritizing speed to market.

The security by design approach requires that IoT security be addressed initially. Devices must be secured in the proper location and at the appropriate level to meet each implementation’s requirements.

A secure IoT architecture must start with security design. Secure data encryption, digital signatures of messages, and over-the-air device and security updates require pre-embedded identifiers and encryption keys.

During the design process, security by design strategy applies to establishing a solid foundation of trusted digital device identifiers and credentials securely stored in the foundations of devices. Device cloning, data falsification, theft, or misuse can all be prevented with secure credentials. Organizations can protect extra sensitive IoT applications against physical and digital access attempts by storing IDs and credentials in tamper-resistant bodies.

IoT security challenges

IoT security is an issue for businesses since the devices they deploy are likely to have several security flaws. IoT devices are not always running the most up-to-date version of their operating systems, which implies that the IoT device’s operating systems may contain known vulnerabilities that attackers can use to control or damage these IoT devices.

• IoT devices rarely come with built-in security mechanisms and tools. Because of this, the attacker has an excellent chance of infecting the devices with malware that allows them to use them in an attack or access sensitive data collected and processed by IoT devices.
• Even those designed to be secure and safe, every software must be maintained with updates to function securely or adequately. The unique deployment problems of IoT devices make it unlikely that they will receive regular upgrades. These security gaps make the devices highly vulnerable to targeted attempts.
• IoT devices face several password-related difficulties. Manufacturers frequently set default passwords for their devices, but users do not change them before or after installation. Manufacturers also embed hardcoded passwords in their systems that users cannot modify. The weak passwords used on these IoT devices put them at significant risk. Attackers can just log in to these systems with little effort using these easily guessed passwords or simple brute-force attacks.
• IoT devices are frequently built to be placed in public and remote areas where a hacker may gain physical access to them. This physical access might enable the intruder to go around existing security measures within the device.
• Specific network protocols have been classified as no longer recommended. because of their lack of built-in security. However, IoT devices are notorious for utilizing these unsecured protocols, putting their data and privacy at risk. IoT security is a crucial element of any organization’s cybersecurity strategy since all these threats represent significant risks.

Common cyberattacks targeted against IoT devices

Due to the popularity of these gadgets being put on business networks, IoT devices pose a significant risk to enterprise cybersecurity. These devices are frequently vulnerable to attacks. Cybercriminals have used these flaws to launch various typical assaults on IoT devices. The common IoT attacks are direct exploitation, botnets, and data breaches.

Printers and scanners are common access points to an organization’s network for hackers. Since everyone needs to be able to use the printer, these devices are rarely protected by firewalls and frequently have exceptional permissions. Attackers may use this to gain initial access to a network via the printer, subsequently expanding their access via the corporate network.

IoT devices are computers linked to the internet, allowing them to be used for automated assaults. Hackers might utilize an IoT device to launch Distributed Denial of Service (DDoS) attacks, attempt to obtain unlawful entry to user accounts via credential stuffing, spread ransomware or other malware, or take various harmful actions against an organization’s systems if a botnet has compromised it.

Sensitive data, significant operations, and cloud subscription services are all common in IoT devices, making them a significant target for hackers. For example, accessing connected cameras or cloud services might allow attackers to obtain potentially sensitive data or other valuable information.

An automation engineer’s day-to-day tasks include designing and implementing technological processes that automate various activities. Automation technology is currently being used in various business, IT, and development processes, which has prompted organizations to seek these professionals to create, test, and implement automation technologies.

Automation is the use of data from various sources to streamline or enhance a system or process. By ensuring their operations are as efficient as possible while maintaining a high-quality output, automation engineering help businesses function more efficiently.

This is a field divided into two main branches. The first is run from a traditional engineering perspective and develops automated solutions for physical activities. On the other hand, modern version automates digital processes with software engineering.

What is an automation engineer?

An automation engineer is a skilled professional who uses technology to enhance, streamline, and automate processes. They are in charge of developing, putting into action, and monitoring such technologies. These engineers are employed in a variety of industries. Mechanical and computer automation are the two most prevalent varieties.

Is automation engineer a developer?

An automation engineer is an engineer that specializes in automating business operations. They use their skills to automate business processes carried out in various settings with software and robotics, resulting in increased productivity. Automation engineers generally need a degree in engineering. Most professionals enter the field via a mechanical, electrical, or software engineering curriculum. Technical knowledge may be acquired on the job for the most part.

What does an Automation Engineer do?

Automation engineers design, program, simulate and test automated machinery and processes. They’re generally found in industries like energy plants, automobile manufacturing facilities, food processing plants, and other environments utilizing robotics.

Automation engineers create detailed design specifications and automation based on precise needs for the process involved, adhere to worldwide and regional standards, process specific norms and rules.

Automation engineers may work in various settings and have a variety of duties. Depending on the environment, they may undertake tasks such as:

• Creating an automated work or manufacturing environment,
• Programming chatbots to answer inbound calls,
• Creating a system for IT support tickets processing and efficiently allocating them,
• Determining whether it’s necessary to automate specific steps in a process to minimize faults,
• Identifying and resolving problems in procedures with the least amount of downtime feasible,
• Installing and upgrading software, databases, and other solutions to improve efficiency.

Is automation a good career?

Automation engineering is a promising career for someone with the technical skills and desire to pursue a career in a technological field. Automation is a fast-paced industry in both technology and manufacturing. As technology advances, more and more activities are anticipated to be automated. As a result, the need for automation experts is likely to increase. Automated engineers command higher salaries than other IT workers, suggesting that the job is highly demanded.

Automation engineer salary (2022)

Automation engineers’ salaries in the US range from $40,000 to $228,000, with a median salary of $92,000. The middle 57% of these engineers make between $92,000 and $135,000 each year, with the top 86% earning an annual salary of $228,00.

What is industrial automation?

Industrial automation is integrating computerized machines, control systems, or other information technologies into business processes to perform work done by humans. Industrial automation uses both hardware and software to streamline mainly labor-intensive physical processes. It is widely used in smart factories and warehouses in production environments to facilitate production, assembly, and material handling.

What skills are required for IT automation?

Scripting, collaboration, source-code management, Kubernetes, security, testing, observability, monitoring, and network awareness are the minimal viable skills for IT automation.

Automation engineers require a wide range of technical and soft skills. They grasp the systems, networks, hardware, and software they are dealing with and can collaborate with other business units, clients, or customers. However, the languages and tools necessary for this position differ by sector.

Automation engineers need a comprehensive understanding of mobile, web, and desktop operating systems, alongside analytics, robotics, AI, and machine learning. Leadership abilities are also crucial since they must lead cross-departmental initiatives to simplify business procedures.

Internet of Things (IoT) solves the critical problems of many sectors, from production to health, from transportation to logistics. However, the increasing security risks for IoT networks require caution when taking advantage of connected devices.

Interconnected IoT objects are not the same devices, objects, or services. Each object has a different purpose, interface, operating mechanism, and underlying technology. Given this diversity, applying a single security structure and approach for all objects is not enough to provide the security needed for IoT networks. IoT security initiatives protect IoT devices connected over a network with preventive methods and aim to prevent large-scale cyber-attacks that can be carried out over them. Like any other computing device, IoT devices are potential entry points for attackers to breach a company’s network. Therefore, robust security measures are needed to protect them.

Today, the scope of IoT has expanded to include traditional industrial machines, equipping them with the ability to connect and communicate with a network. You can see that IoT technologies are now used in medical devices or for various purposes such as education, manufacturing, business development, and communication. Increasing use cases make the security of IoT networks more critical than ever before. According to the Gartner, 61 percent of companies’ IoT networks and strategies show a high level of maturity.

IoT devices can connect to a network or the Internet to exchange data with other connected objects or centers. These devices are not limited to smart TVs or smartwatches. Printers, washing machines, air conditioners, smart sensors, and other industrial machines connected to networks are also IoT devices. The way IoT is implemented today requires institutions and organizations to have ecosystems consisting of many different devices. It is crucial to utilize a combination of IoT security solutions, strategies, and techniques rather than traditional approaches to ensure the security of this ecosystem.

Security tips for IoT networks

Companies can take a few main measures to ensure the security of their IoT networks. These include using authorized software on IoT devices and authenticating an IoT device on the network before collecting or sending data. Because they have limited computational capability and memory, it is necessary to set up firewalls to filter packets sent to IoT endpoints.

On the other hand, you should also ensure that updates and patches are installed without consuming additional bandwidth. In addition to the general security measures above, we recommend that you consider some unique security approaches when planning the security of IoT devices. In addition to device and network security, you also need to ensure the physical safety of the overall IoT and communications infrastructure.

You can adopt the following security approaches to secure IoT devices:

• Ensure physical security: Keep IoT devices relatively isolated and protected from physical access.

• Deploy tamper-proof devices: Use tamper-proof IoT devices. These devices deactivate themselves when tampered with.

• Keep firmware up-to-date: Be proactive in applying updates and patches to your devices as soon as manufacturers release them.

• Run dynamic tests: Run tests to uncover hardware code weaknesses and vulnerabilities.

• Set device replacement procedures: Set procedures for replacing IoT devices when they become obsolete. Carelessly discarded or discarded devices can pose a threat to corporate data and serve a variety of malicious purposes that harm your organization.

• Use strong authentication: Avoid default passwords that pose a password hacking threat. Use complex passwords for authentication and update them periodically.

• Leverage adaptive authentication: Adaptive authentication, or context-sensitive authentication (CAA), uses contextual information and machine learning algorithms to assess malicious intent. In this way, users are asked to perform two-factor authentication in scenarios that are perceived as high risk.

• Implement strong encryption and protocols: Allocate secure data transfer media using strong encryption on Bluetooth, Zigbee, Z-Wave, Thread, Wi-Fi, cellular, 6LoWPAN, NFC, and similar IoT protocols.

• Limit device bandwidth: Limit network capacity and bandwidth to the lowest possible value, sufficient for device operation but not usable in IoT-based distributed denial-of-service (DDoS) attacks.

• Segment the network: Divide your network into smaller local IoT networks using virtual local area networks (VLANs), IP address ranges, and a combination of these. This partitioning process allows you to create different security zones and specify different segments controlled by firewalls.

• Protect sensitive information: Prevent sensitive personally identifiable information (PII) leaks by restricting the discovery of IoT devices. Require authorized clients to implement appropriate service mechanisms and authentication protocols to discover the IoT device.

Nanomachines, also known as molecular machines or nanites, are molecular robots not larger than a strand of human hair. They can be programmed to carry out tasks in biological systems. Biologists frequently use these molecular machines to perform DNA replication and ATP synthesis tasks. Nanorobotics is one of the most promising emerging fields. The continuous investments in this area resulted in the development of even smaller and more capable nanites, actively used for life-saving and enhancing tasks.

What is a nanomachine?

The smallest being virus-sized, nanomachines are orders of magnitude smaller than a human cell, which is usually measured in micrometers (one-millionth of a meter). Researchers and engineers have turned to natural biological technology for inspiration while developing nanorobots since most robotic construction techniques would be impossible at this scale. We already have billions of organic nanobots inside us all the time, powering the many functions of our cells. Ribosomes, for example, are organic versions of biological machines at the nanoscale.

Nanites aren’t your typical mechanical robots. They are not constructed of metals or other materials that spring to mind when you think about a robot. Instead, nanomachines are built from DNA or other biological materials that seamlessly interact with biologic environments in specific ways to accomplish certain results.

How do nanobots work?

The health sector is where nanobots are primarily used. However, they’re utilized in various sectors, including climate control and the military. Medical applications include healing wounds, atomic-scale surgical equipment, and going through the body to discover and treat problems. They can also decrease toxicity and extend the drug’s sustained release period.

Types of nanites

Today’s molecular machines operate due to external stimuli, such as chemical reactions, temperature changes, or radio waves. Nanobots can be seen in many different forms:

• Switch type nanomachines using things like temperature, UV light, or chemical reactions to change from an off position to an on through the process of conformational changes.
• Nanomotors use nanotechnology to move and control molecules in the surrounding environment. Nanomotors can utilize the energy created by the conformational change to produce physical movement in neighboring molecules, making the nanomotor more complex than the nanorobotic switch.
• Nanorobotic shuttles are machines that transport specific drugs or chemicals to a certain destination. Scientists are attempting to connect these with nanomotors to control their movement in biological environments more precisely.
• Nanorobotic cars are the most advanced nanodevices to date. These machines look and work like regular cars, but they are tiny to operate in biological environments. The four-wheeler can move with light or chemistry. Scientists are still working on better controlling these devices, and light seems to be the answer.

The goal of these various nanorobotic components is to build collective nanomachines that collaborate to achieve goals on a macro level. In a manner comparable to an ant colony, a group of nanomachines can move things or overcome obstacles that would be impossible for a single individual. Collective nanomachines will perform tasks far beyond the capabilities of even the most complex components we have now.

Nanomachines in health and medicine

Organisms are predictable closed systems. Scientists have been able to predict how compounds will react once introduced into the system and build tiny gadgets that can execute complex activities undetectable to the naked eye.

Nanomachines can disrupt the cellular barrier of a tumor, causing an increase in permeability and retention (EPR effect) through the vascular endothelial cell gap. This action is targeted at the detection of cancer at the cellular level. The long reach paired with the ability to pass through several anatomic barriers and films allows for greater drug efficacy for current medications. The EPR function is beneficial for medical imaging since it uses magnetic or contrast nanorobots that may be easily guided to the tissue or structure of interest to enhance existing imaging technology.

Researchers have already built tiny robots powered by the human body that can store data, detect their surroundings, and carry out computations. Autonomous DNA nanomachines can perform biological activities in live cells, such as detecting a particular microRNA sequence found in breast cancer cells. As some nanites may recognize breast cancer cells in trace amounts, it is expected that they will be able to find target molecules missed by other methods in the future.

What is DNA origami?

DNA origami is a popular nanotechnology method with a wide range of applications. It refers to the practice of building DNA strands into specific two- and three-dimensional shapes through annealing templates that include hundreds of DNA strands. DNA origami is a popular nanotechnology method with a wide range of applications. It refers to the practice of building DNA strands into specific two- and three-dimensional shapes through annealing templates that include hundreds of DNA strands. This approach is typically used to engineer cancer-fighting nanobots. However, this technique is only marginally more accurate than standard chemotherapy. The nanobots are challenging to control once they enter a living creature and are too tiny to be detected using conventional X-ray equipment.

Future of nanomachines

Nanotechnology is expected to provide us with extraordinary new augmented abilities, with molecular machines allowing us to sense and interact with our surroundings in ways that haven’t been possible. Futurist Ray Kurzweil predicted in 2005 that nanotechnology will enable humans to live forever by 2040, giving us superhuman powers. He believes nanobots might replace native blood cells and back up memories while also replenishing aging cells, essentially curing dementia.

Similarly, Dr. Robert Freitas, a nanotechnology expert at the University of Texas at Austin’s College of Pharmacy and Technology, claimed that by 2050, these tiny robots will organize our blood supply. They’ll be able to repair wounds or even cause new tissue growth in parts of the body where large veins have been blocked off.

The application of nanobots to consumer technology has also been discussed, such as a smart window. That idea would take the form of windows coated with nanobots that can automatically clean the glass and regulate the room temperature for greater energy efficiency. But then again, these are still early days for such a technology.

Most researchers and experts think nanorobotic applications will be incorporated into everyday social activities by the 2030s. Researchers must address problems such as communication, swarm behavior, mass production, biocompatibility, biodegradability, and control of nanorobots in deep tissues to realize the nanomachine revolution.

Foot traffic is one of the most helpful types of data for brick-and-mortar businesses to collect. Tracking how many people enter, where they go and when they leave helps understand customer behavior, assess performance, and optimize store layouts.

Businesses today have a wide array of technologies to choose from to collect foot traffic data. However, this wasn’t always the case. Monitoring foot traffic is an old practice, far outdating digital data itself, and many of its most radical innovations are fairly recent.

Manual Counting

The oldest form of collecting foot traffic information is the same as most data collection forms: manual entry. Mechanical counting tools emerged as early as the nineteenth century, with several inventors seeking patents for simple counting devices in the mid-1800s.

These handheld tools provided a more reliable measurement than counting in your head, but they still rely on manual operation. They’ll only record another count if you press the button. Still, these devices’ simplicity has helped them remain popular today, with stores placing employees with a hand counter by the door to determine occupancy.

Cameras

Foot traffic tracking transitioned to digital data with the advent of digital cameras. Using camera data to monitor people who enter, leave and move around a space removed the need for manual tracking. These records also provide context for foot traffic, not just simple occupancy figures.

Camera data can still be a helpful resource today with the help of machine vision. Amid the COVID-19 pandemic, businesses discovered they could monitor social distancing with machine vision algorithms that analyze video footage. Similar systems can analyze this data to determine customer behavior, like how they interact with various displays.  

Infrared Sensors

A more streamlined approach to collecting foot traffic data is with infrared sensors. These systems use an infrared beam to register customer movements, counting each time the beam breaks from someone passing through it. More advanced versions can even determine the direction of travel, showing if someone is entering or exiting.

Infrared data can provide real-time, reliable information, and it’s often affordable to implement. They also don’t capture people’s likeness like cameras do, which helps protect customer privacy. However, it doesn’t provide context by itself, so what you can glean from it is limited compared to some more advanced options.

Thermal Sensors

A similar alternative is to use thermal sensors. Instead of using a simple infrared beam, these devices track heat signals to monitor foot traffic. They register each customer’s heat signature as they pass through an area and provide more context than when they enter and leave.

Temperature readings can show where people gather, indicating high-traffic areas that may need reorganization. Businesses can also use them to monitor for unusually high temperatures that could indicate sickness. They can then recommend health testing, inform people of possible disease exposure, or more.

Smart Beacons

Today’s most advanced foot traffic data collection method is the smart beacon. These devices use wireless signals like Bluetooth or Wi-Fi to connect to people’s smartphones. If businesses have beacons throughout an area, they can learn what products customers look at, how they moved throughout the store, and more, not just their location.

Since beacons connect to phones, they can also connect foot traffic data to people’s browsing history and shopping habits in some circumstances. Given this wealth of information and opportunity, it’s clear why experts predict beaconing to be a $25 billion industry by 2024. However, this data does bring more security and privacy risks that businesses must consider.

Foot Traffic Data Collection Has Come a Long Way

Foot traffic data can be a precious resource to retailers and other businesses. As the tools to gather this information become more complex, its potential keeps expanding. With many of these technologies only gaining mainstream appeal within the last ten years, groundbreaking solutions may have yet to emerge.

According to IDC, IoT data is forecasted to reach 73 zettabytes by 2025, while a recent study by Deloitte estimates that 40% of IoT devices will be capable of sharing location in 2025, up from 10% in 2020. This means time and space data is the fastest-growing big data category this decade. 

The next few years will see the geospatial technology industry experience rapid growth and change. More location-aware devices and services will expose the world to how technology can utilize data across time and space. Early adopters that take advantage of this will have a vast market opportunity within their respective industries, while slower organizations will risk getting left behind. The key to being an early adopter will be to understand the following: the trends behind this market opportunity, the need for new analytics technology, and the crucial role of the cloud in leveling the playing field.

Time and Space Data: The Rise of Geospatial Insights and Analytics 

The global geographic information systems (GIS) market will be more than double to $13.6 billion by 2027. Three particular industry trends create this.

1. The cost of sensors and devices that collect geospatial data is falling rapidly.
2. The expansion of 5G networks will accelerate IoT deployments. 
3. The cost of launching satellites is falling on a per-kilogram basis, meaning more satellites will be gathering data with a spatial dimension.

A new breed of analytic geospatial capabilities is becoming widely available in the market, allowing more organizations to begin experimenting with geospatial data and analytics. Opportunities abound across industries such as proximity marketing in retail, smart grid operations management in energy, real-time patient tracking in healthcare, fleet optimization in logistics, and autonomous driving in automotive.   

Out With The Old (Traditional Databases) and In With The New (Vectorization) 

As more organizations begin experimenting with geospatial data and analytics, they must understand the need for new analytics technology to successfully process and analyze massive amounts of data in a fast and reasonable amount of time. The current generation of massive parallel processing (MPP) databases for big data analytics simply weren’t designed to handle the speed, unique data integration requirements, and advanced spatial and temporal analytics on data across time and space. The result is slow decision-making, a lack of critical context, and sub-optimized insight. On top of that, using prior generation databases for spatial and temporal data analytics is expensive due to inherent compute inefficiencies, forcing organizations to explore new approaches and technologies. 

Vectorization, which accelerates analytics exponentially by performing the same operation on different data sets at once for maximum performance and efficiency, is one such approach. This method is particularly adept at functions required to perform advanced calculations on time-series and geospatial data, giving organizations full context and results in seconds where traditional analytics took hours. Early adopters that recognize the ability to analyze and track real-time data through many fused sensors enabled by vectorization will have a vast market opportunity within their respective industries. At the same time, slower organizations will risk getting left behind. The idea of using advanced technology such as vectorization and focusing on data with a spatial component may seem daunting and only relevant for big tech companies. However, like other once-flashy technologies such as containers and blockchain, vectorization could soon be the next “must-have” for every organization in the next few years. 

Yet Another Reason to Move to the Cloud

However, organizations should be wary that properly utilizing the onslaught of geospatial data isn’t something that teams can handle in-house. Traditionally, only the most significant organizations (think Fortune 100’s or government agencies) have had the resources to leverage the advanced computing needs (like vectorization) such as high-end computing processors and primitives from NVIDIA and Intel. Furthermore, companies used those initiatives almost exclusively for deep learning and virtual reality simulation projects, using cases that focused on far-sighted research vs. business objectives.

Organizations that invest in new sensor hardware will rightfully be wary of spending even more funds on advanced chips of their own. Instead, they should turn to major cloud service providers like Microsoft Azure. As-a-service databases are readily available and easily capable of leveraging vectorized computing processors for common big data analytics workloads such as time series analysis, location intelligence, visual scenario planning, and other forms of complex mathematics at a scale that incoming geospatial data will fuel.

The Future of Time and Space Data 

As data across time and space continues to rise, organizations must also ensure they are set up with a database that is designed to process and analyze massive amounts of data in a fast and reasonable amount of time. These two elements will be vital to unlocking opportunities, innovations, and instrumental in organization-wide transformation. 

The power of geospatial data lies in answering “where” questions: Where do organizations have exposure to supply chain or regulatory risk? Where should organizations improve product selections to increase sales? Beyond telling us where things are, analyzing data through the lens of location provides organizations new information to make better-informed decisions and enhance performance. The future for organizations across all industries entails taking advantage of geospatial data capabilities.

The Internet of Things (IoT) is seemingly everywhere these days. You can find IoT devices in connected utility systems, appliances, sensors, security systems, cameras, wearable health devices, and pretty much everything else you can think of.

This is just the start of what’s to come, with the IoT market on pace to grow 25% each year through 2027. Rising consumer demand coupled with connectivity advancements like 5G should lead to massive IoT adoption among businesses and consumers in the coming years, bringing connectivity to just about every conceivable endpoint. 

In the meantime, the industry faces several major hurdles, including bandwidth constraints, a lack of interoperability, and regulatory fragmentation. Yet all of this should improve in time through steady advancements in connectivity, production, and policymaking. 

The real issue that should be keeping IoT stakeholders up at night is consumer trust — or a lack thereof. Research shows more than half of global consumers do not trust connected devices to protect their privacy and handle information in a respectful way. Further, 63% of consumers perceive connected devices as creepy in their behavior and the way they collect data. 

Gaining and keeping consumer trust is integral to increasing user adoption and unlocking the full value and potential of the IoT. The industry is now at a crossroads, and vendors must demonstrate greater transparency and safety. Failure to do so could mean putting themselves at risk of losing market share to competitors that place more emphasis on privacy and protecting consumer data.

Looking beyond security 

In the age of high-profile data breaches, it comes as no surprise that IoT security is now a major point of contention. The issue recently reached the highest level of government in the United States. In November 2020, Congress unanimously passed the IoT Cybersecurity Act, which sets minimum requirements for patching, developing, and configuring IoT devices. 

IoT security again made headlines in January, following an announcement that President Biden would not be able to use his Peloton bike in the White House due to security concerns. The latest research from McAfee justifies this action, citing a security vulnerability in Peloton products that makes them susceptible to cyberattacks.

As important as security is, it’s only half the battle for establishing consumer trust. The other critical piece of the puzzle is privacy. To clarify, security, in this case, refers to protecting data while privacy refers to the process of collecting, using, and accessing it. 

While privacy has long been an afterthought for IoT manufacturers and vendors, the topic is now receiving more mainstream attention from lawmakers and consumers. As a result, providers need to start taking this seriously or risk facing backlash.

On the regulatory front, the U.S. currently lacks a federal privacy program like the EU’s GDPR standard. However, a growing number of states are taking matters into their own hands. California, Virginia, and Colorado each have strict regulatory frameworks protecting consumer data, for example. And Pennsylvania may soon follow suit with HB-1126, or the Consumer Data Privacy Act, which is now under consideration. Some federal lawmakers are also pressuring Congress to pursue a federal privacy agency.

On the other side of the pond, the U.K. is also accelerating efforts to protect consumers from unsecured IoT devices. In fact, the U.K. government is now planning a new law that would hold manufacturers and vendors offering IoT devices more accountable for privacy and security violations, increasing safety measures for consumers. 

Making privacy a differentiating factor

While lawmakers continue to debate and enforce privacy measures, companies also face rising pressure from consumers who increasingly demand transparency and control over their personal information.

In one study, 32% of consumers said they care about privacy enough to switch companies or providers to protect it. Further, 90% believe the way vendors treat customer data reflects the overall way the company treats customers. And in a separate study, 92% of consumers said they want to control what personal information companies automatically collect — and increase punishments for companies that violate their privacy. 

In light of these findings, IoT providers have an excellent opportunity to use privacy as a key differentiating factor. Companies that prioritize and promote privacy stand a much greater chance of displacing competitors who put less emphasis on respecting consumer information. 

As an example, Amazon is prioritizing privacy and security in its new Sidewalk service, which improves connectivity for home devices by connecting local Sidewalk-enabled devices and essentially creating entire smart neighborhoods. It’s a groundbreaking service that helps devices like outdoor lights, motion sensors, and Echo devices work more efficiently over a shared, low-bandwidth network.

Naturally, the product is creating serious privacy concerns, which is why Amazon is heavily promoting safety measures. For example, nobody — Amazon included — can access any of the data that flows between Sidewalk transmissions. The company also uses three layers of encryption and deletes Sidewalk routing data every day. On top of that, rolling IDs add further anonymity for end users.

Consumers naturally remain skeptical about Sidewalk. But by making security and privacy a priority, Amazon stands a better chance of selling customers on privacy and increasing adoption of the fledgling service. 

It’s time to cater to the privacy-conscious consumer

At the end of the day, data privacy is an uncomfortable discussion for vendors and consumers. But in our increasingly digital age, it’s an important one to have — and lead with – as we all have a part to play in becoming better digital citizens.  

Bottom line? Companies that tackle privacy head-on and use it as a differentiating factor stand a much greater chance of earning consumer trust and edging out the competition. 

On the flip side, those that treat privacy as an afterthought might not realize just how wrong that decision is until it’s too late.

Which path will you take?

Smart home devices are used to monitor or control the environment in our homes. These marvels of technology make life easier by handling changes in temperature, lighting, entertainment systems, and other appliances. But while they’re the height of convenience, we can’t ignore the security nightmare being created by their use.

So how smart is it to connect all the appliances, even alarm and security systems, to the internet? We do not have standardized security measures for the devices that are making their way into our homes, but with the convenience they offer, many times that outweighs the application of common sense. With every additional smart device in a home’s network, the system becomes more complex and more at risk. 

The smart home device market has grown immensely, and there are 258 million smart households worldwide. However, 40.8% of these households have at least one smart device vulnerable to cyber attacks. In an increasingly online world, where our homes are the center of our work and private lives, data privacy and security are crucial.

What are smart devices, and when did they come into our lives

We can trace smart devices back to the early 1900s. With the evolution of technology, the definition of what makes a smart device has changed. You could even argue that the very first vacuum cleaner in 1905 was a smart device for its time.

The first device that fits today’s understanding of smart home technology was the Echo IV in 1966. This machine took up enough space to fill an entire room, but it performed most of the features that smart devices today are capable of. Echo IV could control the air conditioning, TV, and keep track of things for you.

Of course, today, we can fit an Echo IV in the palm of our hands, with wireless internet, BlueTooth, cameras, and processors that have 25,000 times the clock speed of that home automation pioneer. Switching on your TV is expected; today, we talk to speakers that do your online shopping, schedule your tasks, and even help conserve resources like electricity and water. 

Where is the security risk?

When looking at convenience and accessibility, smart homes seem to be the obvious answer. You can control your home’s appliances, your locks and alarms, lighting, and heating, all from a single tablet or smartphone. These technologies have proven that they are helpful, and we know that they have become increasingly affordable.

The risk comes in when we realize that our cybersecurity measures have not improved at the same pace. Smart home security systems often have cameras connected to the internet, installed to keep your homes safe but are vulnerable to hackers. The same smart security system can be manipulated by a third party to breach your privacy. 

For example, the use of smart locks on external doors raises many questions. A skilled hacker can easily breach them, and a brilliant one can hide their nefarious activities. And while the common counter-argument is that crooks can pick locks and doors can be broken open anyway, both activities leave physical evidence; something insurance companies typically require before they will payout on a claim. However, that’s changing.

Some insurance companies are offering discounts for consumers with smart security systems. Smart sensors, locks, and thermostats can lower your premiums if your home insurance provider has decided to embrace home automation systems. While there are definitive pros to installing these devices, other than the financial incentive – such as faster fire detection and guest access when you’re unavailable to let people in – you can’t ignore the security issues. The insurance industry has not standardized or decided on its approach, so it’s essential to research this aspect carefully.

Voice assistants by Amazon, Google, Microsoft, and Apple are also risks for our data privacy. They accidentally activate several times a day and record audio (even if you are not directly speaking to the device). The shocker here is that most of the audio that the voice assistant records is stored on company databases. Human workers review these recordings in the process of improving the device.

While the companies make assurances that the recordings are not stored in correlation with the user and that all the voice data is kept confidential, it is disturbing to come to terms with the idea that people listen to what we say to our devices. This is a hole in the privacy of our homes, and it is something to be wary of. 

All the instructions you give your voice assistant, like home address, financial details, and information that may have been accidentally recorded, are stored with the device manufacturer. With the merging of workplace and residence during the pandemic, a significant amount of potentially confidential data is at risk because of these smart devices. Thankfully, there are a few settings that you can change to delete your recordings or opt-out of having a human review your recordings. 

How do we keep our systems secure?

With the overwhelming information indicating that our convenience comes at the cost of our privacy, the next question is how to protect our data. Data privacy and security need to be prioritized as we further delve into the digital space, with information being collected and analyzed from every part of our lives. 

Before buying any smart device, read reviews that focus on the product’s security and what data is recorded and stored. Independent reviews of the product will help understand what the risks are. A few general searches for “smart home security” and “smart device security teardown” will deliver articles that help understand the risks of owning a smart device. 

While in-depth vulnerability services like IoT Inspector and others exist, they focus on organizations at present, so they’re expensive for the average home-owner, but they are also worth considering if the cost is a small percentage of the potential loss. Employing a white hat hacking company is also an option for complex smart home setups. 

If you intend on adding smart devices to your home network, it is vital to use strong passwords and have different passwords for each device. A password manager like Dashlane can come in handy by generating and saving the passwords. This is one step towards securing private information. Another layer of security would be to separate the smart device network from your regular usage network. These steps are a few of the ways that you may take charge of your smart device security. 

Smart homes become more commonplace with time; this is not something that will change. What we do have the power to change is our smart device security. Hopefully, we can move towards a future where convenience and privacy do not come at the cost of the other.

• The DN Unlimited Conference will take place online for the first time this year
• More than 100 speakers from the fields of AI, machine learning, data science, and technology for social impact, including from The New York Times, IBM, Bayer, and Alibaba Cloud
• Fully remote networking opportunities via a virtual hub

The DN Unlimited Conference will take place online for the first time this year.

The Data Natives Conference, Europe’s biggest data science gathering, will take place virtually and invite data scientists, entrepreneurs, corporates, academia, and business innovation leaders to connect on November 18-20, 2020.

The conference’s mission is to connect data experts, inspire them, and let people become part of the equation again. With its digital networking platform, DN Unlimited expects to reach a new record high with 5000+ participants. Visitors can expect keynotes and panels from the industry experts and a unique opportunity to start on new collaborations during networking and matchmaking sessions.

In 2019, the sold-out Data Natives conference gathered over 3000 data, technology professionals and decision-makers from over 30 countries, including 29 sponsors, 45 community and media partners, and 176 speakers.The narrative of DN Unlimited Conference 2020 focuses on assisting the digital transformation of businesses, governments, and communities by offering a fresh perspective on data technologies – from empowering organizations to revamp their business models to shedding light on social inequalities and challenges like Climate Change and Healthcare accessibility.

Data science, new business models and the future of our society

In spring 2020, the Data Natives community of 80.000 data scientists mobilised to tackle the challenges brought by the pandemic – from the shortage of medical equipment to remote care – in a series of Hackcorona and EUvsVirus hackathons. Through the collaboration of governments such as the Greek Ministry for Digital Governance, institutions such as the Charité and experts from all over Europe, over 80 data-driven solutions have been developed. DN Unlimited conference will continue to facilitate similar cooperation.

The current crisis demonstrates that only through collaboration, businesses can thrive. While social isolation may be limiting traditional networking opportunities, we are more equipped than ever before to make connections online.

The ability to connect to people and information instantly is so common now. It’s just the beginning of an era of even more profound transformation. We’re living in a time of monumental change. And as the cloud becomes ambiguous, it’s literally rewriting entire industries

Gretchen O’Hara, Microsoft VP; DN Unlimited & HumanAIze Open Forum speaker.

The crisis has called for a digital realignment from both companies and institutions. Elena Poughia, the Founder of Data Natives, perceives the transformation as follows:

It’s not about deploying new spaces via data or technology – it’s about amplifying human strengths. That’s why we need to continue to connect with each other to pivot and co-create the solutions to the challenges we’re facing. These connections will help us move forward

Elena Poughia, the Founder of Data Natives

The DN Unlimited Conference will bring together data & technology leaders from across the globe – Christopher Wiggins (Chief Data Scientist, The New York Times), Lubomila Jordanova (CEO & Founder, Plan A), Angeli Moeller (Bayer AG, Head Global Data Assets), Jessica Graves (Founder & Chief Data Officer, Sefleuria) and many more will take on the virtual stages to talk about the growing urge for global data literacy, resources for improving social inequality and building a data culture for agile business development. 

On stage among others:

• Erika Cheung, Executive Director, Ethics in Entrepreneurship
• Cory Doctorow, Science Fiction Author, Activist, and Journalist.
• Whurley (William Hurley), Eisenhower Fellow, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE)
• Alistair Croll, Founder, Solve for Interesting
• Clare Jones, Chief Commercial Officer, what3words
• Mark Turrell, Orcasci, CEO
• und many more …

What does the cloud industry has to offer for the year 2020? What are the trends we will see in cloud adoption and cloud-to-cloud migration? How will 5G impact cloud adoption? Read on. 

The global economic outlook has been lukewarm heading into 2020, yet there is much reason for confidence and enthusiasm among those in the cloud industry.  The cloud-services market is a $200 billion industry that’s experienced tremendous growth in recent years, and that growth is expected to continue. Gartner forecasts cloud growth in the range of 20 percent in 2020, since cloud services are integral to the operations of many global businesses. 

Though some market experts are suggesting the U.S.’s dominance in global services is weakening, there still remains significant reliance on cloud services. Hardware and infrastructure are continuing to age – Microsoft has a slew of products reaching end of life in 2020 – and will require upgrades for end users. 

As many businesses look to phase out older hardware, many are opting to migrate to the cloud. Other businesses are experiencing significant global growth. Given this, we believe that reliance on evolving cloud technology will continue in 2020, despite changing political and economic landscapes.

Outlined below are five additional cloud market predictions to consider for 2020 and beyond.

SaaS growth will continue

While the cloud is a $200B market, overall IT spending is in the trillions, meaning much of that spending is devoted to on-premises software and services. As more enterprise leaders adopt cloud services, they have gotten over initial concerns about security and reliability, lending to stronger confidence in the cloud to support their operations. Furthermore, business leaders are moving away from homegrown applications and opting for turnkey solutions that are born in the cloud.

As organizations look to migrate more office-productivity workloads to the cloud, there is still ample technology that can be moved. In 2020, we believe another five to 15 percent of solutions will be cloud-based as companies continue to gain confidence in and reliance on cloud services, retire old on-premises technology, and rely more on SaaS solutions. 

Use of containerization will increase

According to research from Gartner, more than 50 percent of global organizations will be running containerized applications in production by 2020, up from less than 20 percent in 2019. The value propositions around containerization, which allows applications to effectively be written once and run anywhere, are unmistakable. Containerization offers a multitude of benefits to business and IT leaders looking to leverage the cloud. 

Containers allow for easy provisioning of storage and network resources. They allow businesses to bypass building servers, procurement, purchase orders, installation, configuration and security concerns – all of which create opportunities for error. 

By leveraging containers, businesses can define environments by configuring one file, which can then be automated and replicated in a matter of minutes. Containers effectively de-escalate risks during migrations and remove reliance on manual configuration, offering a streamlined migration process. Containerization also helps enterprises reduce costs that are typically associated with managing physical, on-premises network infrastructure. 

As more enterprises become aware of the benefits of containers in 2020, we expect to see an increase in their use.

Cloud-to-cloud migrations will rise

A common hurdle to cloud adoption is vendor lock-in, or the feeling of surrendering control over your data to a single vendor, as opposed to having direct access to physical data onsite. In addition, businesses often don’t want to be dependent on a sole vendor because it limits their ability to negotiate on price and flexibility. Dependency on one vendor can pose a predicament if there is an issue with that vendor’s service. 

To minimize these risks, many enterprises choose to leverage multi-cloud environments, which can help with managing rates and leveraging preferred services. In fact, on average, most organizations leverage five different cloud platforms. We expect this trend will continue, leading to more cloud-to-cloud migrations as businesses reach agreements with multiple vendors in their quest for the optimal digital environment. 

AWS will lose market share to its public-cloud rivals

Amazon has become a household name in many ways and for many reasons. Amazon’s early adoption and implementation of cloud services has positioned it as a frontrunner, accounting for nearly half of the market share. Amazon’s growth and revenue have been astounding, but its high-speed revenue growth rate is not sustainable. Additionally, with public-cloud competitors growing 60 percent year-over-year, AWS will see more competition from the likes of Google Cloud and Microsoft Azure. Google provides end users with technology that is effective, accessible and easily operated, while Microsoft remains trusted among enterprise organizations for the migration of legacy environments. In 2020, AWS will continue to experience growth, but on a lesser level, and companies like Google Cloud and Microsoft Azure will decrease the gap between AWS and its competitors. 

5G will impact global cloud adoption

5G subscriptions are on the rise and according to Ericsson Mobility, there will be 2.6 billion subscriptions within the next six years, accounting for almost 45 percent of the entire global mobile data traffic. In addition, 5G will cover up to 65 percent of the global population by the end of 2025. 

The impact of 5G will reverberate throughout the global cloud market and enable cloud computing in new ways, particularly in underdeveloped economies. Due to the cost of laying infrastructure, such as copper, fiber and piping, underdeveloped economies will opt for the more cost-effective alternative of deploying 5G wireless infrastructure. This in turn will open new doors for the people in those countries and accelerate international cloud adoption.

There are always uncertainties when predicting how the cloud market will unfold. By employing progressive strategizing, cloud businesses can set themselves up for success and take advantage of opportunities as they appear. In doing so, these businesses will deliver added value to their customers, increase their bottom line, and be leaders in their respective markets.

How are various organizations handling the accelerating transition of data to the cloud? What are the obstacles in data cleaning for analytics and the time constraints companies face when preparing data for analytics, AI and Machine Learning (ML) initiatives? Here is a look at some insights from a recent report by Trifacta that answer these questions. 

Data has increasingly become a critical component of just about every aspect of business and the amount of data is skyrocketing. In fact, 90% of the world’s data has been created in the last two years and it’s expected that by 2020, 463 exabytes of data will be created every day from wearables, social media networks, communications (business and consumer), transactions and connected devices. While the explosion in the volume — and more importantly, diversity of data — is instrumental in supporting the future of Artificial Intelligence (AI) and accelerates the automation of data analysis, it’s also creating the obstacles that enterprises currently face in their adoption of AI. Most believe there is great potential to gain efficiencies and improve data-driven decision-making, but as their use cases continue to increase, there is still much room for improvement to remove the obstacles to adoption.  A recent report by Trifacta reveals how these challenges are inhibiting the overall success of these projects and the ability to improve efficiencies when working with data to accelerate decision making. Here is a look: 

Data Inaccuracy is Inhibiting AI Projects

The time-consuming nature of data preparation is a detriment to organizations: Data Scientists are spending too much time preparing data and not enough time analyzing it. Almost half (46%) of respondents reportedly spend over 10-hours properly preparing data for analytics and AI/ML initiative while others spend upwards of 40-hours on data preparation processes alone on a weekly basis. Although data preparation is a time-consuming, inefficient process, it’s absolutely vital to the success of every analytics project. Some of the leading implications of data inaccuracy result from miscalculating demand (59%) and targeting the wrong prospects (26%). Decisions made from data would improve if organizations were able to incorporate a broader set of data into their analysis, such as unstructured third-party data from customers, semi-structured data or data from relational databases. 

C-Suite Has Taken Notice

Simply put, if the quality of data is bad, analytics and AI/ML initiatives are going to be worthless. While 60% of C-suite respondents state that their company frequently leverages data analysis to drive future business decisions, 75% aren’t confident in the quality of their data. About one-third state poor data quality caused analytics and AI/ML projects to take longer (38%), cost more (36%) or fail to achieve the anticipated results (33%). With 71% of organizations relying on data analysis to drive future business decisions, these inefficiencies are draining resources and inhibiting the ability to glean insights that are crucial to overall business growth. 

Rise of AI and ML Push Cloud Adoption

The benefits of the cloud are hard to overestimate in particular as it relates to the ability to quickly scale analytics and AI/ML initiatives, which presents a challenge for today’s siloed data cleansing processes. There are many reasons for widespread cloud migration with 66% of respondents stating that all or most of their analytics and AI/ML initiatives are running in the cloud, 69% of respondents reporting their organization’s use of cloud infrastructure for data management, and 68% of IT pros using the cloud to store more or all of their data — a trend that’s only going to grow. In two years from now, 88% of IT professionals estimate that all or most of their data will be stored in the cloud. 

“The growth of cloud computing is fundamental to the future of AI, analytics, and Machine Learning initiatives. Unfortunately, the pace and scale at which this growth is happening underscore the need for coordinated data preparation, as data quality remains one of the largest obstacles in every organization’s quest to modernize their analytics processes in the cloud.” 

Adam Wilson, CEO, Trifacta.

Data: AI’s Best Friend and Biggest Foe 

Organizations are quickly realizing that AI initiatives are rendered useless, and in some cases detrimental, without clean data to feed their algorithms. 
Often data accuracy would increase if organizations were able to analyze third- party data from customers, semi-structured data, or data from relational databases. However, common barriers to access include data that exists in different systems (28%) or requires merging from different sources (27%) or needs reformatting (25%). Sought-after data sources include customer data (39%), financial data (34%), employee data (26%), and sales transactions (26%). Furthermore, third-party and secondary data present their own sets of challenges, with about half of respondents citing data blending, data movement, and data cleaning as frequent obstacles. 

Data Accuracy is the Only Way Forward 

Organizations can no longer rely on legacy, compartmentalized data integration to handle the speed, scale, and diversity of today’s data. Inadequate data cleansing and data preparation frequently allow inaccuracies to slip through the cracks. This is not the fault of the ETL developer, but a symptom of a much larger problem of manual and partitioned data cleansing and data preparation. According to Harvard Business Review, “Poor data quality is enemy number one to the widespread profitable use of Machine Learning.” 

A clean dataset is critical for AI and ML projects, but as sources of data increase, both in the cloud and on-premises, it’s challenging for enterprises to combat the problems caused by data inconsistencies and inaccuracy. Innovative data preparation technology can help organizations improve data quality and accuracy for AI/ML initiatives and beyond while also increasing the speed and scale of these efforts. Survey respondents’ concerns and priorities for the future speak to how integral these new solutions will become as more organizations rely on data analysis to drive business decisions. The transformational opportunities provided by the advent of AI and cloud computing will only be available to the extent that organizations can make their data usable. After preparation and cleaning, data accuracy increases to 80% (completely = 29%, very accurate = 51%). deduplication (21%), data validation (21%), and analyzing relationships between fields (20%) are the most likely steps to improving data accuracy. 

Looking ahead, given the implications of data inaccuracy and data quality, organizations would benefit from modern data preparation tools to ensure clean, well-prepared data is always available to support business intelligence, analytics, and AI/ML initiatives across the entire organization. Data cleansing can be difficult, but the solution doesn’t need to be. Self-service data preparation tools are solving these problems and helping organizations get the most value out of their data with proper data cleansing. 

Note: The content of this article is from a report titled  “Obstacles to AI & Analytics Adoption in the Cloud” by Trifacta which leverages decades of innovative research in human-computer interaction, scalable data management and Machine Learning to make the process of preparing data faster and more intuitive. Trifacta conducted a global study of 646 individuals who prepare data. The survey was conducted between Aug. 20, 2019, and Aug. 30, 2019, in conjunction with ResearchScape International. 

From wild speculation that flying cars will become the norm to robots that will be able to tend to our every need, there is lots of buzz about how AI, Machine Learning, and Deep Learning will change our lives. However, at present, it seems like a far-fetched future. 

As we enter the 2020s, there will be significant progress in the march towards the democratization of data that will fuel some significant changes. Gartner identified democratization as one of its top ten strategic technology trends for the enterprise in 2020 and this shift in ownership of data means that anyone can use the information at any time to make decisions.

The democratization of data is frequently referred to as citizen access to data. The goal is to remove any barriers to access or understand data.  With the explosion in information generated by the IoT, Machine Learning, AI, coupled with digital transformation, it will result in substantial changes in not only the volume of data but the way we process and use this intelligence.

Here are  four predictions that we can expect to see in the near future:

1.  Medical records will be owned by the individual

Over the last decade, medical records have moved from paper to digital. However, they are still fragmented, with multiple different healthcare providers owning different parts. This has generated a vast array of inefficiencies. As a result, new legislation will come into effect before the end of 2023 that will allow people to own their health records rather than doctors or health insurance companies.  

This law will enable individuals to control access to their medical records and only share it when they decide. By owning your health golden data record, all of the information will be in one centralized place, allowing those providers that you share this information with to make fully informed decisions that are in your best interest. Individuals will now have the power to determine who can view their health records and this will take the form of a digital twin of your files. When you visit a doctor, you will take this health record with you and check it in with the health provider and when you check out, the provider will be required to delete your digital footprint. 

When you select medication at CVS, for example, the pharmacist will be able to scan your smart device to see what meds you are taking and other health indicators and then advise if the drug you selected is optimal for you. This will shift the way we approach healthcare from a reactive to a personalized preventative philosophy. Google has already started on this path with its project Nightingale initiative with the goal of using data machine learning and AI to suggest changes to individual patents care. By separating the data from the platform, it will also, in turn, fuel a whole new set of healthcare startups driven by predictive analytics that will, in time, change the entire dynamics of the healthcare insurance market. This will usher in a new era of healthcare that will move towards the predictive maintenance of humans, killing the established health insurance industry as we know it. Many of the incumbent healthcare giants will have to rethink their business model completely. However, what form this will take is currently murky. 

2.  Employee analytics will be regulated 

An algorithm learns based on the data provided, so if it’s fed with a biased data set, it will give biased recommendations. This inherent bias in AI will see new legislation introduced to prevent discrimination. The regulation will put the onus on employers to ensure that their algorithms are not prejudiced and that the same ethics that they have in the physical world also apply in the digital realm. As employee analytics determine pay raises, performance bonuses, promotions, and hiring decisions, this legislation will ensure a level playing field for all. As this trend evolves, employees will control their data footprint, and when they leave an organization rather than clearing out their physical workspace, they will take their data footprint with them.

3. Edge computing: from niche to mainstream

Edge computing is dramatically changing the way data is stored and processed. The rise of IoT, serverless apps, peer2peer, and the plethora of streaming services will continue to fuel the exponential growth of data. This, coupled with the introduction of 5G, will deliver faster networking speed enabling edge computing to process and store data faster to support critical real-time applications like autonomous vehicles and location services. As a result of these changes, by the end of 2021, more data will be processed at the edge than in the cloud. The continued explosive growth in the volume of data coupled with faster networking will drive edge computing systems from niche to mainstream as data will shift from predominantly being processed in the cloud to the edge.

4.  Machine unlearning will become important

With the rise in intelligent automation, 2020 will see the rise of machine unlearning. As the volume of data sets continues to grow rapidly, knowing what learning to follow and what to ignore will be another essential aspect of intelligent data. Humans have a well-developed ability to unlearn information; however, machines currently are not good at this and are only able to learn incrementally. Software has to be able to ignore information that prevents it from making optimal decisions rather than repeating the same mistakes. As the decade progresses, machine unlearning where systems unlearn digital assets will become essential in order to develop secure AI-based systems.

As the democratization of intelligent data becomes a reality, it will ultimately create a desirable, egalitarian end-state where all decisions are data-driven. This shift, however, will change the dynamics of many established industries and make it easier for smaller businesses to compete with large established brands. Organizations must anticipate these changes and rethink how they process and use intelligent data to ensure that they remain relevant in the next decade and beyond.

The era of everything-as-a-service (XaaS) has provided both an opportunity and a challenge for companies across industries. The XaaS model, a subscription-based solution that makes cloud-based applications available on demand unlike the traditional license-based platforms of the past, delivers several noteworthy advantages over its predecessors. Between cost reductions and easier access to vital tools, XaaS is here to stay. For many, this also means the death of IT as we know it.

On the other hand, XaaS is accompanied by a new set of difficulties to replace older ones. For one, the sheer number of available applications means companies can find themselves overspending and becoming over-reliant on applications. Additionally, the ease with which they can be installed and add new users can create serious problems when attempting to manage large corporate ecosystems.

In an era where unpatched software and applications are growing increasingly problematic, having a veritable “Wild West” of applications on hand can lead to numerous headaches. For companies without IT departments, or with barebones operations, this means having to manually tackle the issue, or simply ignore it. For those who believe IT has become superfluous, the XaaS era proves them wrong. IT hasn’t become irrelevant, it has merely had to adapt to a rapidly changing landscape.

XaaS And The Risky Benefit Paradigm

The migration of most businesses to the cloud has been a constant for several years as companies seek strategies to streamline operations while reducing overheads. Naturally, costly hardware and software licenses are an easy place to start, and the emergence of Software-as-a-Service (SaaS) offers a neatly packaged solution. Instead of allocating ample resources for costly licenses, companies can instead install SaaS applications on as many computers as needed and pay month-to-month accordingly.

Perhaps most appealing is the fact that unlike licensed software and on-premise tools that require constant monitoring and complex installs, SaaS is designed to be plug-and-play. For many companies, and especially small and medium-sized enterprises, this degree of flexibility means decision makers can cut costs in areas like hardware, licensing, and even the IT teams that manage software. With software that is so easy to use, the argument goes, there’s no real need to have someone supervise it.

Initially, that line of reasoning holds water. SaaS is indeed significantly easier to manage, install, and adapt when compared to licensed on-premise software. It’s also designed to be more user-friendly and filled with rich security features. Yet, the simplicity of SaaS, when paired with the absence of oversight, can lead to unexpected security issues.

The Changing Role Of IT

It is hard to deny that SaaS is here to stay. A FinancesOnline report noted that in 2018, nearly 51% of all companies interviewed reported that a majority of their applications are SaaS-based. That number is expected to balloon to 73% next year, and to 86% over the next three. For IT professionals, this is an encouraging sign that arrives with some baggage. The lack of monitoring required for SaaS to function means that some issues may slip through the cracks.

One of the biggest problems that emerges from SaaS usage is unpatched or out-of-date software. While many SaaS applications perform automatic updates, some do not. When software is left unpatched, it creates security gaps and open systems to attacks that have already been rendered useless by new patches. Most importantly, unpatched software has a real cost. Equifax’s data breach, itself the product of unpatched vulnerabilities, cost the company an estimated $5 billion in market capitalization.

IT is still vital in this case, but in a wholly different capacity. Applications like Cloud Management Suite, for instance, scan for unpatched software and provide full upgrades automatically. It can also manage app distribution and security. This removes the need for a dedicated IT department. Similarly, issues of over-use of SaaS tools can raise other issues, such as wasted efforts, resources, and generate redundancies. In these cases, there are multiple apps such as Torii, which supply simpler and more efficient high-level management tools for ecosystems that rely on multiple SaaS applications.

Finally, the online nature of SaaS means even applications that are fully patched and up-to-date may face a new kind of security problem. With employees coming and going from companies, and so many applications to manage, companies can easily forget to modify credentials for former employees or temporary contractors. This opens companies to seriously harmful exposure when a disgruntled employee or even an unwitting former worker leaves credentials publicly available. In these cases, tools like Menlo Security and CyberArk can provide a secure ecosystem while ensuring companies can properly manage access to their networks and software infrastructure.

IT Is Different, But No Less Important

In the end, IT was always destined to evolve. As companies’ technology needs shift, the old tasks required of IT—managing on-premise servers and hardware, installing licensed software and managing it—are quickly growing obsolete. However, new models are accompanied by their own problems. Instead of fading away, IT is instead emerging as a more fast-paced, fluid role for tech-focused companies.

How will micromobility change the way we travel from point “A” to “B”? How will micromobility co-exist with the traditional models of transportation? What is the importance of network effects in micro mobility?

Kristin Dolgner, a marketing and communications professional at BCG Digital Ventures based in Berlin had an eventful week recently, when she travelled to the US. It started with testing a Lime kick scooter in Santa Monica, then riding an Uber to her office in Manhattan Beach and after her return to Berlin, she spontaneously signed up for Jump electric bike by Uber.  

Kristin, in her own words, is an excessive user of the ridesharing app Berlkönig offered by Berlin public transport Berliner Verkehrsbetriebe (BVG), German car-sharing apps such as DriveNow and car2go, as well as taxi service FREE NOW( formerly mytaxi). 

If you ask her to quickly review her experience of the week when she tried these different modes of transport, she says, “ I was really excited for the kick scooter but I didn’t feel that it was an exclusive experience. Maybe there was a problem with this specific scooter, but I experienced a bumpy ride with the engine gearing up and down. Apart from this, I was carrying a bag with me, which turned out to be very impractical for the kick scooter ride and left me feeling unsafe next to the heavy cars and scooter traffic in LA. However, looking at my Uber experience in the U.S, it is still  a better experience as compared to Uber in Germany – be it availability, fleet, service oriented drivers, pricing etc. Jump bike was the biggest surprise to me : the shrill pink look is killing me but biking at 35 couldn’t have been any more pleasant. Two rides in and I am already a huge fan.”

It is not just Kristin who is confused, but most people today are, who are spoilt for choices when it comes to using the modes of transport and making the right choices. The question that arises is that how are these different modes of transportation going to co-exist? 

Lawrence Leuschner, CEO and co-founder at TIER mobility explains the existing scenario of mobility when it comes to transportation, “The current transportation system is dominated by cars. It has a negative impact on health, environment, space and safety. At present, Europe is pushing cities towards car free cities. The younger generation does not want to own assets. There are three options to travel at present: Car sharing (DriveNow), Ride Sharing(Uber) and then there is micro mobility (TIER). The cake is big enough for all the players right now, but in the end you want to be the one who wins.” 

Most new mobility models want to solve the existing problems in transportation by reducing carbon footprints and decreasing the time of commute.  Cities such as Berlin and Tokyo, could boast about their extensive and user-friendly public transportation systems, which include subways, trains, buses, and trams. Into this field of transportation within cities, enters Micro mobility. 

Micromobility, a relatively new concept, is defined as any transportation via very light vehicles. These include, but are not limited to, electric scooters, electric skateboards, and bicycles.

One of the names you will hear more than often in this space is US-headquartered Lime which has raised over $600 million in capital. “ There are 45 million cars in Germany alone , which is a country of 80 million people. There are 250 million cars in the US with over 320 million people. You need three things to be a great company- operational excellence, leadership in hardware, true partnership with cities. Eight months ago, we did not have a single market that  could say with a straight face that we are making money . Today, 30 percent of our markets are making money and the nmber is going to increase. There is no micro-mobility unless you are a true partner with cities. A deep focus on safety, people and underground teams in each city has been our strength. Ride sharing and mapping companies have invested in Lime,” says Wayne Ting, Global Head of Operations & Strategy, Lime . 

THE IMPORTANCE OF NETWORK EFFECTS IN MICROMOBILITY

Micromobility is new and there are existing traditional players that still dominate the market.  Hence, we see a greater movement toward traditional corporate mobility and micromobility startup partnerships.  

Gunnar Froh, Founder and CEO, Wunder Mobility, says, “In the present scenario, there is a need to be the middleman. If it requires technology- to upgrade mobility- companies like us do it. Digital solutions will open new choices for customers. For example, ticketing by mobile phone for various modes via mobility platforms helps consumers to search for the best platform.”

Some examples of corporate- startup collaborations are given below: 

• Ford, is partnering with small electric scooter companies, such as Spin, to diversify their brand as well as maintain influence in the future of the mobility sector. 
• Volkswagen set up its VW Digital Lab in 2016, an innovative hub to execute transportation experiments.
• Honda has its Xcelerator program, which funds startups that are coming up with mobility solutions for the future. This movement of traditional, corporate companies partnering with startups, if done effectively, could mean successful metropolitan integration. 

A recent report by Deloitte says: Automakers are experimenting and inventing, and have passionate voices within their ranks, describing much-altered futures. Most have set up offices in Silicon Valley to gain greater proximity to technology development and early-stage funding. Among the noteworthy examples of forward-thinking initiatives are Ford’s 25 mobility projects, BMW iVentures,Daimler’s engineering advances in intelligent driving,and Cadillac’s “super cruise” functionality. In addition, public-private partnerships such as the recently opened Mcity in Ann Arbor, MI, provide a platform to enable more efficient and effective automated vehicle (and feature) testing.

This approach is consistent with historic norms, in which automakers invest in new technologies—e.g., antilock brakes, electronic stability control, backup cameras, and telematics—across higher-end vehicle lines and then move down market as scale economics take hold. In Deloitte’s ongoing conversations with auto-industry leaders, they repeatedly and collectively argue, that outsiders simply do not appreciate the sheer complexity of developing a vehicle today, the challenge of introducing new advanced technologies into a vehicle’s architecture, or the rigor and inertia of the regulatory environment. All of this encourages incumbents to believe that they can be at the center of actively managing the timing and pace of these converging forces.

HOW STARTUPS AND CORPORATES COULD WORK TOGETHER? 

The crucial question is, how far can these partnerships go and why  they are so important right now? “Startups and corporates are like fire and water – work attitude, culture, values etc,” says Holger G. Weiss, CEO of German Autolabs. However, innovation in digital times is very fast and has to be fast. “That is to my knowledge the strongest reasons why startups are outpacing traditional corporations in new technologies and business models. They can be faster as they don’t have to be so risk-averse, they can test without immediately burn a brand etc. On the other hand, if it comes to scale and execution, corporates can show their true potential. So, while at first glance there is no common ground, in fact it’s a question of timing that startups and corporates will benefit from each other,” he says.

Gal bechor,  Product Manager, Volkswagen Digital Lab feels, “In a world that is changing so rapidly, we realize it is important for us to collaborate with startups, they can move faster and do things that our processes and bureaucracy do not allow us to do.However, startups usually lack the resources, experience, and scale that we have. By collaborating we are able to get the best of both worlds and build things together. We are now trying this in a small scale with “Inbound Initiative” where we take challenges from business units, and collaborate with startups that can solve them.”

Marvin Metzke, founder of an e-scooter startup called Simple Mobility, talks about the necessity of partnerships between startups and corporations to create sustainable cities. Simple Mobility is a startup that provides its customers with user-friendly scooters as a method of transportation. As a startup, Simple Mobility is able to move with flexibility, but doesn’t always have access to infrastructure necessary to complete its vision. Corporations are more steadfast in their approach, and  have the resources and the infrastructure to execute its goals. 

For example, Simple Mobility’s partnership with Deutsche Telekom will give the startup the infrastructure and investment it needs to successfully bring its scooters to the streets of Berlin. Unlike other e-scooters, Simple Mobility scooters do not need to be plugged in to charge. They rely on chargeable batteries, which can be removed, recharged independently, and inserted back into the scooter, thus allowing more flexibility in scooter movement and business. Simple Mobility’s goal in a partnership with Deutsche Telekom is to enact battery exchanges and charging programs on a large scale. 

WHAT IS THE FUTURE OF MICROMOBILITY? 

With micromobility, a 35-40 minute bus ride becomes a 9 minute scooter ride. As Marvin rightly puts it,  “A future in which scooters, cars, buses, etc. successfully coexist on the streets has the potential to create a better quality of life for people who live in the city.”  

As per a Deloitte report, young adults, along with urbanites, are gravitating toward a model of personal mobility consumption based on pay-per-use rather than upfront purchase of a capital asset, which fundamentally challenges today’s consumption model centered on personal ownership of cars. The change will happen systematically—a rising tide, not a tsunami. At no point will the world be presented with a Manichean choice and collectively decide to plunge all-in to a system of driverless, pay-per-use travel—or else to change nothing at all. Rather, the new personal mobility ecosystem will likely emerge unevenly across geographic, demographic, and other dimensions, and evolve in phases over time.

With corporate funding and infrastructure, micromobility startups have the potential to change the way we live and move. Getting from point A to point B within a city could be an issue of the past. Nico Wohlgemuth, Managing Partner of Dayone, a service design studio leaves us with a question to think about, “If there are passenger drones in the future, the question won‘t be how you get to Moabit. The question will rather be why you‘re still living in Moabit – and not outside the city listening to birds and going for a swim in the lake nearby. The city will just be a glimpse away.”

Note: The quotes by Lawrence Leuschner, CEO and co-founder of TIER mobility ; Gunnar Froh, Founder and CEO of Wunder Mobility; Wayne Ting, Global Head of Operations & Strategy, Lime – are extracted from their speeches at the Noah Conference Berlin 2019. This is a co-authored piece by Diksha Dutta and Arwa Sutarwala.

Meet investors, Blockchain and crypto enthusiasts, a talent pool of developers and programmers  as they solve three Blockchain challenges over two days in Berlin. Here is why you should be a part of LongHash Cryptocon Vol2.

Berlin has been recognised as the cryptocurrency capital of Europe for more than half a decade. The city emerged as one of the first in Europe to accept digital currencies back in 2013 and the crypto revolution is now backed by over 100 blockchain companies based in Berlin. Jasmine Zhang, CEO, LongHash Germany, which is organising its second Blockchain Hackathon (part of LongHash Cryptocon Vol2) in the city on May 18-19 this year, rightly puts this in perspective, “Berlin, as many people have commented already, is a great place with infrastructure and talented, international people. We would like to leverage the strength and expertise we have from the East, and bridge with the West to make a positive impact on blockchain ecosystem. Our aim is to further accelerate the understanding and development of blockchain technology globally.”

LongHash is a platform for accelerating the development and understanding of Blockchain technology. LongHash incubators provide a full-range of support for start-ups working on blockchain-related projects.

As an early-stage blockchain investment and incubation firm, Longhash supports its portfolios long-term. Zhang says, “We are hosting different events including hackathons worldwide, like in Germany, Japan, Vietnam since last year to help their ecosystem grow. This edition’s three projects come from U.S, China and Germany with big potential and a healthy, strong developer community is what they are seeking at the moment and this belongs exactly to the post-investment management that LongHash is providing.”  

Back in Berlin:  With more challenges and ETH prizes for developers and Blockchain Geeks

The first edition of the Hackathon was last year during The Longhash Crypto Festival Berlin, which took place between October 26 and October 29 and promoted innovation among programmers, attracting participants from Asia, eastern Europe and the US. And this being the second edition, the competition will be more challenging yet rewarding at the same time. Winners of the second edition of hackathon have an opportunity to win upto 30 ETH equivalent prizes. Here is a look at the categories:

• Cybex Prize: 5 Eth
• MXC Prize: 5 Eth equivalent amount of MXC Token
• Taraxa Prize: 5 Eth

On top of this, one chosen winner will be  awarded Euro 2,000 equivalent amount of VET powered by VeChain and more prizes are to be announced soon!

The challenges have been carefully designed considering the needs of the Blockchain ecosystem and where the innovation is most desired. Here is a look:

Challenge 1: How to implement an Algo Order in Cybex Dex？

Cybex.io is a blockchain based decentralized exchange that supports crypto trading. When a user has an intention to perform a large trade, it is useful to have an algorithm to split the order into smaller slices and trade it over a longer period. This feature is referred to as ‘Algo Order’ and is widely adopted in regular exchanges.

In decentralized exchange, each sliced order must be signed by the user’s private key. This provides a new challenge to algo orders. In order the place orders automatically, while keeping the private key safe, a user typically has to write its own program and run it in its own machine. This makes it difficult for normal users to use algo orders due to the lack of programming skills.

Design a solution that allows a normal user to execute and manage algo orders.  

The following are some basic Algo order types:

• TWAP :http://empirica.io/strategies-catalog/twap
• VWAP :http://empirica.io/strategies-catalog/vwap
• Iceberg:https://www.investopedia.com/terms/i/icebergorder.asp

The solution should be using Cybex API, which is available at the following locations:

• Cybex API server documentation: https://apidoc.cybex.io
• Sample program to place an order:https://github.com/CybexDex/cybex-python

Solutions will be graded on :

• User interface friendliness
• The ideal solution should be easy enough to attract people without programming skills
• Security
• As trading involves using private key, the management and storage of the key is a crucial consideration.
• Framework Coding quality

Challenge 2: How do we automate the Smart Machine Bidding procedure for the LPWAN devices in order to reduce the costs of an IoT network?

MXC foundation focus on connecting Low Power Wide Area Network (LPWAN) technology with the blockchain as an infrastructure for Internet of Things (IoT). MXC automates machine-to-machine (M2M) transactions and provides a device data economy. The pricing policies of data transmissions through gateways in LPWAN are determined by MXC Smart Machine Bidding (SMB). In the SMB, based on the bidding strategies provided by the device owners, and the gateway owners, the payments for using downlink / uplink LPWAN resources will be determined.The following parameters are set by the device in bidding strategies of the SMB:

• max_bid: the maximum bidding price defined by the device owner shows the upper payment threshold of the device (in MXC tokens) for the downlink request.
• max_delay: this parameter defines, under certain circumstances, the maximum acceptable_delay (in seconds) for the packet to be sent. If max_delay is reached, the packet will not be sent and the cloud will notify the client about the rejection of the downlink request.
• accepted_delay: the tolerable delay defined by the client (or device owner) to indicate the time period a packet is willing to wait for the lowest possible price.
• Lowest possible bidding price is the current lowest bid of the available gateways for the device.

Each gateway provides a value on using its resources called min_bid. The device in order to use the gateway downlink resource, should bid at least min_bid value. If multiple devices in a same time wants to use a downlink resource of a gateway, the one which define more max_bid will be the winner. More details about bidding procedure are provided in MXC Smart Machine Bidding white paper (available in the repository stated below and the MXC website). Based on downlink / uplink data flow of the device owners and their requests, MXC cloud can provide data driven automated smart machine bidding.  max_delay parameter is mainly related to the application and the priority of the data which is known by the device owner/client and is defined by the requirement of the provided application by the device.

On the other hand, accepted_delay, and max_bid parameters should be provided by the device owner (or the client) in some way to make a balance between the priority of the related uplink/downlink data and the corresponding data transmission cost. These two parameters (accepted_delay, and max_bid) can be automatically provided for the device owner to make this balance. Your task is to develop an automated solution (e.g. based on Machine learning methods, dynamic algorithms or greedy algorithm) which provides near-optimum value for accepted_delay and max_bid parameters to reduce the total cost of the LPWAN for the user.

In the input file, you will receive max_delay, payment limit which the data owner wants to pay in total (for all of the transactions), and the downlink resource usage history of the device and other devices. Your program (preferably in Go) will be evaluated by output efficiency (based on the test cases of LPWAN data simulation), and solution explanation (provided in your documentation). Note that you can provide multiple solutions and do the implementation as much as you want/can. A sample input file and its details will be provided in the below repository:

https://gitlab.com/mxc-hackathons/smb

Challenge 3: How to implement an anonymous data collection scheme that allows the manufacturer to anonymously collect data from its end devices without knowing exactly which device it came from?

Data privacy and security has become an increasingly urgent concern worldwide. Large corporations cannot simply collect data from its end users without their knowledge or explicit consent. However, it would be nice if a manufacturer could still collect data generated by its devices without user consent, but do so in a way that’s cryptographically-guaranteed to be anonymous. In this scenario, the manufacturer would like to collect data from anonymous devices, but it would want to be sure the data it’s receiving is not garbage and is guaranteed to have come from a device it has manufactured. The end user would not mind that its device’s data is being harvested as long as it is impossible to trace that device’s data directly to the end user’s identity.This problem is broadly defined as direct anonymous attestation, and more narrowly defined as a membership proof. An earlier paper (https://infoscience.epfl.ch/record/128718/files/CCS08.pdf ) had been published with an open-source implementation published ( https://github.com/ing-bank/zkproofs ).

Assumptions :  We assume that the manufacturer has embedded within each device it made with a pair of asymmetric encryption keys, and that hacking the device on-premise to obtain these keys is prohibitively expensive to do. We further assume that the manufacturer is willing disclose the public keys associated with all of its products to the open.

The challenge: HOW to implement an anonymous data collection scheme that allows the manufacturer to anonymously collect data from its end devices without knowing exactly which device it came from?

• A device can prove to the manufacturer that it is indeed one of the devices it has created
• The manufacturer will construct a temporary X.509 certificate so that a proof does not need to be provided every time, temporary because the end user might want to stop even anonymous data collections

Bonus:

• Manufacturer & device both anchor the challenge & proof onto the blockchain
• Device anchors its data transmissions onto the blockchain with the temporary certificate

Does this interest you as well? Apply here for the Hackathon before the 12th of May – the event is free for all developers. Also, there is more. If you are a developer or aspiring entrepreneur in the blockchain/crypto space and  want to know about the investment perspectives from Top Asian & European Funds in the Blockchain segment or business use cases in real word adoption, get your free tickets for Hash Talk which will be an afternoon-long summit focused on discussions and creating insights on investment, business, and tech in blockchain curated and brought by LongHash Germany. More details here.

How do we automate the Smart Machine Bidding procedure for the LPWAN devices in order to reduce the costs of an IoT network? Yes, this is one of the challenges for the second Blockchain Hackathon (part of LongHash Cryptocon Vol2) in Berlin on May 18-19 this year. More details here.

As an advantage to all developers, blockchain enthusiasts and crypto geeks who are aching to solve this challenge, here is an interview with Aslan Mehrabi, Data Scientist at MXC Foundation which defines LPWAN in detail and its IoT devices that operate through it, and maybe a few tips that might help in cracking this challenge.

MXC foundation focuses on connecting Low Power Wide Area Network (LPWAN) technology with the blockchain as an infrastructure for Internet of Things (IoT). MXC automates machine-to-machine (M2M) transactions and provides a device data economy. The pricing policies of data transmissions through gateways in LPWAN are determined by MXC Smart Machine Bidding (SMB). In the SMB, based on the bidding strategies provided by the device owners, and the gateway owners, the payments for using downlink / uplink LPWAN resources will be determined. Your task is to develop an automated solution (e.g. based on Machine learning methods, dynamic algorithms or greedy algorithm) which provides near-optimum value for accepted_delay and max_bid parameters to reduce the total cost of the LPWAN for the user. Edited excerpts of the interview:

Please share a some background of MXC Foundation and its focus?

MXC is creating a global data highway, which automates machine to machine (M2M) transactions, decentralizes big data and enables a device data economy. With the introduction of the Machine Xchange Coin (MXC), adopters of LPWAN data technologies trade data access or sensor data for MXC.

The MXC global data highway is automated using smart contracts running on the Machine Xchange Protocol (MXProtocol). Based in Berlin, MXC is a non-profit foundation promoting the global adoption and implementation of LPWAN data technology.

At MXC, we believe that MXC, paired with LPWAN is the next step in the fourth industrial revolution, we’re actively enabling smart cities and providing public access to big data. By introducing the Machine Exchange Coin and the Machine Exchange Protocol, MXC gives everyone a chance to profit from a more balanced and intelligent infrastructure data network. This is why MXC, is the future of IoT.

What is a “Low Power Wide Area Network”?

LPWAN stands for ‘Low Power, Wide Area Network’. it can be used to realize the Internet of Things (IoT). LPWAN is a type of wide area network that allows radio-equipped devices to communicate. WANs are simply telecommunications networks. The system of cell phone towers and 5G you rely on every day is a WAN. So is the internet, if you want to get technical. You could form an Internet of things WAN using 5G technology, or even landline broadband. However, unless your device has a mains plug, you’re going to run out of battery power very fast that way. Instead, the future of large scale, low maintenance, widely dispersed IoT applications will be found in LOW POWER wireless WANS – LPWANs.

What are the examples of IoT devices that operate through MXC or LPWAN?

Temperature sensors, smart locks, movement sensors, fire alert and etc.

Where will the data come from?  In the LPWAN based IoT network, the devices (also known as sensors / nodes) are required to send the data which they have produces to their corresponding server and receive commands from it. It makes the flow of data which is possible by LPWAN.

How are the prices determined?

By the bidding procedure which is determined in the SMB white paper of MXC

Any examples of device owners/ gateway owners?

If I have a LPWAN device (temperature sensor, smart lock, movement sensor, etc) I am a device owner. LPWAN Gateways are needed in order to send/receive data to/from the LPWAN device. Gateway owners – people or companies who own and maintain gateways.

How fast can the machine operate? How much data can it process and transmit?

It depends on what do you mean by the machine. For LPWAN devices, based on the applications and the firmware, different processing and data transmission speeds can be provided.

What are the main industries where such a technology is applicable?

Smart cities, smart homes and in general the future world will use it. For more information take a look at https://www.matchx.io/solutions/

Why focus on using GO as the programmatic language?

Golang is a preferable language.  It’s convenient, fast, and secure to write code with Golang, and it provides cross-platform support. Golang is currently one of the fastest growing programming languages in the software industry. Its speed, simplicity, and reliability make it the perfect choice for all kinds of developments.

How is cost being defined exactly if the value of the data is set by the owner?

In this task (and generally the SMB), we are investigating on data transmission cost which should be paid by the device owner to the LPWAN resource providers (e.g. gateway owners). Value of the data is set by the data owner in the data market place of MXC which is managed in another ways and is not related to the SMB.

Tell us a few more applications of the LPWAN?

LPWAN can provide a cost effective network for IoT.  Battery usage of LPWAN devices are super low. The devices are able to send / receive data for several years with a single battery. These are just a few of the reasons to say LPWAN empowers the IoT.

Any extra tips for the developers who are working on this task?

A really good implementation of this task is very important because it will help to optimize expenses for end-users.

Does this challenge interest you? Apply here for the Hackathon before the 12th of May – the event is free for all developers who apply. Also, there is more. If you are a developer or aspiring entrepreneur in the blockchain/crypto space and  want to know about the investment perspectives from Top Asian & European Funds in the Blockchain segment or business use cases in real word adoption, get your free tickets for Hash Talk which will be an afternoon-long summit focused on discussions and creating insights on investment, business, and tech in blockchain curated and brought by LongHash Germany. More details here.

Vehicle-to-Cloud – yes, it is a thing!  And it is making automotive insurance providers and telecoms thrive together to support telematics

“Self-driving cars are the natural extension of active safety and obviously something we should do,” this is a popular quote by Elon Musk. But wait, even more popularly discussed are  the data privacy challenges which come with connected cars. The solution to these challenges rest in the world of Telematics, a method of monitoring an asset (car, truck, heavy equipment, or even ship) by using GPS and onboard diagnostics to record movements on a computerized map.

Telematics might be a decade old phenomenon, but what is relatively new to telematics, and innovating at a high speed, is the effort to connect telematics to the internet of things (IOT); and find out how telematics solution providers can benefit from cloud-based management services. The critical question is how the data generated within a vehicle will effectively interact with data of the outside world through connected devices. We all are waiting for an era where vehicles will not only drive themselves but also talk intelligently to us. This makes the role of telematics service providers important who offer services to vehicle drivers for either a subscription fee or any other arrangement. These can be emergency services or information services to improve the driving experience.

To keep up with the recent government regulations across the globe and cope with climate change, fleet operators are strongly emphasising energy saving and vehicle safety measures. At the same time, they want to ensure business models which can reduce Total Cost of Operations (TCO), which encompasses acquisition cost, operational cost and depreciation. This is where connectivity-as-a-service can help telematic service providers.

Here is how it works: Data is gathered from vehicles and transmitted to a cloud-based platform and then used for various services depending on the functionality of the app-  location, fuel consumption or speed. Apps interact with telematics systems to see how fleet companies are operating. (see chart below)

The numbers speak of the potential of this growing industry for automotive, cloud-based solutions and telecommunications, among other third-party providers. According to  Global Telematics Market Report released by Netscribes, the global telematics market is expected to grow at a compound annual growth rate (CAGR) of 28.5% between 2017 and 2022 to reach a global revenue of USD 233.24 billion by 2022. Here are a few areas where connectivity-as-a-service can help telematic service providers.

Compliance with Government regulations

Governments in several countries have made it mandatory to have an electronic onboard recorder (EOBR) fitted commercial vehicle, which is one of the key drivers of the adoption of telematics among fleet companies. Today, North America is the highest revenue-generating region for the automotive telematics industry. The telematics market is at a mature stage in countries like the US and Canada, where stable growth is forecasted until 2022. The ELD mandate, passed this year, limits how much truck drivers can drive and when; for example, they cannot drive for more than 11 hours during a 14-hour period. These rules have required telematics service providers to keep a track record of necessary data to ensure driver safety and compliance.  

Documentation of real-time data by fleet operators

The primary role of telematics service providers is to measure driving data to offer services like driver behaviour analysis, safety training integration, predictive analysis and connected vehicle frameworks. To provide any of these solutions, one needs to accurately collect real-time data generated by vehicles from various devices and then transform it into a structured form to make accurate business decisions. For instance, helping an insurance company know the driver performance data to offer better products. With the documentation of real-time data, solution providers can access cloud-based management systems through mobile or desktop.

Security and safely in vehicles

Statista predicts that by 2020, connected cars will make up 98% of the new car market worldwide. With this innovation also comes challenges regarding security. Sample this: In 2016, a team from security research firm Keen Labs successfully demonstrated how to hack Tesla’s Model-S. They tricked drivers into accessing a fake website through a malicious Wifi hotspot, which then downloaded the researchers’ software. This software enabled them to gain control of the car’s features. The solution to protect connected cars also lies in connectivity-as-a service, which will enable transparency and decentralisation of data.

Customisation for various business models through collaboration

Cloud platforms allow drivers, carriers, shippers, fleet operators, dealers, service stations, insurance companies and other authorities to be connected in real-time with each other. The likes of Google and Alexa can get information on the local road safety and what steps to advise next in terms of recommendations to users. Through connected platforms, service providers can give personalised advice gathered on the basis of contextual data such as geolocation, traffic zone etc. It is as simple as Alexa helping a user to make the next decision with the help of a connected device through different apps. What route to follow? Which restaurant to visit? What speed to maintain?

Conclusion

It is clear that the IOT  is revolutionising fleet management. The newest generation of Fleet Management Solutions have migrated to the IOT. The clear benefits of an effective vehicle-to-cloud platform are increased driver safety, reduced TCO and predictive analysis for the future.

To deliver optimum results to telematics solution providers and fleet operators, there is a need for reliable connectivity across coverage areas, and compatibility with networks– which a reliable connected platform can provide.

We are leaving you with a list of ten telematics service providers:

Disasters are dangerous, but Big Data can help improve disaster relief and preparedness to cut back on lives lost and community damage.

Historically, public policies have proved ineffective in providing adequate help for disaster-stricken citizens. A year after hurricane Harvey in 2017, for example, residents are still in the midst of recovery despite $15.3 billion earmarked for relief efforts.

With the emergence of new innovations, one wonders if legislators should give more thought to incorporating Big Data technologies into aiding in disaster prediction and relief.

Over the last two decades, remarkable innovations such as the Internet of things (IoT) have entered the mainstream. While the intensity of natural disasters is increasing, advances in communications because of this technology has greatly reduced casualties and injuries. For instance, agencies such as NASA and the National Oceanic and Atmospheric Administration (NOAA) leveraged big data technology to predict hurricane Harvey’s landfall and coordinate emergency response personnel.

The technology helped the agencies choose ideal disaster response staging locations and evacuation routes as well as pinpoint likely flooding areas and prepare accordingly. Additionally, agencies throughout the storm impact area used machine learning algorithms to dictate the trajectory of the storm and its potential damage.

Big Data for Disaster Management

Big Data technology has proven its merit as a resource for disaster relief and preparedness. It helps emergency responder agencies identify and track populations such as elderly communities or areas with high concentrations of babies and children.

Additionally, Big Data systems help rescue workers identify support resources and plan logistics during emergencies. Big data also facilitates real-time communication during a disaster, and emergency managers use the technology to forecast how residents will react to crises.

Today’s big data systems are growing at an accelerating rate with some studies saying that 90% of the worlds data was created in the last two years. All of this data can be used to help emergency managers make more informed decisions before, during and after natural disasters.

Dr. Anirudh Ruhil, professor of leadership and public affairs at the Voinovich School of Leadership and Public Affairs at Ohio University, says that data can also be used to help improve the current process, “By seeing how residents move, by gathering data on their experiences, what worked, what did not, and then going back after the emergency is over to study the emergency response and identify weak spots.”

This reporting allows disaster response managers to combine mapping data with geographical records and real-time imagery.

The reports also provide responders with on-demand information about activity in disaster areas and gives them a continuously flowing stream of real-time information during frantic emergency scenarios.

Big Data Helps with Crisis Mapping

In Nairobi, the nonprofit data analysis group Ushahidi has developed an open-source software platform for information gathering. Their technology works with an interactive mapping platform developed in 2008 that was used to analyze violent areas after the Kenyan presidential election.

At that time, the agency gathered information from eyewitnesses and social media. Team members then plotted that information on an interactive Google map, which helped citizens steer clear of danger.

The group’s technology was deployed again in 2010 doing the earthquake in Haiti, helping to save the lives of many citizens in that region.

The U.S. Marine Corps. used the organization’s crisis mapping system to quickly find and rescue citizens. This innovation is invaluable during crises and can also reunite separated families. For it to work effectively, however, residents and volunteers must assist with data collection.

Connecting Families & Loved Ones

Technology leaders Google and Facebook have also developed advanced resources that help during disasters. The tech giants have deployed online systems that help family members reconnect after being separated during an emergency.

Google, for instance, released its “Person Finder” application immediately after the 2010 Haiti earthquake. The platform allows anyone to enter missing persons information and hopefully reconnect with family members during a disaster. Doing the Haiti earthquake, for example, citizens updated Google’s Person Finder 5,300 times in attempts to locate family members.

Robots & Drones

In 2015, governments around the world deployed drones to aid in 43 disasters that occurred in 13 different nations. The technologies reduce exposure to risks for emergency workers such as claims adjusters, volunteers and engineers.

As an example, unmanned aircraft systems (UAS) are especially beneficial for tracking and reconnaissance. They’re cost-effective and provide rescuers and emergency workers with visual perspectives that cannot be acquired with manned craft. Furthermore, robots and drones can make structural adjustments, provide logistics support and deliver supplies. They’re also especially beneficial for responding to disasters involving chemical, biological and nuclear materials. Resultantly, robots and drones hold tremendous potential for aiding in disaster recovery efforts. Analysts forecast that the global demand for UAS will climb to 4.7 million units by 2020 and generate between $2 billion and $127 billion at that time.

Emergency Preparedness

Big data systems are making it easier for agencies to forecast when disasters will occur. This advanced information allows agencies to prepare communities for emergencies and protect them from threats. The organizations combine data collection, scenario modeling and notification platforms to form preemptive disaster management systems.

Citizens contribute by providing household information that the agencies use to evaluate and allocate resources during disasters. For instance, residents can share potentially lifesaving information, such as whether there are physically impaired residents living in a household.

The United States needs more data scientists who work with technologies that aid during disasters. This is a common theme among many enterprises that are struggling to fill their ranks with qualified information technology (IT) professionals.

In fact, a recent survey revealed that over 40-percent of enterprise leaders believe that the data scientist shortage is making it difficult for their organizations to thrive in the competitive marketplace. Firms that do manage to fill their IT ranks, however, perform more strongly than their talent deprived peers.

Analysts are forecasting that United States enterprises will create 490,000 new jobs for talented data scientists by the year 2020. However, with the data science talent pool only forecast to reach approximately 200,000 available experts by that time, there are ample opportunities for these professionals for the foreseeable future.

The challenges facing cities in the 21st century are greater than ever. A new focus is required to overcome the emerging social, technological, economic, environmental, and political forces exerting pressure on cities. This new focus must address how to create cities that are sustainable– for citizens, for business and for the environment.

Tackling this issue head on, Arcadis has developed a Vision 2030 plan that sees new platforms, capabilities and domains operating in cities– with citizens at its core.

According to the Arcadis Sustainable Cities Index 2018, “Sustainable cities can be thought of as places that are planned and managed with consideration for social, economic, environmental impact, providing a resilient habitat for existing populations, without compromising the ability of future generations to experience the same. Accordingly, measures of sustainability need to be able to measure current city performance, ability to mitigate future impacts as well as investment in future capability– ideally measured from the perspective of the citizen.”

Data– and how we analyse it– is the game changer. Data provides the tools to plan, develop and construct cities in which citizens can thrive in harmony and order. Advanced analytics allow organisations to think differently, using data to uncover trends, insights, and even unearth hidden problems. The results allow cities to plan for the future and budget more effectively, leading to stronger end results.

Arcadis is working with organizations in cities across the globe to co-create solutions to their unique challenges, and develop sustainable new models that will improve the quality of life for their citizens.

New York: Reducing Traffic & Carbon Emissions

New York City has committed to reducing commercial waste disposal by 90% by 2030 as part of its “One New York: The Plan for a Strong and Just City”. We have been working with the New York Department of Sanitation (NYDS) to introduce reforms to optimise its commercial waste collection. The improvements will result in cleaner and safer streets, improved air quality, and a more efficient commercial waste collection system, all of which will advance the City’s zero waste goals. Advanced data analytics provided the evidence for authorities to make informed decisions for a better system across the five boroughs.

Our research of 9,900 routes revealed multiple commercial waste haulers covering the same routes, driving on average 123 miles each day. This resulted in long shifts (15+ hours), and created related safety concerns for drivers and citizens– including fatalities.

The team analysed the routing data, customer locations and haulers’ behaviour at a granular level and simulated a variety of options.

The result: Arcadis recommended a strategy for a zoned system with a limited number of haulers in each. This approach would provide a modern commercial waste collection system that would reduce waste disposal-related traffic by 60%, and CO2 emissions by 500 tonnes a year.

San Francisco: Managing BART’s Assets & Creating 15-Year Work Forecasts

SEAMS, an Arcadis company, has been working with Bay Area Rapid Transport (BART) since 2017 to develop a network renewal optimization model using SEAMS’ Enterprise Decision Analytics (EDA) software. The model predicts the changing condition of every asset on the BART network, from the rolling stock and track assets, to staff vehicles and computer terminals. It can account for the impact of the introduction of a new fleet of trains, new signalling systems, the construction of line extensions and the associated disposal of legacy assets to deliver the best overall network plan.

The value of this model is in its ability to identify the optimal mix of asset renewals to deliver the best overall reduction in network risk, whilst complying with the highly complex funding rules imposed on BART. EDA is currently the only solution that has proved powerful enough to achieve this.

The result: the model allowed the client to account for the impact of introducing of new assets, identify the best overall asset investment plan to manage the overall system risk, and create detailed work forecasts for the next 15 years.

Manchester: Fighting the UK Housing Crisis With Data-Driven Investment

Greater Manchester Combined Authority (GMCA) has a problem: the UK Housing Crisis. It is enormous in scale and recognizable in cities across the UK, and beyond.

GMCA has a target to deliver 10,600 new homes each year for the next 20 years, and they are currently set to deliver 7,900 in 2018. They also need to make sure they are the right types of housing (20% must be “affordable”), on the right type of land (brownfield vs green belt), in the right areas, with the right infrastructure and investment from the right developers. The existing process to marry these demands is complex and time-consuming; it also involves multiple teams, datasets and approvals.

Arcadis has been working with GMCA (using our “design thinking” approach focusing on the end-user) to co-create City Analytics, a solution that provides GMCA with a platform to support data and evidence-based decision-making by utilising a mix of land, building, commercial and geospatial planning data to demonstrate and model the impact of planning decisions across a range of assets and variables.

The result: The platform has the potential to create robust and defensible plans and to guide investment across regions to build more of the type of homes we need. By breaking down the siloes and integrating data held across GMCA’s 10 districts– alongside publicly available data and external providers and companies– and presenting the data with a simplified interface, processes have become more consistent and streamlined.

Northumberland: Optimizing Investments to Provide Better Value to Customers

Utilities companies are under enormous pressure to provide high-quality, uninterrupted service to their customers. SEAMS’ work with Northumbrian Water Group (NWG) is helping them to optimise and plan for better investment. SEAMS have helped NWG achieve their aim of embedding an in-house analytical and modelling capability to ensure data-driven decision-making became business as usual, reducing dependence on external contractors.

SEAMS worked side by side with NWG to train the team over the course of five years. The first model focused on clean water infrastructure assets, predicting the forward investment needed to maintain service levels for customers including bursts, interruptions and repairs on assets. In the second exercise, a waste water model for their gravity network was developed, which helped NWG understand the impact of sustainable, long-term investment over 25 years on collapsed sewers, blockages, flooding and pollution.

Through the co-creation of these models, the internal team now has the skills to complete a full range of services including data infill, probability of failure analysis, performance analysis, quantification of deterioration, whole life cost modelling and investment scenarios.

The results: NWG are empowered to optimize their investments and provide better value to customers. Analytics and modelling– a key part of their planning process– have improved their decision making, and they are investing in the right assets at the right time.

Tools For a Sustainable World Today, and Tomorrow

Data analytics allows us to maximise the power of the data at our fingertips. Simulating models and demonstrating outcomes enables us to help our clients make the most informed, data-driven decisions with the ability to track performance on their our long-term goals.

The methodology applied in these examples proves that through standardized data collection, cities have the tools to measure, monitor and improve their performance, creating sustainable environments that will improve the quality of life for us all.

The Ruhr region known to be a steel and coal mine hub in Europe is reimagining the entrepreneurial spirit which was it’s foundation 150 to 200 years ago. This time lies a huge opportunity for data-driven startups to innovate and collaborate with the big traditional corporates that exist in the region. Here is look how.  

The year 2018 is a significant one for the Ruhr region in Europe. At the end of this year, Prosper-Haniel mine in Bottrop, the last coal mine in the region will be closed. Decisions which were made more than a decade ago are now reaching its inflexion point. To refresh the memory : In February 2007, the German federal government, the NRW (North Rhine-Westphalia) and Saarland State governments (where hard coal mining still took place), and RAG, the coal mine company owning and operating all of the Ruhr’s coal mines since 1969, reached an agreement on the final phasing out of underground coal mining in Germany and its subsidies by 2018. Over the last 50 to 60 years, there has been an economic transition from a steel and coal based economy in Ruhr to a serviced economy which now has huge potential for smart manufacturing and internet of things. This has lead to discussions whether Ruhr area could be the new hot spot for startups and tech innovation? The region is one of the five largest conurbations in Europe and is home to approximately 5.2 million people.

Whenever there are significant changes like these, there are also immense opportunities. While this is an end of an era of coal mines, it is a birth of a new one. When we ask Marc Weimer-Hablitzel, Lead, Data Hub, Gründerallianz Ruhr about what this will change for existing big companies in Ruhr and what opportunity lies ahead for startups in the region, he says, “ If you look at data science from the perspective of B2B traditional companies, they are just starting to understand that data is not just a technical topic- rather it is their main source for competitive advantage which further leads to revenue generation. They realise that there is huge potential if you apply machine learning in processes like quality measures, energy consumption, etc. This industrial revolution in Ruhr region emphasises on the role of companies to care more about reducing pollution and saving energy. The right use of data by these traditional huge corporates can solve the problem. ”

Germany has a target of reducing greenhouse gas emissions by 55 per cent by 2030 compared to the 1990s levels, and data-driven solutions can help in a big way to achieve this goal. And, startups are the ones who are innovating constantly to come up with these solutions. What is missing is a framework where startups can connect to these giant companies who have existed for years.

Catering to this, Gründerallianz Ruhr supports the innovation ecosystem in the Ruhr region in Germany and bundles all startups activities in the area. The goal of this initiative is to strengthen the local startup scene and nourish it’s growth.  Gründerallianz Ruhr was founded by Initiativkreis Ruhr and the initiators of „Glückauf Zukunft!“, RAG-Stiftung, RAG Aktiengesellschaft and Evonik Industries.

What problem can startups solve in the Ruhr region and how?

Marc draws an interesting analogy between how the Ruhr region started with an entrepreneurial and startup mindset around 150 to 200 years ago when people were driven to build a steel and coal based economy from the region of Ruhr to now when the same mindset will bring the next wave of change in the region through data-driven solutions.

He says, “We want to bring out the same attitude of innovation but in a way where data science and machine learning is being adopted by traditional B2B companies. We want to make the Ruhrgebiet an attractive location for data-driven startups .”  The Data Hub Program by Gründerallianz Ruhr creates the framework conditions and promotes cooperation between startups and companies based on concrete use cases.

A whitepaper by The World Economic Forum says that while the European economy is growing faster than the US economy at present, the European innovation ecosystem is stronger than ever before but still trails other markets in terms of available nance and collaboration. The European economy’s continued success requires a better connection between both worlds: the traditional businesses and new market players. Both benefit from collaboration to create markets they would struggle to create alone, and to ensure they remain at their competitive edge. This exactly resonates with the mission of what Data Hub Program is trying to do by connecting startups with big corporates.

The program is now inviting startup applications to solve challenges/use cases listed by top corporates such as Vivawest, Kolumbus and RAG in Ruhr Region and Gründerallianz Ruhr is the glue between these alliances of startups and companies. Marc shares, “ We are the facilitators between the two. This is a time when companies feel that they should be using data-driven solutions and they are beginning to share information with the outside world. For the last 15 years,  data has been a CTO topic and it was hard to imagine that it could have some return on investments or be monetised. It was collected and stored as a practice, but without a purpose. CEOs have now started realising that good customer experience comes from developing data in the most efficient manner. In a recent etventure study, we asked CEOs what’s the most important source for competitive advantage and 63 percent of them answered it was “data”. This was not the case five years ago.”

The challenge right now is that while corporates have huge amounts of data and though they also have data scientists employed, these scientists end up working in silos struggling how to use heaps of data, not being able to differentiate between good data and bad data. On the other hand, there are young startups who have data sets and don’t know how to use them as they don’t know which are the use cases available by big companies.  

“We are the point to bring these two sides together and reduce friction that is hindering them to work together.  The legal work to collaborate with a big company could be a lot for a startup and we take care of these necessary logistics. In return,  we expect startups to deliver a solution that gives a corporate enough confidence that they can solve the problem overcoming the first hurdle by developing a proof of concept,” shares Marc.

What are the use cases and how are they developed?

One of the use cases that is open for startup applications at this point is listed by RAG, one of the ten partners of Gründerallianz Ruhr.

Sample this from the perspective of RAG: The last two RAG mines will close at the end of 2018. As a result, the challenges of post-mining and structural change will become the focus of our activities starting in 2019. We are already setting the course today – for example with a variety of measures for socially responsible personnel adjustment, the planning and implementation of responsible water management or the revitalization of former mining areas.

To help keep the Ruhr area safe, RAG conducts flights that measure exact ground elevation levels. These measurements reveal ground depressions that can be used in the future. Spotting these depressions is a difficult task. Can we develop an algorithm that spots ground depressions better than the human eye? There are similar challenges by a total of seven big global companies based in Germany at present and startups can apply by 31st October in an attempt to solve these use cases.

Marc explains the unique data thinking approach which is used to develop these use cases, “ Data Thinking is a new innovation method that moves away from traditional big data consulting. The focus is not on technology and data collection, but on data design for high-potential use cases. In the systematic identification of these data use cases, the data thinking method follows that of design thinking – placing the needs of the customer or user at the center of all considerations. Data Thinking is a highly iterative and cost-effective data science approach that demands fast feedback directly from the user and continuously tests, analyzes and optimizes potential solutions and hypotheses.”

With the right financing for startups and a framework where big corporates can collaborate with younger innovative companies- there possibly is a big industrial revolution waiting yet again for Ruhr region. This time backed by the adoption of machine learning and data science.

Internet of things or IoT has become one of the most influential technologies, currently connecting more than 23 billion devices. This number is expected to reach 30 billion by 2020 and more than 60 billion by 2024. But the future of this technology is largely debatable, considering that it’s plagued by a poor business model, inadequate connectivity and scalability, as well as lack of overall trust due to cracked security.

Researchers have proposed using SCN or service-centric networking as a means of providing global connectivity and reliable scalability, however, this approach cannot guarantee service incentives, nor trust and security. Another solution for this issue is to use blockchain technology as a means of achieving security, trust and incentivizing IoT.

That said, these types of solutions require a well-connected, stable network to operate, a luxury in dynamic and mobile IoT scenarios. Enter BlockCloud, a new technology that takes the best of both worlds in order to provide a viable solution for increasing trust and scalability. But what is BlockCloud and how does it help overcome above-mentioned IoT issues?

Block-Cloud ICO

BlockCloud refers to a new blockchain-enabled TCP/IP architecture that combines blockchain with SCN as a means of powering IoT solutions. It is not restricted to otherwise clunky platforms and instead operates as a highly-mobile, layered solution that doesn’t suffer from the same scalability issues as do other blockchain options. Block-Cloud may be facing fierce competition from similar, as well as centralized solutions, the benefits it offers to provide it with a fighting chance of entering the growing IoT sector.

The market opportunities

As various new technologies seep into our everyday lives, such as autonomous vehicles, augmented reality and smart healthcare and so on, the world we live in will become progressively device-oriented. Having a streamlined and interconnected future does have its fair share of issues, namely the poorly developed IoT architecture.

There are many, sceptred solutions trying to fix these underlying issues, but BlockCloud is trying to solve them in a more comprehensive manner by propelling mobility, security, scalability, and trust while allowing for a fair distribution of profits among the market participants.

In the case of Block-Cloud, blockchain will be responsible for the applications layer and the underlying architecture will be made using SCN tech. This poses a potential risk, as the team behind BlockCloud is trying to combine two previously unaligned technologies and make them work as a comprehensive, unified solution.

Five main components

• A blockchain integration layer
• Service Access layer used for communication
• Service verification using proof-of-service
• CoDAG or Compacted Directed Acyclic Graph mechanism for transaction logging
• Truthful (continuous) double auction as the pricing mechanism

Focusing on a new PoS (Proof of Service) consensus, Block-Cloud is trying to resolve blockchain transaction simply by having the relevant devices share information between themselves. This will allow for a clean verifying environment, with unsavoury participants being penalized and restricted from using the network.

As a multi-layered solution, BlockCloud wants to introduce a couple of new concepts:

• Edge Computing – which enhances current connectivity levels between different computing devices
• Internet of Vehicles – a communication solution for smart vehicles
• Smart Home – blockchain-enabled functionality to power smart homes of the future
• Smart Health – system for improved delivery of various health-oriented services using IoT devices
• Sharing Economy – a decentralized, trustless ecosystem powered by Block-Cloud

The pricing mechanism is integrated as a part of BlockCloud technology in order to create a marketplace for both service providers and service utilizers. The marketplace will play a major component, as there will be an increasing number of companies who want to contribute their services, but also have an access to all this new data. Solving the underlying issues with the infrastructure and building new application layers on top offers great potential and provides immense value for the future.

As a cloud-based blockchain solution, it can be used by virtually everyone without having to pay for a dedicated hardware infrastructure. The BlockCloud whitepaper discusses at length all the beneficial possibilities this tech offers, including the next-gen IoT computing by enhancing its integration within blockchain.

Block-Cloud ICO crowdsale

The platform is trying to create an $80 million market cap, with $15 million raised through fundraising. Unlike similar crypto crowdsales, BlockCloud is trying to raise $12 million via private sale investors, instead of trying to secure funds via a public offering. The company is going to sell 1.5 billion BLOC coins with an appraisal of $0.008 apiece, with 20% of tokens being released before the exchange listing. Two months after the initial release, BlockCloud will release 10% of tokens per month and continue doing so for 8 months.

This technology promises to be the next big thing and completely revolutionize the way we interact with different IoT devices, but also the way different IoT devices communicate between themselves. If it takes off, BlockCloud could have a huge impact on digital marketing and numerous industries such as automotive, financial, healthcare, computer sciences, you name it.

BlockCloud-specific challenges and potential weaknesses

While the concept has been worked on extensively on paper, the project has not been put the test as of yet. The team is planning to release a comprehensive test net around the third quarter of the following year, but time will tell whether their project will be delivered without any delays. This can be an issue for early investors who not only have to wait a whole year for a proof of product but also have to hedge their bets as they’re taking large risks investing in a potential merging of two already new technologies.

While a degree of crossover does exist between the software and its protocol, the development of a complete 4-layer architecture will require time, likely extending the roadmap into the last quarter of 2019. The team might have displayed considerable technical knowledge regarding the project, they are not quite versed with the actual development of the blockchain-related protocols.

BlockCloud is still in its early stages, but its conservative timeline might require bringing on new and experienced blockchain programmers in order to deliver a viable test net on schedule.

The takeaway

Block-Cloud is one of many recent solutions claiming to be able to resolve the underlying scalability and security issues associated with the growing IoT sector, however, its offering does seem to be a comprehensive one. The team behind BlockCloud were some of the top movers in big data analysis, which only reaffirms their credibility in both the business and academic fields. While the whitepaper doesn’t go into too much detail, the benefits offered by this technology cannot be overstated. Whether it will be implanted or not, remains to be seen.

Kevin Ashton, the man who coined the term “Internet of Things” (IoT), wrote in 1999:

“If we had computers that knew everything there was to know about things – using data they gathered without any help from us – we would be able to track and count everything, and greatly reduce waste, loss and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best.”

In 2015 Kevin was again quoted in Smithsonian Magazine:

“In the twentieth century, computers were brains without senses—they only knew what we told them. That was a huge limitation: there is many billion times more information in the world than people could possibly type in through a keyboard or scan with a barcode…”

What Kevin didn’t foresee was the invention of the blockchain—a technology that allows you to store data in a transparent and unchangeable way—which offers IoT a solution to many, if not most, of the security problems preventing it from realizing its full potential today.

Why should IoT meet blockchain?

Combining these two concepts allows companies and even consumers to directly monetize the “billion times more information” that is generated by the approximately 30 sensors in your car, engine sensors in airplanes measuring 5,000 elements per second, and billions of other sensors in every part of our daily lives measuring things like weather effects, pollutants, location, fuel, temperature, humidity, moisture, sound, vibration, wind resistance, pressure, weight, electricity, and more than 300 other types of elements.

GE and Cisco Systems, two of the top companies in this field, estimate that we will have 1 trillion sensors in the world by 2020.

With this, one can imagine the possibilities of opening up gazillions of data points measured every day by these one trillion sensors to public (or private) marketplaces built on blockchain technology specifically designed to sell and buy this data.

To give an example: A car company could track the relation between air resistance and fuel consumption in their cars, stream that data to the marketplace in real time and sell it directly to manufacturers of car parts, who can then use these metrics to improve the design of the overall structure to reduce fuel consumption.

Yes, it is true that helping competitors in this way could be negative – but what if the income from selling the data is worth more than the competitive advantage of keeping that data to yourself in the first place?

Similarly, what happens if you, the owner of the car, could directly sell this data to any number of companies interested in improving different parts of the vehicle, but who lack the myriads of data to effectively do so?

Selling wind resistance data is just one out of maybe a hundred marketplace use cases which are possible today, thousands within the next 5 years, and perhaps hundreds of thousands in this century.

I haven’t even touched on the potential use cases outside of marketplaces, like trustless supply chains or autonomous vehicles talking and microtransacting with each other.

But if these use cases are so well suited for blockchain, then why are we only talking about hypotheticals and not real-world examples?

We can imagine it, but can we build it?

The sensors exist. The blockchains too exist (however not in the most compatible manner). But the glue to bind IoT and blockchain together doesn’t exist. Especially not the kind of tamper-proof “super glue” which is needed to make most of these use cases… useable.

For the past years a very limited amount of top corporations have been testing basic use cases which will be deployed sometime next year, but nearly no one is using IoT-blockchain solutions today, and many soon-to-be-launched blockchain projects in e.g. the supply chain sector will only be using the “blockchain” part of IoT-blockchain to track their goods, but still input the data manually.

Will IoT and blockchain eventually collaborate or will they continue to simply co-exist?

To summarize, these are the problems standing in the way of an autonomous future:

1. There is no software standard for connecting blockchain to IoT.

As a result, every single vertical company has to build the entire use case AND infrastructure from scratch, which can literally take years.

2. Existing blockchains are immature and are outdated quickly.

No company wants to commit to a technology that a) doesn’t work properly for the intended use case (due to slow validation times or hardware constraints on the IoT device), or b) could be outdated in 6 months.

No one knows which distributed ledger technology (DLT) will be the best one for X use case in Y years.

Solution?

An embedded wallet for the Internet of Things seems to be a  promising solution at the moment, basically making a “Ledger Nano” for machines. This would allow almost any device to connect with any DLT, and by not being an IoT gateway it retains the security benefits of the blockchain while by not being a full node it dramatically reduces the load on the IoT device.

This could be one of the ways to go if we want a fully scalable infrastructure for an autonomous, machine-driven economy in the decades to come.

Conclusion

Blockchain is still comparable to the Internet of a few decades ago. The Internet we know today looks nothing like it did back then, and the same can be said about blockchain looking forward. In essence, blockchain can add a whole new dimension to the Internet by introducing new standards of data transparency and peer-to-peer communication. A few decades from now, I reckon the blockchain (and IoT) use cases we see today will look like the incredibly slow Internet we saw when it became public in the 90’s. Devices can now not only transfer data but also monetary value, opening up brand new markets worth trillions in the coming years

For those with chronic health issues or life-threatening illnesses, the line of specialists and procedures can seem endless. Moreover, each time a new practitioner or clinic is visited, the patient is required to fill in long and complicated forms – especially if a medical insurance is involved.

It’s now clear that the industry has to undergo some major changes, starting with data management. Over the last decade, hospitals, medical practices, medical schemes and research facilities have been digitizing their data in an effort to warehouse it effectively. They are now starting to turn to consulting firms to adopt big data as a solution to data storage.

Does Healthcare Need Big Data?

When most people hear the term “big data,” they immediately think of large data volumes. If this were the only reason for healthcare bodies to adopt the new way of storing data, they could manage without, because most of them could contain what they have in a robust relational database.

In 2001, Doug Laney described the “3 Vs” of large and complex data as “Volume, Velocity and Variety.” While healthcare CIOs could benefit from all three, the emphasis should be on variety. This is a trend experienced not only by healthcare, but by all industries.

Apart from traditional patient data contained in text, there are various images and sounds recorded, from x-rays and ultrasounds, to Doppler and MRI imaging. Some doctors even prefer that their conversations with patients be recorded for the patient’s benefit. This collection of disparate information is generally unstructured and cannot be ordered in the neat tables and columns of a relational database.

This is where big databases, like Hadoop, score. However, it is one thing to store big data and quite another to retrieve it in a meaningful way. Data scientists who are able to design methods to extract meaningful information from the non-sequential and seemingly random big databases are now in high demand.

These skills are expensive and in short supply, but the IT industry is starting to deliver solutions that make meaningful data extraction easier. There is also a move to a hybrid database structure, where data is stored in both a relational and a “NoSQL” database.

Where healthcare entities have tackled this hurdle, the result is a holistic view of the patient, which removes some of the complexity of diagnosis for the medical practitioner and makes life simpler for the patient.

It also opens the way for the move to machine-to-machine (M2M) communication and the use of artificial intelligence to sift through and analyze data transmitted from the sensors gathering it. The future promise is analytics that will monitor health like never before – but there are also further issues that need to be addressed, such as data privacy and security.

Big Brother is Watching Your Vital Signs

The use of sensors to monitor everything – from whether a patient took the right dose of medication at the right time, to whether their insulin levels are in check – is one of the big growth areas in the Internet of Things (IoT).

Combining this with the global adoption of mobile devices, especially smartphones and wearables, means our health can be monitored on a continuous and proactive basis by AI. If a problem is detected, a healthcare professional can be alerted to take appropriate action.

While the benefits of such constant surveillance of one’s health, especially in cases of patients with chronic or life-threatening illnesses, are obvious, they also bring added risk. Where there is a sensor, there is vulnerability and the risk of cyberattacks.

Recently, Johnson and Johnson warned diabetes patients of the possibility of hackers affecting their insulin dosages. Although the probability is low (and possibly would have been lower without the publicity), this does raise fears of attacks on prominent people who need to manage their insulin.

Data privacy and patient confidentiality is also at risk. One of the advantages of paper and siloed electronic records is that they are not hackable. When healthcare organizations consider mobile application development to support IoT devices, they must also ensure that vulnerability is kept in check.

Despite these risks, wearables, sensors and mobile applications are advancing the frontiers of medical knowledge at an ever-increasing rate – especially in combating and preventing life-threatening diseases and autoimmune illnesses.

Big Strides in Combating Cancer

The chances of developing cancer in one’s lifetime are estimated to be as high as one in two (for US males). However, the industry is making rapid strides in combating and preventing these cancers.

One of the key factors contributing to this war on cancer is the ability to gather large volumes of patient data during clinical trials, using sensors rather than surveys and interviews. Patients with multiple myeloma, a very painful blood cancer, are to be monitored via a combination of wearables and smartphones, with data uploaded to a cloud platform. These patients are not being monitored for their response to drugs, but rather for their quality of life. Their sleep patterns and general mobility are measured to get a better understanding of how the disease and subsequent therapies impact their daily lives.

Such research can also be used to inform and assist health professionals. Doctors of today need to stay on top of developments in the efficacy of treatments. A platform that both collates oncology research and keeps doctors informed helps to improve cancer treatment on two fronts.

Future Predictions

Healthcare is a sector in which IoT and big data will play an increasingly vital role. While big data is not currently required from a volume or velocity perspective, this will change when data from billions of sensors will need to be stored and analyzed. 

Another growth area will be predictive analytics, once the storage and retrieval of all this data is mastered. Benefits can be derived in mundane areas such as inventory and waste management, as well as patient care.

It is important to note that the improvements in healthcare are not limited to the first world. In fact, the real changes will be seen in the treatment of patients in areas of the globe that rarely see health professionals. The UN’s Sustainable Development Goals require that healthcare be radically improved in the most underserved areas.

A combination of big data and IoT will undoubtedly be major forces for change in the years to come, as global connectivity increases and technology flourishes.

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In the field of data, ease of access and portability are becoming increasingly important. Businesses need to utilize data to remain competitive; meaning, they must have quick, real-time access to their data. Now more than ever, accessing this data can mean the difference between competition being one step ahead or behind.

The General Data Protection Regulation (GDPR) hopes to make personal data more accessible to subjects, prompting discussion of why ease and portability may have detrimental effects in addition to positive outcomes.

Why Portability Is Important

Data portability is the concept behind protecting users from having their data stored in various places
that are incompatible with one another. Making this data portable means that consumers will have an
easier time accessing their data across a number of channels and platforms. Portable data is important beyond convenience. For one, data portability provides more transparency since it enables people to search and analyze relevant data from organizations. Additionally, portable data is an excellent pairing with analytics, since portable data can link with several types of activity — everything from tracking your travel distance when using a commuter vehicle to monitoring energy usage on-the-go.

Portable data’s high level of transparency serves as an extension of data protection legislation. For example, GDPR lets people exercise their data access rights free of charge. This increased access will likely result in more people exercising their right to find out the information that organizations have on them. In the long-term, the use of analytics with portable data creates greater versatility and personalization.

Services can utilize portable data to create an accurate picture of a consumer, designing their service around that consumer’s preferences and analytical behavior. GDPR requires data collection to be transparent. Nonetheless, the regulations align with the vision of utilizing data to help people make optimal decisions.

For example, a grocery store app can utilize the portable data of your past purchases to provide relevant sales offers and recipes. Alternatively, a doctor can use portable data to quickly find information regarding your blood type, diet and general activity. Portable data has the potential to enhance our lives and serve as a more transparent form of data.

Businesses are also using data virtualization and federation to make data work better for them. Data virtualization provides a simplified view of real-time business data, creating a useful hub for your analytics, applications and users. Data federation is a type of data virtualization, improving the ability to query and aggregate data by applying specific architecture.

The Struggles of Portable Data

Portable data offers a variety of exciting opportunities in helping with decision-making and user experience, though it has its share of limitations. For example, the transparency of portable data prompts questions regarding security. Third parties routinely access and scrape password protected sites, asking users for login information. For someone who uses the same password across multiple sites, it only takes one hacker to manipulate this process and gain access to someone’s entire digital existence. Poorly implemented data can provide a lucrative endeavor for identity thieves and phishing attempts.

Additionally, although GDPR requires data to be recorded in a commonly used format, there is no guarantee that data will be standardized across platforms. Where one business may label a field “Location,” another one may label the same field “Locale.” As a result, it’s no sure thing regarding data’s ability to be imported or aligned with other data unless manually processed.

GDPR also states organizations must respond within a month if they receive a request under the data portability right. While this sounds fine, issues arise regarding competition. For example, a company may provide general consumers with requested data quickly, while holding off on giving the same data to what they perceive as competition.

For these responses, large businesses will implement automation to deal with a flurry of requests, while smaller companies respond in an ad hoc way. As a result, access to such data will vary depending on whether the business perceives you as competition.

Portable data and GDPR will strive to prevent those taking advantage of data requests, purely to build their marketing databases – though the success of that prevention is no sure thing. People must choose wisely in granting access, while still complying with GDPR.

The Future of Data Portability

Despite its exciting aspects, data portability may discourage innovation. GDPR’s role in creating standards for data may force services toward choosing compliance over innovation, with there being ample debate over what defines essential data. Additionally, data portability may place more power in the hands of large companies, who have the resources to automate data requests, dedicate a department to compliance and reach into their deep pockets to pay any GDPR fines.

There’s also the future potential of data importation being a mandatory requirement for all site sign-ups. In their efforts to deter spammers, sites may opt for a recognition method utilizing portable data, which can identify an individual online. As a result, consumer and privacy rights groups will likely find fault in mandatory data importation for service or site sign-ups, labeling it “data greed.”

More positive results include that data portability can encourage people to donate data for good causes, especially in medical research. Portable data that can track one’s medical statistics can provide incredible insight into the progression of diseases and potential methods for treatment. Ease and portability of data will be a significant talking point in the coming years. Businesses and consumers alike will undoubtedly weigh the pros and cons of frictionless data as GDPR makes its entrance into the conversation.

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All new technologies are becoming a part of our environment, but many of them remain unnoticed or incomprehensible. For many people, beacons are one of these mysterious items. Many IoT applications in large industries –such as retail and warehousing – use beacons everyday, but these small devices go unnoticed. Although the mass media has covered IoT technology repeatedly, hardly any outlets explain how these work.

What is a beacon? 10 fast facts

• Beacons are small, wireless sensors that are normally placed in a casing;
• The technology uses Bluetooth Low Energy (also called Bluetooth Smart or Bluetooth Version 4.0+) to broadcast radio signals or, simply put, to communicate with other smart devices;
• The broadcasted beacon signals can be captured by smart gadgets, like phones, to call ad-hoc actions;
• Under the beacon’s casing, there is a small ARM computer with a Bluetooth Smart connectivity module, which is powered by a battery;
• The module runs on firmware, a piece of software installed on beacons;
• The max Bluetooth Smart playout is 257 bytes, which is insufficient for embedding any media content;
• As the computing power is limited, it can be used for processing sensor data (information about signal power) and encrypting a beacon’s ID;
• There is a small antenna from the CPU;
• The antenna is built in to broadcast electromagnetic waves with specific length and frequency (2.4 GHz radio waves);
• The technology is primarily used for mapping and location services using the RSSI (received signal strength indicator) estimate. The location services are normally conducted by a framework (SDK built in the core location), which includes noise reduction algorithms to make the signals smoother and the results fairer.

It’s no secret that the future of business is hyperconnected. A beacon is just a new Bluetooth-dependent technology, which is a good investment in this connectivity. You might wonder: If beacons are similar to GPS in their functionality, why would I need them?

The answer is that both technologies are used for tracking. GPS is now widely used for tracking assets (themselves) outside or inside a room. Beacons are used for tracking everything in between. The beacons act with up to a 100-meter range as a linking bridge that helps to provide total tracking rather than controlling using indoor/outdoor devices.   

3 Beacon IoT Use Cases

Beacon developers are still struggling with a number of unsolved issues, including designing better antenna shapes and increasing signal distance. Despite this, beacons are increasingly being implemented in many retail IoT solutions, and are projected to extend further into the industrial and healthcare spheres. Several of the use cases are presented below.

Beacon IoT Ads Application

The retail application is a beacon IoT solution that uses Bluetooth geolocation to give shoppers valuable information about sales and other promos that they may find in their vicinity, for example in a shopping mall. The information is displayed on their smart Bluetooth-supporting devices.

By connecting directly with potential customers, the beacons help vendors focus their marketing campaigns with minimal effort. The beacon advertising applied by Rainbow Light boosted the average sales rate of vendors using it by 15%.

Among the beacon IoT products in the retail segment, there are also products supporting shopper mapping, customer loyalty management, in-store digital media and others.

Beacon-Powered Smart Shelves

A beacon-powered smart shelf is supplied with a radio frequency identification reader (RDIF). The reader can be built into the shelf or placed above, underneath or behind the shelf. What can it do? The reader scans the targeted items on the shelf and then notifies the backend system of the items placed on the shelf.

This allows for a better understanding of customer demands and preferences. However, the smart shelves can be no less valuable for plant storage in the industrial sphere, where it can allow fast and easy stock control and tracking.

Today, this beacon IoT project has been applied by the U.S. supermarket chain Kroger, but it has the potential for many other business segments.

Beacon IoT App for Local News

The internet allows us to get fresh news from around the world in a blink of an eye. Beacon technology helps users to experience location-based storytelling.

UK-based startup OtherWorld applies Google Eddystone beacons across Manchester to provide local stories to pedestrians’ smartphones. This helps attract tourists and tell the citizens of Manchester about the latest news, events, offers and facts about the city. When a pedestrian exits the beacon range, the story disappears from the device screen.

A similar app has been already used in Australia for visitors of The Walk of Honor.

What are the benefits of beacons?

Even though the beacon is not as popular of a tracking technology as GPS, it is being progressively implemented by IoT software development companies. Shopper mapping, beacon advertising, customer loyalty systems and many other projects have used beacons, and IoT solutions have been turned into reality.

Today beacons are being used in the healthcare and industrial spheres. BLE beacon solution providers see the possibility for implementing them to enable better asset tracking in hospitals, enhance equipment utilization, improve information flow to hospital staff and increase the overall quality of medical services.

Beacons combine both offline and online campaigns, as well as connect indoor and outdoor devices into a single self-monitoring network. Using a Bluetooth connection, the technology is cheaper than alternatives and easier to use and support. Though developers are continuing to work on technology improvements, the beacons’ IoT use cases show the viability and benefit of these projects.

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It seems like every quarter a new McKinsey report predicts that this will be the year trillions of dollars of IoT potential is unlocked. But while the amount of data IoT produces has skyrocketed, we’re still waiting for that return on investment. The good news is, the reports aren’t wrong. Actionable data can, in fact, enable data scientists to accelerate business growth. The bad news is, businesses haven’t had access to the right tools to make their data actionable. In fact, examples indicate just 1 percent of operational data is being used in enterprises.

The primary exhaust of IoT devices is time series data, i.e. sequential events indexed by time. Working with time series data is tricky. Whether it’s information coming from a machine on a factory floor or the trunk of a self-driving car, events occur in uneven intervals, different sized windows and formats that vary across datasets. Time series data is unique in that it’s write once, non-deletable, non-transactional and non-relational. It also has different access patterns, such as looking for behaviors and patterns across time rather than joining on a specific field.

Unfortunately, time series data often gets grouped with other types of data such as CRM records, log data and general analytics. This results in tools that don’t work, leaving data scientists and their organizations without an effective solution for leveraging their data or making it actionable.

Unique Hurdles and Advantages

With traditional datasets, data scientists often look for relationships that can be expressed easily and efficiently with SQL. For time series data, however, data scientists need to look for behaviors and patterns in events streaming across time. They need to look for specific sequences, how often they happen and the characteristics of the data during these windows of time in order to gain insights and build models. Relying on SQL to do time series data lookup can quickly become very costly and inefficient.

Luckily, time series data can be sampled. Data scientists only need a small portion of the extracted data to understand its overall shape. This initial sample can fit into memory and be analyzed with pandas or a Jupyter notebook. It may even be small enough to efficiently do full table scans inside of a No-SQL or SQL database. The small sample size of time series data makes it possible for data scientists to quickly explore the data for patterns and write small programs to transform the data or add new features.

Performance and Workflow Challenges

Eventually, though, analysis needs to scale. Managing the performance and workflow of time series analytics from a small sample to production-level volumes can be extremely challenging for data scientists. For instance, even simple pre-processing and data transformation steps need to be moved to distributed batch processing workflows. Moving the extract, transform and load (ETL) program from local scripts into a production-ready data pipeline requires rewriting entire programs for environments where table scans just aren’t feasible.

The level of experimental interactivity and flexibility during the data exploration and model development process is directly related to how valuable the time series data insights will be. When data scientists are forced to wait hours or days for long batch processing pipelines, they lose interactivity, iterate less and find suboptimal solutions that often have unintended consequences. For instance, because it’s so inefficient to adapt or tune an ETL, early assumptions aren’t tested, leaving dangerous biases and failure modes in a system. These problems compound when data scientists need to join streams of data together, each with different states, features, ETL requirements and schemas. The resulting pipelines are extremely fragile, and they break frequently. Before you know it, the majority of a data scientist’s time is spent troubleshooting.

Crucial Best Practices

Creating useful metrics from time series data requires looking at high-level features not visible in the raw data itself. For instance, instead of merely looking at a temperature value, it’s useful to extract degrees/hour change. Useful patterns are discovered by combining derived features from multiple data sources into higher level query expressions.

For data scientists looking to effectively leverage the insights behind their organization’s time series data, acknowledging and prioritizing scaling challenges is critical. Maintain a high level of interactivity so you can explore and iterate quickly. Recognize the unique behaviors complex events will reveal, and be ready to test as many combinations as possible. In doing so, data scientists can productively work with time series data to help a business grow.

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Today, professionals in HR departments are being forced to adapt to technological innovation – whether it’s in recruitment, hiring, payroll, or even the management of benefits. But if we look closely at what’s garnering the most attention right now, it’s one thing: data. HR managers must begin to understand the significance of data-driven technologies and how they will affect HR in the future.

Let’s take a closer look at the significance of people analytics, the impact of IoT on HR and application of the latest HR technology trends.

Significance of people analytics

People analytics refers to the utilization of data-driven techniques for managing people in the workplace. These analytical approaches include reporting, metrics and the predictive analytics of experimental research. Business leaders can make decisions concerning their employees based on the in-depth analysis of data instead of outdated and ineffective methods of risk avoidance, decision making and personal relationships.

In today’s business environment, people analytics helps examine the efficiency of processes and practices. It helps businesses understand how to make objective and informed decisions concerning people, and assists in directing the actions of HR, solving problems of individuals and uncovering new insights.  As a result, it helps entire businesses run more smoothly when it comes to employee recruitment, performance and development.

The impact of IoT

IoT influences HR management by harnessing large amounts of data from people, providing HR management with new strategies to maximize agility. In addition, IoT enhances employee productivity in various ways. First, it provides the means of gathering more reliable data – as opposed to gathering data manually, which is prone to human error. HR managers can use data from the employees, for example, to schedule brainstorming meetings during their most productive periods. IoT can also boost employee satisfaction and health through fitness trackers and associated monitoring devises, as well as track employee progress.

Latest HR trends

Big data is continuously influencing the workforce on a large scale, highlighting company and employee needs and behavior with remarkable precision. We can now be transparently aware of important information such as who should be hired for what role, how they might do their job and how they might feel on the job.

The flexibility of remote work has modified markets around the world. Organizations are looking to outsource as many jobs as possible, especially the ones that they need not handle on a regular basis. Since workforce fluctuation has created a very time-consuming problem, this policy is heavily influencing HR departments.

Nevertheless, we have to depend on the fact that our future employees will also have access to the open global market to seek new opportunities, which indicates that companies will have to be highly competitive when hiring talent. That includes offering remote workforces with expanding benefits, more flexible work hours and the promise of a healthy and productive working relationship.

Policies and regulations regarding IoT will leave a lasting impact on HR. When it comes to safety and privacy in the IoT world, there is undoubtedly a demand for responsibility. As far as technological advancement is concerned, governmental support will present a complicated issue. It is yet to be determined how HR departments will face the consequences and liabilities that new technologies present, but approaches will differ among organizations based on their structure and goals.

Looking ahead

Big data and IoT have become critical for HR. An HR officer’s set of tasks is already highly dependent on continuous micromanagement and a hierarchy of communications – both of which data can influence immensely. HR teams must keep up with new developments to optimize their work and remain competitive.  Humans are evolving alongside advancements in technology, and so, HR must evolve too.

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We have liftoff! Data Natives 2017 is just a couple weeks away, and we have never had this many industry-altering speakers,  workshops and networking opportunities. Anyone working in Data Science would be wise to get tickets, especially if the fields of Mobility, Health and Government impact your work (which they do).  From November 16th-17th, top business leaders along with the most informed members of the data science community from both the US and Europe will be meeting in Berlin to share the latest advances in their fields – making for a highly informative and possibly career-altering couple of days.  Here are 5 reasons why this year in particular is the best year yet to attend Data Natives. 

1. Speakers and Workshops:

We have over 90 speakers and counting this year. That includes big names like Booz Allen Hamilton’s very own Kirk Borne,  FinTech entrepreneur and Luxembourg House of Financial Technology (LHoFT) CEO Nasir Zubairi, HealthTech expert and Director of Digital health DACH at IBM Germany’s Bart de Witte, world-renowned AI expert Toby Walsh, MIT Media Lab research Nan Zhao and many more.  Other prominent names include data-driven behavioral scientist Suzy Moat as well as Forbes Europe’s celebrated 30 under 30 entrepreneur Mari Hermans – head of business intelligence at solarisBank. Whether concentrating on IoT, FinTech HealthTech, GovTech, Machine Learning, Mobility, AI or any other field of disruption, all of our speakers are ready to share the trends and topics that are redefining entire industries. The same goes for our workshops. Have a look at our IoT workshop from Alexandra Deschamps Sonsino and learn the latest in language processing from Francisco Webber, founder of Corticol.io.

2. The Unconference:

Our unconference takes place in its own room. Attending an unconference means having the rules of the game flipped.  Instead of a lone speaker, the event’s topics are determined by the crowd at the beginning of a session, with attendees writing their ideas on a massive white board. Then, instead of a lone speaker, EVERYONE participates in an open discussion. Disrupting the more typical norm of routine schmoozing that characterizes many a networking session, an unconference is specially designed for events where many attendees have lots of expertise and firsthand knowledge in their fields and are ready for rapid fire engagement.  Benefit from the Hone your selling approach with feedback from some of the best minds in data science and learn how to perfect your informal pitch.

3. The Venue and Format

Centrally located, our  venue this year is Kühlhaus Berlin – a standout industrial building from the 1800s that now houses art exhibitions and an array of unique events. Its spacious layout is ideal for our conference format, which takes place in three rooms over two days.  Format wise, we have our workshops on November 15th, with gifted presenters like Chris Armbruster, followed by the official conference on November 16th and 17th.  You can see the full schedule here. In Room 1, you’ll have speakers focused on the strategies and ideas behind “The Business of Tech.”  Room 2 will house “Tech Trends” looking at the latest technology. Throughout the day, we have evenly spaced out panels on Mobility, AI, IoT GovTech and more with coffee and networking breaks, talks from our esteemed speakers, and of course, our unconference. Lunch and dinner are also available on-site. Breaks between talks are also at the same time whether you’re in Room 1 or Room 2.  

4. Networking:

Rome was not built in a day, but a day can mean everything  if you’re building a network.  Be part of the largest meeting point for industry experts, entrepreneurs, tech and business professionals to inspire each other and disrupt the status quo. Get ready for outlandishly great access to founders, investors and creative thinkers with both the capital and the street cred to get a project off the ground and hear you out about your ideal plan for the future.

5. Startup Competition:

Taking place in our unconference room, This is your chance to make your name known and potentially gain access to some incredible opportunities. Six startups will be selected to make a five minute pitch, followed by a two-minute Q&A session with our judges. The winning startup will win a rare consultation with VC heavyweights  High Tech Gruenderfonds.  Having successfully launched over 470 high-tech companies and with leadership from people like Chiara Sommer, HTGF is uniquely positioned to incubate startups at their most critical stages.  Consultation and bootcamp for our startup competition winners also has a habit of going well. Last years winner was data tracking startup riskopy – now tracking over 100 million business in the US, UK and Europe and finding new opportunities in financial markets all over the globe.

Ready to learn more?  Get your Data Natives ticket here.

Anyone who has ever had their bag go AWOL while travelling knows that sinking feeling that comes with being told that your luggage is lost. Sadly, lost luggage is one of the perils of modern travel, and one that seems to happen on an all-too-often basis. Fortunately, just as technology is influencing the way we reach our destination, it’s also having an impact on our luggage, its transfer, tracking and safe arrival.

As the number of travellers across the globe has increased incrementally over the years, so too have the amount of suitcases and bags that need to move from point A to point B. While self-check in via website, app or kiosk has become something of a staple, checking in baggage and keeping track of it isn’t always as simple, but this looks set to change.

Sophisticated technology is altering the way our bags move when we do, and streamlining everything from the check in process to the collection. Both airlines and luggage companies have been involved in working towards smoother baggage control, and there are several developments and products that are going to make the future of travel so much better.

Electronic Luggage Tags

While carry on luggage lets you skip check-in queues at airports, checking bags in still takes time. Travellers need to print out tags, affix them to their bags and drop them off at the check in counter – all time consuming activities that make the whole process that much more labour intensive.

Lufthansa and German luggage manufacturer Rimowa are hoping to simplify the process and are currently rolling out a range of suitcases that come standard with an embedded E Ink display. This E Ink display is the same size as the standard paper luggage tag and it uses Bluetooth radio to collate data from supported airline apps, or Rimowa’s own app. The app then uses its luggage check in tool to synchronise with the electronic tag and the bag can be dropped at the counter, ready to be loaded onto the relevant flight.

This electronic tag system has been trialled since March 2017, and although Lufthansa is currently the only airline to support the service, and it’s only available at Munich, Hamburg and Frankfurt airports, it’s clear that the product has major potential.

Real Time Baggage Tracking

In the USA, Delta Airlines is deploying Radio Frequency Identification, or RFID technology that’s being used to track baggage in real time. This is a first for U.S carriers and marks a historic shift for Delta, as they handle 120 million bags on an annual basis. Up until now, these bags have been managed by barcode technology that was implemented in the 1990’s.

With RFID technology, scanners utilize radio waves to capture consistent, accurate data that is stored on a chip embedded in a luggage tag. This allows for complete transparency and makes tracking simple and straightforward. Travellers can see where their bags are at all times, ensure they have made it on to the plane and have been unloaded at the other side. The Fly Delta mobile app makes it possible to track baggage at all times and to ensure it reaches its destination safely.

This technology will be in use at 344 stations around the world, and has a 99.9% success rate for correct routing and loading. Travellers are given the ability to keep tabs on their bags at all times, and this has not only reduced some of the stress of travelling, it’s also drastically reduced the number of lost bags, or bags that haven’t made it to their correct final destination, as customers can easily pick up when their baggage has gone off course.

Smart Luggage

The Internet of Things is also having a major effect on travel, and is pairing smartphones with everything from our fridges to our lights. This makes our lives easier and more fluid, and it was only a matter of time before it crossed over into travel. Smart luggage is thus the next obvious step.

If carrying luggage around is too cumbersome, or you’d prefer a hands-free experience, then the Hop or Spacecase are going to make travelling so much easier. Both of these smart bags are designed to follow their owner unaided, and are equipped with Bluetooth technology and a camera sensor. Designed by NUA Robotics, these self- carry suitcases are designed to travel automatically alongside their owner when activated by a smartphone app.

These suitcases feature a proximity detector that allows them to travel at the same pace as their owner, and also have been equipped with an anti tamper security feature that sounds an alarm if separated. These prototypes are sure to spawn many other smart luggage options, and will allow for an easier, lighter travel experience. These bags can be taken on board as hand luggage, or checked in as cargo.

The Hop was designed in 2012, but it’s only now getting the attention it deserves as there is an increasing focus on changing the way we travel and making it simpler.

Microchip Tags

A number of companies have released luggage tags that contain imbedded microchips. These microchips are similar to those used to identify pets, and can be scanned by a barcode reader.  The ReboundTAG allows airline staff to scan and see what the bag owner’s itinerary is, and view their contact details in case the bag has been mislaid or not reached its intended destination. If an airport does not have a scanner they can enter the tag number on the ReboundTAG website and find the owner this way.

SuperSmartTag works in much the same way, and also ensures that bags can easily be tracked back to their owners, or sent on to the owner’s next destinations.

Magellan’s Retriever Tags are not as sophisticated, but they are just as clever. The tag contains a capsule with instructions written in 8 different languages that tell baggage handlers to check the itinerary inside your bag and send your luggage on to the next destination, rather than returning it to the initial point of departure, as is the case with so many other lost bags.

These microchip and capsule tags are a cost effective option and start at as little as a few dollars, proving that cutting edge technology doesn’t have to be prohibitively expensive.

All of these tech innovations are set to make travel that much easier, faster and convenient. It seems that by giving travellers greater control over their baggage, as well as greater insight into its movements, lost bags may well become a thing of the past, and transferring baggage from one point to another will be as easy as boarding a plane.

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As the collectors of actionable input information, networked smart devices with embedded sensors, software and electronics are a key driving force behind the Internet of Things (IoT).

However, they do not generate value for organizations on their own. Powerful, fast database technologies are required to create meaningful insight from the connected devices that are generating Big Data. When used correctly, the opportunities smart devices and the IoT present across sectors can be ground-breaking and deliver a real competitive advantage.

• Opportunities in every sector

The financial services sector is a well-known example of where Big Data is already having a significant impact. The move from people-driven to algorithmic trading is just one illustration of this. But, the technology that enabled this shift holds the potential to significantly benefit a myriad of industries. First movers across numerous sectors have already woken up to the potential, and it won’t be long before smart devices and the IoT become standard practice across every area of business.

A number of industry leading healthcare firms, for example, have already begun testing embedded devices to collect health metrics like electrocardiogram (ECG) movements and blood pressure readings, to deliver proactive healthcare. And in manufacturing, major companies are not just purchasing new equipment with IoT sensors pre-installed but are retrofitting them in existing factories in order to improve operational efficiency and productivity. Electricity grid operators and utilities too are looking to make use of smart devices for measuring and communicating electricity use, to improve billing, generation and reliability.

As these cases demonstrate, this new era of connected devices gives companies more detailed operational data which in turn can be used to gain actionable insights that were previously not possible.

• Database technologies for real-time insights

In practice, the rise of the use of smart sensors and the IoT across industries means that the amount of data to be processed will increase exponentially. In the Utility sector alone, we estimate that by 2025, companies could be receiving around 900 times more data than they do today. These massive volumes of data generated by IoT devices cannot be ingested by traditional solutions, and as such are beyond their scope to process.

This is where combined in-memory and on-disk database systems become key to the successful application of the IoT. Not only do these solutions have stream processing capabilities, allowing them to analyse very large quantities of data in-flight, they can also leverage historical data for real-time analytics. Some also offer support for time series, a feature which whilst rare, is very relevant for many IoT applications, such as preventative maintenance, where large quantities of data need to be collected at regular intervals for instant analysis. Combining IoT outputs with software that can analyze real time and historical data quickly can literally transform a business.

• Upgrade path for sensor analytics in the IoT

Whilst the potential for using database technologies to make sense of the data collected by smart devices is clear, it’s worth considering how industries can leverage the opportunities whilst protecting the investments they have already made in their systems’ infrastructure.

Installing an IoT-optimized database platform is one of the best ways to simplify the upgrade path to powerful sensor IoT analytics.

It’s important to bear in mind that any new technology will need to interface seamlessly with the existing platform to have minimal impact on operations. So, as well as the creation of a historical database, there should be careful planning around how the new technology can be used to bolster existing systems.  

Whilst introducing new real-time visualization and analytic tools, the requirements of business users in the organization also need to be kept front and center to ensure they are receiving maximum value from the investment. Looking for technology that is scalable, does not require a large investment in hardware, is relatively simple to maintain, performs extremely well and is highly available will pay dividends in the long term.

With more than 100 billion IoT connected devices projected to be installed by 2025, the speed of adoption of these and related technologies is increasing rapidly. What’s more, according to ABI Research, the addressable market for analytics and other value-added services for the industrial IoT is expected to reach $120 billion by 2018. Clearly, the time to start adopting and implementing sensor technologies is now.

On a sunny Friday morning, the IoT-EPI Challenge started bright and early at 9 AM (which is quite early, for Berlin standards) with an introductory talk for the participants and the media to get the rundown of the day.

This wasn’t a typical idea-hackathon. Collaboration was key. The goal was to work on challenges in the fields that the projects are already active in, with an emphasis on integrating their existing technology into the solutions, and exchanging ideas with the mentors (who were IoT-EPI members). Around 30 participants from 12 teams had a go at one of three challenges – ‘Trust’, ‘Mobility’, and ‘Retail’. The Trust challenge was run by INTERIoT and bIoTope; Mobility was run by symbIoTe; and Retail by BIGIoT, TagITSmart!, and AGILE.

The challenge wasn’t so much about developing technical frameworks, because the projects were building and providing those themselves. The purpose of the challenge was motivating teams to create feasible, applicable, business ideas that could turn into real-world solutions. How did the judges pick the winners? Teams could get a maximum of 9 points, divided into three categories:

1. Feasibility
2. Team dynamic
3. Potential for future collaboration with IoT EPI platforms

Trust

InterIoT and bIoTope were the mentors of the 6 teams that participated in this challenge. It was the most sought-after challenge, with the highest number of applicants. In it, the teams were confronted with an issue that might sound abstract at first. What does ‘trust’ mean for IoT? Trusting platforms? Trusting partners? Trusting technologies? Trusting data? Trusting people? In this challenge, participants could chose between two scenarios:

• ‘Port’, where 2 teams were tasked to find solutions for trust issues among different IoT platforms responsible for handling different day-to-day elements of port functioning e.g. port authority, cargo loaders, docks, etc;
• and ‘University’,  where 4 additional teams worked with the same task, but applied to different day-to-day elements of identity-based University benefits e.g. library access, food court access, etc.

Mobility

The Mobility challenge was unlike the Trust challenge, in that it gave participants some more freedom to experiment, since there were no defined scenarios within the smart mobility topic. SymbIoTe mentored three teams, two of which were already established startups – Innroute (Spain), and Inovatica (Poland).

Retail

The biggest challenge in terms of mentoring projects (BIGIoT, TagITSmart!, and AGILE), it was similar to the mobility challenge in its experimental freedom. It was also the one where the jury found the winner of the overall IoT-EPI Challenge. One team, lead by Rahul Tomar, focused on developing a solution to urban food waste, and the other, thingk-design, built an IoT solution for tool sharing.

To sum up the day’s work, each team had 3 minutes to pitch in front of an audience of other teams, mentors, project leaders, media, and IoT-EPI. The only rule – no self-promotion. This was all about the problems and their solutions.

“The challenge wasn’t only exhilarating because of the time limit. It was exciting because of the fact that we get to work with something new, and with great technology”.

• Florian Pichler, team Essentim

Since a lion’s share of participants have experience (or are active) in startups and pitching, the round was very entertaining, lighting fast, and had some truly excellent speakers. This, of course, did not make the jury’s decision any easier. The pitch round was done, and it was time for the judges to decide who won each category, and who was the best team overall.

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In the end, the proposals that made the smartest use of IoT to solve the task at hand, ended up being the winners.

The judges found that:

• thingk-design‘s ‘toolstation’ (Retail)
• Innroute (Mobility)
• and Digital Partners‘s ‘Legal Harbor'(Trust)

were the projects with the highest potential for ‘real-life’ success.

If you’ve been reading carefully, by now, you’ll know that thingk-design was the overall winner of the IoT-EPI Challenge 2017!  They are developing toolstation, a smart sharing system which provides professional grade tools for everyone, round the clock. It provides an ecosystem including manufacturers of tools and wearparts; a social network for DIY-instructions with crowd-sourced info material such as videos, manuals, etc; a cross-selling-platform for construction materials, as well as a smartphone app for end users. This team was the one that, on one hand, who could leverage the most synergies with existing IoT-EPI projects, and on the other, offered to seamlessly integrate BIGIoT‘s, TagITSmart!‘s, and AGILE‘s technology.

Open doors for future collaboration

The work of other teams did not go unnoticed. Several runners-up across categories were encouraged to further develop their projects and make use of the resources that are available through the various Open Calls throughout the year.

“We’ve had a great time in Berlin, gathering feedback for our projects, making new connections, and getting such amazing mentorship from the IoT-EPI projects. It’s really exciting to be challenged in this way”

• Adam Cavallari, team nooncare

IoT brings accessible solutions to day-to-day problems, bringing high-tech to the home, and to society in general. The efforts to make the IoT-connected world a reality are multilateral and therefore based on collaboration and trust, which is the basis to every long-lasting, fruitful, and productive relationship.

The latter is both what the IoT-EPI Challenge (and the IoT-EPI Week as a whole) is all about, and exactly what came out of it – successfully implementing a “from-lab-to-market” approach, that enabled collaboration amongst the IoT-EPI projects, as well as the future collaboration with ecosystem partners, such as entrepreneurs, developers, corporates, VCs and other multipliers in the field of IoT. The projects’ technologies paired with the teams’ curiosity and willingness to join forces to make viable solutions, were, in the end, the driving forces behind the event’s atmosphere of trust, collaboration, and boundless potential for open innovation.

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Collaboration and innovation go hand in hand, and are based in transcending frontiers – physical, technological, economical, you name it. Open innovation is one of the ways in which Europe can secure global competitiveness. The H2020 research and innovation program is the EU’s financial instrument to achieve this, and within this framework, the IoT-European Platforms Initiative (IoT-EPI) was formed to build a vibrant and sustainable IoT-ecosystem in Europe – one that is based on interconnectedness.

In March 2017, the IoT-EPI met for one week in Berlin, to attend business-building and community-building workshops, to share their projects with the Berlin tech and startup scenes, to host the IoT-EPI challenge 2017, and to strengthen the bonds of this growing network in its effort to unify a very fragmented European IoT platform ecosystem.

Berlin has been one of Europe’s most attractive destinations of the 2000’s. It has become synonymous with avant-garde art, electronic music, and a flourishing startup scene. What it is not synonymous with, however, is well-functioning infrastructure – at least when air travel is concerned (definitely hinting at the infamous BER-airport project here.) Because of an ill-timed airport workers strike in Berlin, a considerable part of the IoT-EPI members were not able to make it to the first meeting days. However, even though the group was smaller than planned, the first workshop days turned out to be very productive. The second part of the week (the Meet & Greet, and the Challenge) went on as planned.

IoT is about connecting

Media outlets, startups, SMEs, and corporates met at Ahoy! for this IoT-EPI Meet & Greet, to get to know the initiative and learn about its projects. Laura Kohler, etventure Startup Hub’s Managing Director, set off the evening with a talk that went to the core of the problem of the siloed IoT platform ecosystem in Europe, and that clearly showed the importance of IoT-EPI’s mission – IoT platforms help organizing an increasingly connected world, but they are not connected amongst themselves. Because there can’t be a one-size-fits-all solution for all the existing and upcoming platforms, collaboration is key within the IoT space, in order to create an ecosystem where platforms and services can talk, and thus connect the world.

“An IoT ecosystem based on interoperability and communication can be the driver of truly smart cities, and a connected world”

• Eneko Olivares, Universitat Politècnica de València, INTERIoT project coordinator

Startup pitch coach Bianca Praetorius took the stage from Laura, to give attendees a clearer picture of the stakeholders of the European IoT ecosystem, in the form of 10 archetypical figures in the space. From The fresh out of university founder to The EU funded R&D project, she highlighted the labyrinthine network of symbiotic relationships within this very interconnected space. Her talk led the way to the more lighthearted part of the evening.

Drawing inspiration from the long-running German dating show Herzblatt, IoT-EPI took on the task of matching innroute, a logistics startup from Barcelona, with a partner that could help reach the next stage of development as a company. The three candidates were an innovative corporate partner (Deutsche Bahn), a VC (Videesha Kunkulagunta), or a mentor (Srdjan Krco, project coordinator of the IoT-EPI project TagITSmart!). Depending on how the candidates answered the questions innoroute posed, one of them would be picked to work closely with the spanish startup. After the candidates answered questions about building and ending working relationships, market fluctuations, cooperation, and other key aspects of a business cleverly disguised as romantic issues, innroute saw themselves in a crossroads. How do you pick only one out of three promising candidates? Eventually, the startup saw a perfect match in Deutsche Bahn.

IoT is about sharing

IoT-EPI consists of seven research and innovation projects spanning the whole continent, designed to make their technology accessible to 3rd parties – Inter-IoT, BIG IoT, AGILE, symbIoTe, TagItSmart!, VICINITY and bIoTope. These projects draw upon the principles of open innovation and collaboration to create opportunities for platform development, interoperability and information sharing.

BIG IoT // big-iot.eu
This project is a seven-partner network with a team of around 40. Their aim is to create an API and a marketplace for IoT data and platform providers to serve as a one-stop-shop for monetizing, finding and acquiring IoT data & platforms for IoT projects. They focus on the smart mobility space, but decided to try something different in the IoT-EPI challenge, and went for the Retail challenge instead.

AGILE // agile-project.eu
Agile is building a gateway to control data sharing (in the cloud), that supports protocol interoperability, device and data management, design and execution of IoT apps and external cloud communication. In a nutshell – building an architecture to support communication between IoT devices

INTER IoT // inter-iot-project.eu
InterIoT is a partner network of 14 universities and big TelCos. Their goal is to build an open framework to integrate new and existing IoT platforms, enabling voluntary interoperability among IoT platforms and across multiple layers – from devices, through middleware to a semantic layer. In layman’s terms – On one hand, they’re building bridges between IoT platforms, so they can communicate; on the other, they’re also building the tools for others to be able to build these bridges between themselves.

bIoTope // biotope-project.eu
bIoTope is leveraging the know-how of their 20 partners, to lay the foundation for open innovation systems to enable horizontal interoperability across IoT systems. As they described it during the meet and greet, basically, they’re offering “everything-as-a-service”.

symbIoTe // symbiote-h2020.eu
symbIoTe wants to help solve the fragmentation problem of the EU IoT landscape by building orchestration middleware for transparent interoperability of IoT platforms and sensing/actuating resources.

TagITSmart! // tagitsmart.eu
TagIT Smart create smart tags for retail products, to enable ecosystems of connected products that are otherwise still out of reach due to technological limitations.

VICINITY // iot-epi.eu/project/vicinity
The VICINITY project will build and demonstrate a platform and ecosystem that provides “interoperability as a service” for infrastructures in the Internet of Things. Unfortunately, the project was not able to be present at the IoT-EPI Week because of the airport strike.

The projects welcomed visitors from large, innovative institutions like BMW, EnBV, and Siemens, to discuss their work, their goals, and to talk about finding ways to build the strong ecosystems and communities that an interconnected European future needs. For BIG IoT, interconnectedness takes on a very physical meaning. With an emphasis on smart mobility, the BIG IoT project is conducting 2 open calls to enable external organizations to join the project with their own ideas and contributions, within a larger IoT-EPI program of 11 open calls, offering funding totalling €5.5 million.

What’s there to win? Aside from getting to work within the BigIoT ecosystem, they are offering a total of 750.000 € for new partners, taking on the role of either IoT service, application, or platform providers.

“There real value-add for businesses and other organizations in the versatility of IoT data”

• Jelena Mitic, Siemens, BIGIoT project coordinator
  

The first next open call, by BIG IoT, starts in April 2017. It will focus on IoT platform & service providers who are willing to provide new data to the project through the BIG IoT API and offer the data on the BIG IoT Marketplace. The second open call will be published in January 2018, targeting also IoT applications providers. Head to BIG IoT’s website to fill in the application form, and to get more information on the project and specific details about the open calls.

IoT is about challenges

The lively networking session was far from the end for this IoT-EPI week. As the evening drew to a close, everybody’s eyes were on the following day and its prize. On the next blog of the series, we’ll share our impressions of Friday’s IoT-EPI Challenge 2017, where 12 teams from all over Europe got together to find the IoT solutions of the future.

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2017 is set to be a success for the IoT industry, as the number of connected things grows at soaring speeds. The time has come for businesses, consultancies, and entrepreneurs to tap into this opportunity, if they want to stay in the vanguard.

Of the projected 8.4 billion IoT-enabled devices in the world in 2017, the consumer segment represents a staggering 65%. Yet, we’re seeing industrial IoT get all the shine, while the consumer side gets pushed to the side lines. In reality, the space for consumer-facing IoT applications is flourishing and offers unrivaled opportunities for innovation.

Why is it so important for startup entrepreneurs to lead the way? Simply, because, unlike large corporations, startups are agile, nimble, and have the freedom to experiment with new methods to explore new ideas. However, founders have to know that they will embark on a journey that is all but smooth sailing.

A Smooth Sea Never Made A Skillful Sailor

Creating software and internet-enabled product experiences takes plenty of effort and unwavering resilience. IoT startups have to cover all the bases from day 1 – ideating, prototyping, beta testing, production, and engaging with wholesale and retailers, all while having to be savvy of fundamentals such as intellectual property laws, and business structures. And then, there’s the money.

Some say that the road to success is paved with mistakes well handled, but all that paving has to be paid for somehow. Even though getting investment does not assure success, it is definitely a crucial way to get there. Alexandra Deschamps-Sonsino is a world-renowned IoT industry expert, prominent public speaker, and tireless entrepreneur who has toiled away for the past 12 years, running a design studio, a consultancy, and building an IoT product, all while running the world’s second largest IoT meetup, and getting her work exhibited at New York’s MoMA, the Victoria and Albert Museum, and the London Design Museum. If anyone knows what it takes to make it as an IoT entrepreneur, well, it’s her.

The Internet of Things Entrepreneur Checklist

Drawing from her years of experience running her own ventures and advising tens of European startups, Alexandra compiled the ultimate guide for the budding IoT entrepreneur. This to-do list will get you set up towards reaching your goal of getting investment for your idea, which probably represents the first 6 months to a year of your business. Ready to take on the challenge?

Get your copy of The Ultimate IoT Entrepreneur Checklist

Psst – We’re hosting a hands-on workshop with Alexandra, delving into the world of commercial IoT. Discover your company’s unlimited potential to innovate, and facilitate the journey of digitalization for you and your clients. The workshop is an intensive course where she will provide guidance and share everything she knows about how to build a business that offers software and internet-enabled product experiences – from the ground up.

What? 3-day intensive IoT workshop

When? February 24-26, 2017

Where? SAP Data Space Berlin

JOIN US

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Image: Defence Images, CC 2.0

 Convince Your Boss! 5 Reasons to Attend the IoT Weekend

You really want to come to our IoT workshop but you are not sure how to convince your boss to pay your ticket? Say no more. We’ve prepared some pretty good reasons for you (not that you do not know them already) to pass on to your higher-ups.

1. Learn from an expert

Alexandra Deschamps-Sonsino is an entrepreneur and a designer with over 12 years’ hands-on experience in the IoT space. She knows how much money you need to invest, how much time you have to spend, and who you want to work with to get your project going. She is not yet another consultant who will throw you buzzwords and provide you with high level content. She gets things done. The workshop is an intensive course where she will provide guidance and share everything she knows about how to build a business that offers software and internet-enabled product experiences from the ground up.

2. Full focus on commercial IoT

Of the projected 8.4 billion IoT-enabled devices in the world in 2017, the consumer segment represents a staggering 65%. Yet, we’re seeing industrial IoT get all the shine, while the consumer side gets pushed to the side lines. In reality, the space for consumer-facing IoT applications is flourishing and offers unrivaled opportunities for innovation. We want to attract the edgiest product visionaries out there by bringing our focus on commercial IoT to Berlin, Europe’s most exciting tech hub.

3. Do it for the network(ing)

We want you to be able to focus on what’s important. We’ve limited capacity to 30 attendees, so there’s ample time to ask questions, discuss plans and exchange ideas with Alexandra and with fellow participants. The list of attendees participants includes the who’s who of the IoT scene including senior employees from Ernst&Young, R&D engineers, and Managing Directors of established retailers moving to make their mark in the digital world.

The workshop was conceived with an excitingly mixed audience in mind. The model attendee is a professional who promotes innovative product development and marketing, within a company or independently as a consultant or an entrepreneur.

Ideally geared towards Intrapreneurs, the course is also designed to provide Consultants, Product Managers, Developers, and Founders, with the tools they need to manufacture products and fully understand the commercial IoT space.

4. Exclusive Content

The workshop covers these four key areas:

• Planning and Prototyping
• Beta testing and Production
• Engaging with wholesale and retailers
• Intellectual property, business structures & investment

Want to go for the nitpicky details? Then check out the Schedule; Alexandra’s post about how what lies at the heart of building an IoT product; or her talk at Data Natives 2016

5. The SAP Data Space

DATA SPACE, SAP’s flagship space in Berlin, is the place to meet innovators, change makers, partners, startups, artists, Berlin’s multifaceted community, and all people interested in Digital Transformation.

Go ahead and send this article to your boss. You’ll thank us later. Discover your company’s unlimited potential to innovate, and facilitate the journey of digitalization for you and your clients.

What? 3-day intensive IoT workshop

When? February 24-26, 2017

Where? SAP Data Space, Berlin

JOIN US

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Images: Ars Electronica, CC2.0

Dataconomy has joined forces with Alexandra Deschamps-Sonsino, one of the world’s leading IoT entrepreneurs and influencers, to run an exclusive, 3-day IoT workshop in Berlin, February 24-26, 2017. Alexandra will host an intensive course where she will provide guidance and share everything she knows about how to build a business that offers software and internet-enabled product experiences.

Book your spot here.

A few years ago, I gave a talk at a Lunch and Learn event organised by Shoreditch Works and I wanted to revisit it in light of the upcoming IoT weekend workshop.

Building an IoT product isn’t like building software nor is it like building a product. It’s also different in terms of fundraising. Here’s how it breaks down:

An IoT product isn’t like software

The biggest difference with software is that you actually have to physically ‘ship it’. And it makes no difference if it’s a friday 🙂 The other difference is that software is easily updatable and you can decide how many users you wish to consider valid users to your potential investors. With #iot, you’re talking about sales numbers (there’s no such thing as an ‘active’ customer in that sense), use is secondary. That’s why wearable companies sell you stuff they know you will stop using witting 3 months and don’t remind you to use it or build return-reuse systems. They shipped you a thing once and that’s it. They’ll collect data for as long as a customer actively uses the device but he/she will always be a sale, a customer. To deploy entirely new features, you probably have to try to convince them to buy the product again. The changes are sometimes so important, a simple firmware update won’t do it. An #iot product simply doesn’t behave like software.

An IoT product isn’t like a product

When you shop for a sofa you don’t have to worry about setting it up, connecting it to your wifi service or seeing if you have connectivity in the living room. The unboxing experience of a connected product is much longer and that can affect the use and acceptance of the product. The reliability of the connection will also affect how someone feels about the product. There might be batteries to replace too, or to recharge, or a plug adapter to buy. It’s much more faff than a regular product. So it’s important to understand that user experience and making sure it’s not so painful as to become a barrier or even a deterrent. You have to remember that if you get a single complaint, chances are 100 other people may have had a bad experience.

So what do these two differences have to do with raising money? Well, plenty. When I first looked for funding in 2013 I noticed some patterns in my (sometimes) short meetings with investors:

• They didn’t have experience shipping physical products but had experience shipping software
• They were intrigued with #iot but hadn’t invested yet
• They thought about products in traditional terms (you have to have minimal protection for your product through a patent or other forms of IP) not in tech terms.
• They rarely take the jump unless you’re already selling and growing.

So how do you prepare for these types of conversations? Well you’re going to have to educate your potential investors. Read Venture Funds to familiarise yourself with the types of mechanisms they’re used to, and be prepared to share your business processes with them. The IoT-ness of your product may mean that you have to initially invest heavily in customer support and repairs/returns as you find your feet with your supply chain. You have to explain this to them. If your investment talks turn into master classes, moving on to join an incubator instead is not a bad option. They often will help you with money and for a 3 month stint, which is not bad at all and worth the 6-7% equity. Check out Startupbootcamp, RGA, CLR, Bolt, Usine.io, HAX, Highway1, and many others. There’s also EU grants, government loans and tax schemes for investors, which small businesses can take advantage of. These take more time to apply for but it’s money with no strings attached

Whatever your product, it’s important to make sure you can talk to an investor about it and highlight the major differences to their experience if they’re new to IoT. If you’re clear about what makes your product better and you can prove you’ve thought about all the ways in which you can mitigate the issues I’ve described, you’re on your way already.

A 3-day, IoT workshop to cover all the bases

I don’t think that my struggles are universal. I want this to be easier for everyone. With Dataconomy, I’ll be running an intensive 3-day IoT workshop, condensing everything I know about how to build a business that offers software and internet-enabled product experiences.

Whether you’re a budding entrepreneur with an idea, a product designer looking to grow their toolkit or business owner looking to expand into software-enabled products, there should be something in it for you.

I’m covering everything from planning a product from day 1 and prototyping, to beta testing and learning then how to make  bigger production batches and engaging with wholesales and retailers. You’ll learn about IP and business structures as well as investment in this space. You’ll come out of this with a realistic understanding of how much money you need to invest from day 1 and how much time you can expect to spend and who you might want to work with. I am opening up my blackbook and sharing the learnings of the last 12 years of my career with participants online and offline.

This is ideal for you if you already have a product idea you’re looking to develop or if your business is looking to spin off a product idea. If you don’t have an existing idea, this will feel very abstract. You can be a single founder, a team, in a business or a student. As long as you have a product idea already in mind, you’ll get plenty of this overview. I’m confident that these 3 days will save you 2 years of work and will help you manage your expectations moving ahead with your idea. You’ll meet other entrepreneurs, which is essential to a journey which at times can feel really isolating. Community is everything in an entrepreneurial journey.

If you want to read more aboutthe workshop, the schedule, and myself, check out the first post of this series announcing the 3-day IoT weekend workshop.

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Image: Good Night Lamp

IoT data offers answers to a simple question: “Are things changing or staying the same?”

There are new data streams generated each day, that make it possible to quantify the formerly unquantifiable. The Internet of Things (IoT) enables us to measure processes and react more quickly to ever-evolving conditions, not only in industrial settings, but also in the spaces closest to us – a home’s energy consumption, a patient’s vital signs, real time social interactions among machines, companies and people, just to name a few.

Some experts’ forecasts estimate that there will be upwards of 30 billion connected IoT devices by 2020. Smart cities, connected cars, logistics and industrial equipment will all be changed by IoT. With this new information come new opportunities for optimization and prediction. However, some industries are discovering the challenging differences between IoT data and other streams of analytics data.

This e-book shows you how to find useful patterns in IoT data and how to use them to predict and model successfully.

Many companies already collect plenty of data, mostly for creating reports and dashboards. Some of the approaches and workflows for working with these high dimensional, high volume streaming data are easily adapted from existing applications and other domains, but not all. Before using the data for optimizing business processes, analysts must put a few things in place.

Setting up a ‘Data Historian’
A Data Historian is the place where data are indexed and made available for analytic software and routines – be it a standard database or data warehouse

Finding out what you need to predict
In order to find interesting patterns for their models, analysts can rely on the analytic strategies that are used for other data sets, such as:

• Aggregating and aligning data
• Determining aggregation intervals
• Forecasting and predicting in the time domain
• Modeling trajectories and multivariate anomaly detection
• Finding inflection points

Now that the proper mechanisms are set in place to get exactly the right data you need, Statistica presents the tool that brings the math to the data.

Click here to download the e-book!

Native Distributed Analytics – Taking the math to the data, wherever they are

Given that the worldwide amount of connected devices is rising at an increasing rate, the ability to collect data will quickly outpace a company’s ability to analyze it. This could mean overburdened networks, unclear objectives for data analysis, and ultimately, higher efforts needed to really boost a company’s bottom line. The goal is to detect “something interesting” – state changes, anomalies, trends and inflection points – and use that information for the analytics process.

A Native Distributed Analytics Architecture (NDAA) runs analytics on aggregates computed in a database, gateway or device/sensor, wherever it may reside. This is what the workflow looks like:

1. Users build an analytical model, such as a neural network.
2. They export the model in the languages (Java, C, C++, PMML, SQL) appropriate to the platforms (Hadoop, Teradata, Netezza, Exadata) on which the data reside.
3. The models execute platform-specific operations and user-defined functions.
4. The results return to desktop software, where visualisation technology displays them to analysts and users..

How to effectively measure with IoT data

IoT technologies hold the promise of delivering new data, information and insights in industrial applications like patient monitoring and automated manufacturing. The next hurdle, as described in this e-book, is to tailor traditional analytics techniques to IoT data. Analysts can then derive useful and actionable information from such data and figure out how to make IoT work for both industry and consumers.

Who should read the ‘Analytic Strategies for the Internet of Things’ e-book?

As analytical tools become intuitive and easy to use, gone are the days when only statisticians and those with PhDs in mathematics could unlock the power in data to innovate. In today’s world, with easily available and accessible data, big or small – fast or at rest, analytics is becoming democratized.

Read this e-Book if:

• you are interested in newforms of data, but are just starting out, or
• are already data native working on complex data modelling    

Download your free copy of ‘Analytic Strategies for the Internet of Things’ here!

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Dataconomy has joined forces with Alexandra Deschamps-Sonsino, one of the world’s leading IoT entrepreneurs and influencers, to run an exclusive, 3-day IoT workshop in Berlin, February 24-26, 2017. Alexandra will host an intensive course where she will provide guidance and share everything she knows about how to build a business that offers software and internet-enabled product experiences.

Book your spot here.

I’ve been building the Good Night Lamp for many years now. For 12 years in fact. Something I’m not particularly proud of but sometimes it just takes a lot of time to do simple things because the conditions around you change. In 2005, when I came up with the Good Night Lamp as a student in a design Master’s degree, I never thought of what exactly it would take to make it into a commercial product.

The idea seemed simple: a Big Lamp that when turned on (when you push down the chimney) triggers Little Lamps around the world to turn on, too. Many people use it to indicate to a loved one that they’re available, that they’re thinking of them, or to call now because the kids want a story before bedtime. It’s a simple way of sharing presence and availability.

In order to make this a reality there was, in retrospect, no single course where I could get an overview of the whole process. I simply had to bumble along.I had to try things, fail, get back up again, and try again. I spent a lot of my own money, and my investors’, in this process.

That’s why it’s taken 12 years to get the Good Night Lamp in the newly opened Design Museum in London and selling online to UK & European customers with US sales in development for 2017.

A 3-day, IoT workshop to cover all the bases

I don’t think that my struggles are universal. I want this to be easier for everyone. With Dataconomy, I’ll be running an intensive 3-day IoT workshop, condensing everything I know about how to build a business that offers software and internet-enabled product experiences.

Whether you’re a budding entrepreneur with an idea, a product designer looking to grow their toolkit or business owner looking to expand into software-enabled products, there should be something in it for you.

I’m covering everything from planning a product from day 1 and prototyping, to beta testing and learning then how to make  bigger production batches and engaging with wholesales and retailers. You’ll learn about IP and business structures as well as investment in this space. You’ll come out of this with a realistic understanding of how much money you need to invest from day 1 and how much time you can expect to spend and who you might want to work with. I am opening up my blackbook and sharing the learnings of the last 12 years of my career with participants online and offline.

This is ideal for you if you already have a product idea you’re looking to develop or if your business is looking to spin off a product idea. If you don’t have an existing idea, this will feel very abstract. You can be a single founder, a team, in a business or a student. As long as you have a product idea already in mind, you’ll get plenty of this overview. I’m confident that these 3 days will save you 2 years of work and will help you manage your expectations moving ahead with your idea. You’ll meet other entrepreneurs, which is essential to a journey which at times can feel really isolating. Community is everything in an entrepreneurial journey.

I hope you’ll join us and book your ticket now!

The IoT Workshop schedule –  February 24-26, 2017.

What? 3-day intensive IoT workshop

When? February 24-26, 2017

Where? SAP Data Space, Berlin

JOIN US

The IoT Workshop lead – Alexandra Deschamps-Sonsino

Alexandra Deschamps-Sonsino is an interaction designer, product designer, entrepreneur based in London. Watch her talk at Data Natives 2016.

Arguably one of the leading voices in the IoT space, she was named 2nd in a list of 100 Internet of Things Influencers (Postscapes, 2016), 2nd in Top 100 Internet of Things Thought Leaders (Onalytica, 2014) and in the Top 100 Influential Tech Women on Twitter (Business Insider, 2014).

She is the founder of the Good Night Lamp, connected lamps for your global friends and family.  Alexandra is also the Director of designswarm, a strategic consultancy focusing on the internet of things. Some of her clients include BBC R&D, P&G, British Gas, EDF R&D and Nominet, British Telecom and others.

She was co-founder and CEO of Tinker London, a smart product design studio. Tinker was the first distributor of the Arduino platform in the UK. In addition, she has been running the London Internet of Things meetup since 2011, the second largest meetup on that topic in the world.

Her work has been exhibited at the Museum of Modern Art in New York, the Victoria & Albert Museum, the London Design Museum and galleries around the world.

Presently, she is part of the Mozilla Leadership Network Advisory Group on the board of the Virt-EU project and an advisor to many startups.

She is @iotwatch on Twitter.

What? 3-day intensive IoT workshop

When? February 24-26, 2017

Where? SAP Data Space, Berlin

JOIN US

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Titled ‘Data Science for Banking & Insurance’, Dataiku’s free eBook includes recommendations, use cases, testimonials, and how-to checklists that enable you to make your mark in this new era of Fintech and Insurtech. Whether you are working in marketing, risk management, product design, finance, actuarial science, underwriting, or claim management, this ebook illustrates how banking and insurance can seize the analytics opportunity. Get your free copy here.

In the internet era, giants of the digital age like Google, Apple, Facebook, and Amazon (GAFA) in Western markets and Chinese powerhouses like Baidu, Alibaba, Tencent, and Xiaomi (BATX) in Eastern markets have been increasingly straying away from their bread-and-butter products and testing the waters in large, established industries like banking. GAFA and BATX are beginning to offer services like online and mobile payments, money transfers, personal lending, account and savings management, peer-to-peer lending (crowdfunding), and currency trading.

And it’s not only the tech giants that are moving in. Countless startups in financial services have also been flooding the space and gobbling up market share, cherry picking high-volume services tailor-made for the online and mobile world into which they were born. Though large banks recovered from the global financial crisis of the early 2000s to continue to serve customers, they quickly started to lose ground as those customers turned to faster, more cutting-edge solutions to meet their financial needs.

The Rise of InsurTech

Unlike the banking industry, GAFA and BATX have not made direct forays into insurance, though the number of Iinsurtech startups in this space is on the rise. The market is ripe, as younger generations are used to ease of mobile apps and one-click shopping, and they want the same with insurance; they are not interested in the heavy process and expense associated with traditional insurance.

As in banking, peer-to-peer is hot in insurance with older players like Friendsurance and also newcomers such as Lemonade, InsPeer, InSured, and Teambrella. Each promises insurance that is more transparent and social with shared costs – things that have wide appeal in today’s market where customization is king.

Another interesting area in insurtech is item-specific, event-specific, and on-demand coverage – “smart insurance.” Startups in this space collect data about a customer’s possessions and provide machine-learning enhanced risk pricing for single-item coverage of any duration. This model allows premium levels to scale down to pennies with durations down to the second for completely customized coverage.

Insurtech and the Internet of Things

Aside from insurtech startups, the Internet of Things (IoT) is also poised to change the insurance industry in the coming months. Though IoT has been around since the 1970s, it has only recently started to infiltrate all aspects of consumers’ lives. Billions of sensors, computer processors, and communication devices are being embedded in or attached to every kind of ordinary thing imaginable, from watches to agriculture crops to cars.

And we’ve just begun to scratch the surface; Gartner estimates that by 2020, there will be more than 21 billion connected devices. Considering there were only around 3.5 billion smartphones in the world in 2015, this is astronomical growth.

Currently, the manufacturing, healthcare, retail, and security industries lead in the IoT sector, but insurance companies are well positioned to take advantage of this space. Given the upcoming ubiquity of smart homes and cars (like Nest and any number of the developing self-driving cars), a new generation of products based on real-time monitoring, collection, and analysis of data coming out of these products is on the horizon.

Ride the IoT Wave

To stay relevant in the age of IoT, some insurance companies are partnering with insurtech startups, particularly for devices in the smart home business. While GAFA is investing heavily in IoT, thus far, they have largely decided not entered the insurance market directly, leaving this space for traditional insurers to step in (for now).

And the most savvy insurance companies are beginning to step up and realize the potential of IoT. For example, insurance companies have partnered directly with device manufacturers like Water Hero, which monitors and displays water flow in real time and offers a robust alert system and remote shut-off capabilities. It’s easy to see the appeal in this partnership given that roughly one-third of all household claims are related to water leaks.

The most popular items in smart homes right now deal primarily with security and access (from light control switches and dimmers to remote security and smart doorbells), with obvious appeal to insurers. But other startups like Water Hero are creating more specific IoT devices that will certainly help prevent costly claims, particularly smart smoke and carbon monoxide detectors and mold detection. All of these devices open up the door for insurance in the time of IoT.

Staying Relevant

Of course, even when insurance companies partner with IoT manufacturers, the question still remains: who owns the customer relationship? For complete control of the customer experience and customer proximity, it’s essential that today’s insurance companies embrace the age of algorithms and better leverage IoT technology and big data to drive innovation.

Insurance can’t continue to simply partner with IoT manufacturers for long – they have to lead the movement. This means appropriating the very tools giving their new competitors an advantage in both IoT and non-IoT spheres: big data and algorithms. By leveraging IoT technology to gather more data about customers’ homes, cars, and even the people themselves, insurance companies can then better use real-time data, predictive modeling, and machine learning to create new business models and new offerings for clients.

For example, by becoming more connected to customers’ data, not only will business improve vis-a-vis claims reduction (think of the Water Hero use case and potential disasters averted with IoT), but it’s also easy to see how overall customer experience will also be significantly improved.

Customers, of course, will similarly benefit from avoiding the hassle of a claim and repairs, but on top of that, IoT can create a better experience in case there is an accident. Think of a smart car that is involved in a crash – with real-time data, the process of knowing exactly what happened and who was responsible for the crash becomes infinitely easier, more concrete, and more transparent..

Additionally, innovative developments in insurance will expand providers’ value proposition with customers. Using data and predictive data science will give more flexibility to offer customers only services and coverage that they will use. Clearly, people are looking for this offering given the number of startups in this space. But with IoT, traditional insurance companies can also compete in the space.

Algorithms are the way forward

Current startups in the space are proving that the age of algorithms is a positive development for the insurance business itself and for its customers, who are looking for more options, flexibility, and transparency, all of which IoT and big data analysis can offer.

IoT is moving forward at an astounding pace, and developments in IoT deeply entwined with and affecting insurance will continue whether insurance companies choose to get involved or not. So leveraging and investing in big data in insurance seems like an obvious win – what are you waiting for?

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Image: makototakeuchi, CC 2.0

Kevin Mahaffey is an entrepreneur, investor and engineer with a background in cybersecurity, mobile and machine intelligence. He is CTO and Founder of Lookout, a cybersecurity company dedicated making the world more secure and trustworthy as it becomes more connected, starting with smartphones and tablets. He started building software when he was 8 years old and it has been a love affair ever since. Mahaffey is a frequent speaker on security, privacy, mobile and other topics.

Tell us a little bit about yourself and about Lookout

I am Kevin Mahaffey, I’m the founder and CTO of Lookout. We are a cyber security company focused on mobile.

I like fixing problems. The company started in 2007 and actually myself and the other two co-founders were doing research into mobile phone security and we got our hand on a Nokia 6310i, you know, black and white screen, had snake the game on it, and this phone was notable to us because it had Bluetooth on it. We found actually some pretty bad security vulnerabilities on that device. You could hack into it and reboot it. And we looked on a whole bunch of other devices and we found similar vulnerabilities in almost every phone we ever touched. And we tried to work with all the different manufacturers, everyone from Blackberry to LG to Nokia in this case and nobody really took security very seriously because the question was why would anyone wanna hack a phone? This is in 2004 mind you. And one of the other excuses was, well the range of Bluetooth is only 10 meter so you had to be really close to someone.

And so Bluesniper was created to extend the range of Bluetooth to 1.2 miles away. And in doing so we proved that you could actually hack a phone from really far away. We thought that this is maybe something we’d talk about at some technical security conference but we were surprised to be on the front page of the business section of the Wall street journal of the NY Times, so we thought “this is a big problem that we can solve”. So we said ok lets start a company to solve the problem and in 2007 we started Lookout to build software to protect both individuals and businesses from cyber threats on their mobile phone.

What makes you want to hack things?

Hacking is not like we see in the movies. The way a system does work is different than the way it was designed to work. And they surface that. Good hackers, people who want to make things better, when they find a way to manipulate a system in a way that wasn’t intended they try to get it fixed.

Where is the company’s HQ located and why?

We are based in California, San Francisco, and we have offices in Europe and Asia.  The reason we are all over the world is because this is a global problem. Mobile security doesn’t affect only one country but every person on the planet. From individuals who’re using their phones for online purchases to large companies who’re using mobiles to run their businesses to manufacturers.

We started in LA and in 2009 we moved to San Francisco because Google and Apple became big in mobile.

Are you going to stick to phones? Or you have plans for other devices, such as cars?

We don’t have any products in that space, [car security etc], I’m not sure if we will ever have a product for cars but the passion of everyone in the company is [understanding] how to make the world a safer place and sometimes that means releasing a product, sometimes it means doing and publishing research. And if there is a product that is needed, we go build it. IoT security needs to be taken very seriously. However, we are focused on mobile right now. We’re focused on one problem at the time.

Can you also hack an offline network?

Most people are focused on how to secure a network. How to stop bad things from happening. But if you think of your body, your immune system doesn’t work that way. And most networks are architected to assume you can block those things. But nowadays you can’t control what’s in your network anymore. So a lot of companies are getting breached everyday, and usually by someone inside their network, they use some valid credentials to access the data that they shouldn’t, and that’s a really big problem. So we advocate for this concept of the immune system where you gather data, preferably no personal identifiable data to know how things are working, everything from your smartphones to laptops, then you process that data and analyse it for find indicators of a threat and sometimes you can automatically respond, or sometimes you need to escalate to some smart human in a security team to think about it some more and decide what to do. But this is very different than stumbling upon a hack because they take out your internet connection for taking so much data from the company, sometimes that’s how you discover a hack.

What kind of advancements do you see happening in the future for your company and in the world?

So right now a lot of individuals use Lookout. The big course for us right now is helping large companies and governments secure their mobile devices because 3-4 years ago people could get email (if that) on their phones. Basically everything you can do on your PC most organisations started to be able to access from a smartphone or tablet. But the organisations don’t have any idea what’s going on on these devices right now. So we see a lot of demand on that. How to secure these devices. We look at a modern way to stop advanced threats that it’s not just signature based to stop attacks on mobile.

And do you see this happening in general?

Everyone is moving towards data security. Some companies are building their own software and they’re very far down on that road, other companies are just starting to get there. But it’s not the device, it’s the data. You can’t stop the device of getting hacked, you have to defend your data and you have to respond to threats and hope they never happen. Those two principles are really coming forward. Unfortunately it means a lot of organisations have to rip out some things and replace some things but I think it’ll make companies and people more secure because when companies are more secure, as an individual your data will be breached less often.

What are some key hurdles in the industry that you’re experiencing and how do you see data science applications solving this problem?

The hurdle is there’s too much data in security or not enough data. In the case of not enough data, many security organisations apply. You can ask any given system, what is the data that will show the hacker gets in. And if you don’t have data coming from that system, then you never gonna know that a the hacker gets in. Other times you have so much data that it is not very useful and you don’t know what to do with it. So you have to set the security teams that are drowning alerts. They’re so busy that they can’t focus on the really important threats. And what I have to see is machine learning emerging to actually helping with these issues.

First there are organisations stitching together the data. so instead of a bunch of isolated data streams we use the phrase joined-in and analyse it. Joined-in is where you take your source code data and mash it with your vacation data so if an engineer checks in for threat indications that’s actually something you wanna look at. But if you only look at source data you’ll never be able to make that conclusion. And analyse it means to look deeper and extract more information. And then, using machine learning to take that huge volume of information and funnel it down to a simple message which says, okay, here are the things that humans need to look at and here are the things that humans don’t need to look at and we know how to deal with it. We can automate responses, cut the device from the network etc. Ultimately humans can only make so many decisions per hour and we have more and more connected things in the world and so if we try to add those things and do the security the same way we did in the past, we’re gonna lose.

What are the possibilities and benefits of using data science in cyber security?

[Using data science] I think security teams will get more sleep, companies will be more secure, hacked less frequently, and individuals will see their data be more protected.

When did you notice that things started to take off?

When we started the company we were securing windows mobile smartphones. And projections for how many smartphone there will be in 2017 were very few. So when we went to investors they were like ‘oh yeah the smartphone market is not very big one’. And now there’s billion smartphones shipped every year and what changed was iPhone and Android launched and that made smartphones easy and fun to use and then at the same time you had 3G and now 4G networks and made the data connection very fast. And the growth of Android and iPhone helped business to grow because it turns out everyone is using smartphones personally. And more recently they started to use them more for work and we’re using things for data, for shopping and for sensitive business data that attract hackers.

So if you could tackle any technology exists today to solve a challenge which would it be?

I think there’s still a lot of misinformation around machine learning and big data systems, I think a lot of people believe that you can just apply machine learning to data and magic happens and problem solved. It’s not true. Machine learning is something that can be a good classifier can detect anomalies in some cases it’s not just machine learning it’s what we call a cyborg. It’s machines doing one thing and humans doing another and find the right handoffs approach so that they can operate together.

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Image: James Case, CC BY 2.0

Anatoly Lebedev is the CEO and Co-Founder of Irish company Cesanta. Together with his team, he helps define the future of embedded communication technologies. He believes that if we want to get to 20 billion connected devices by 2020, then IoT integration needs to be made simple, secure and fast. Anatoly ensures that Cesanta products match this vision. Cesanta has been named as the ‘One to Watch’ by Business & Finance Magazine and is the 2015 winner of the Web Summit’s ‘GoGlobal’ competition. Previously, Anatoly shaped strategic partnerships in Europe, Middle East and Africa in his 8-year tenure at Google. He is heavily involved in the Irish startup scene and can be found as a mentor at hackathons and startup weekends. When he’s not driving the business side of Cesanta forward, you can find the racing enthusiast driving through the Irish countryside.

A little bit about you and your company

My name is Anatoly Lebedev and I’m the CEO of Cesanta, an Irish technology startup working in the Internet of Things field. We are on a mission to bring all devices online. This means we apply connectivity and networking solutions by bringing devices and equipment to the Internet.

I spent 8 years at Google before founding this company.  I started in the tech field and then I moved to the business side and then to strategic partnerships. I was doing multimedia, hardware distribution, data acquisition and so on. And when we decided to start Cesanta, a bunch of other engineers joined us. 70% of the company is comprised of ex-Google employees. The mission itself is quite challenging. How do you bring all those devices online? How do you actually program them physically? That’s why we created Mongoose IoT Platform and made it easier for people with limited skills to actually prototype and build these connected things.

What was the reason for starting this company?

We have a different product called Mongoose Web Server Library. And, this product is widely used by companies like Intel, HP, Dell, Samsung, even NASA; Mongoose is now on the international space station. What we noticed was that Mongoose had been used as part of systems that companies built in-house to achieve IoT connectivity. While those guys I mentioned might have big pockets and a lot of resources to do that, the majority of companies developing IoT-enabled products are smaller and don’t have those budgets.  These smaller companies often stumble upon problems that will create insecurity and unstable products. So what we said was why don’t we just provide them with a platform to create simple, secure IoT connectivity? We know how to build it, and that’s how we decided to build the platform.

More and more companies are going to bring their products online, and all of them will be struggling with infrastructure. But, for most of them their core business is the device and what it does for the consumer; not the connectivity piece. That’s where we enable them. We’ve taken away a big, very specific problem in regards to infrastructure, connectivity and security and we’re giving them more time to concentrate on what they do best – product development.

What do you think is the benefit of using data science in IoT?

Big Data has been ‘the’ big topic, right? I think one of the reasons is that big data probably didn’t succeed as much as expected. There was a hype but then it dropped. There was not enough Big Data. What IoT actually creates effectively is simply a huge amount of data coming in. So in a nutshell IoT is just an enabler for Big Data. We see ourselves as being an intermediary between the business which will create tons of data and the solutions and data scientists who slice and dice the data, providing the actual intelligence for these businesses.

Why did you choose Ireland for your HQ?

We have a lot of diversity inside the company. Most of the people came to Ireland for work.  The majority of our staff worked for Google. What’s beautiful about Ireland is that it’s part of Europe, it’s a relatively inexpensive place to live, it’s in the Eurozone, it’s a 3 – 4 hours short flight from anywhere in Europe.  It’s between the US and Europe, flying to NY is 6 hours and it has a small market. What you see here in Germany [at CeBIT] is that most of the promotional material is in German. Unfortunately, I don’t speak German so I can’t understand what they’re talking about and this [CeBIT] is an international event with 200.000 people coming from all over the world. Plus, Germany is a big market in which companies can  produce mainly for Germany. The diversity that can be achieved in Ireland is not needed if you aim at one large market.

We had a chance to move to Silicon Valley for example. But, in that moment, economically it made more sense to stay in Ireland and we achieved much more. In Silicon Valley everything is more expensive. There’s a lot of talent. But, the talent is hopping from job to job because there’s a large variety of jobs.

Ireland also has pretty good conditions for startups now. It’s actually great when the government helps you as a company.

What are the other significant shifts you see in IoT?

Everyone tries to play into it [IoT]. Every week you see a huge announcement of a big company going into IoT, pouring tens if not hundreds of millions of dollars into development, saying they are going to be the next big player. And it’s great, because it creates more awareness and brings more opportunities to the market. So, we definitely see more businesses entering the market because they figured out that an existing product or a new product that is IoT-enabled will be much more sellable and actually bring more revenue to them. To businesses it’s a no-brainer that IoT is positive, it’s not a fake hype. If you’re for example in British Gas and you install a thermostat which is connected, customers are more interested and their electricity bill will decrease because you are only using heating when you need to, not according to preset timers. Or take connected cars – you won’t have to bring your car to a service station to install a new feature. It can be pushed over the air. Or in health, what we now have are simple trackers, but they will move to becoming  solutions like nano robots in your bloodstream that  tell you ‘oh this fella is about to have a heart attack’. This is not not very far away.

By bringing all devices online we create an additional value not only for businesses but for people. It’s going to be securer and safer because you can prevent a lot of things before they happen. When you have things that talk to each other it’ll be easier to prevent issues.

A lot of problems in the world right now are because people or things don’t communicate in the right manner. Apart from the golden billion, there are an additional six billion people on this planet that will leapfrog. Take parts of Africa where they never had landlines, they leap-frogged into mobile phones directly.

I told you about this chip right? It’s actually a Chinese company producing that chip, it costs about 3 dollars and has an MCU which holds enough memory to make one person feel like a WiFi antenna. It works for a distance of up to 350 meters and there is enough capability to actually embed it into pretty much anything and make that thing connected. At the price point, you can put it pretty much everywhere. Take trackable clothes. Clothes producers are already thinking about how to track shorts and trousers etc.

In five years we’re going to live in a whole different world. But, you need to have security, sensibility, data protection and privacy. Ten years ago we had no phones in our pockets. And now everyone has at least one. Actually, we do much more with them than just phone people. We share everything that’s happening in our lives with our technology.

I watched a talk from Eugene Kaspersky, CEO of Kaspersky Antivirus and he said the biggest threat to the user is himself. Because the amount of information we share about ourselves is enormous. A lot of people post so much on Facebook. They don’t even understand that people outside do have access to that. How old you are, where you live, when and where you are going to travel. So something like connecting them, sending data somewhere can be actually more sensible because you can actually create hard rules to do what you actually need to do. Security is the big issue to solve around IoT.

If you could tackle any technology solvable problem existing today what would that be and why?

One of the biggest challenges for the IoT is it’s diversity.  Companies create a lot of different things that don’t talk to one another. Ideally, I would like to see everything in our lives (which is connected) be able to talk to each other without us. And, I think it’s a long shot, but, when things start talking to each other, things will be way easier. Let’s say you arrive to your house in your smart car. By the time you approach your house, the gates are opening, you park in your driveway and you walk out and the car parks itself in the garage. You come to the door and the door opens because it knows it’s you, you don’t need keys.  Once you enter, by the way your heater knew you’re coming, your system knows that it is Thursday and usually Thursday night you have a glass of wine. The fridge already knew this but also knew that you had ran out. It ordered wine for you and it’s on the way before you even arrive home. These are small things but imagine how much time we’ll free up!

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Meet the people at the forefront of Big Data and world changing technology

Where: Rise Tel Aviv, Tel Aviv, Israel

When: September 25th 2016

datanatives.io

Taking place in one of the world’s largest startup nations, Data Natives Tel Aviv celebrates four areas of technology that are driving business innovation and the next wave of billion dollar startups. Join industry leaders specializing in Big Data, IoT, AI and Machine Learning.

Amongst 30 of the brightest minds, we are proud to be joined by thought leaders from across the globe:

Aldo de Jong: Co-Founder at Claro Partners, the #1 accelerator in Europe

Crystal Valentine: VP of Technology Strategy, MapR Technologies

Thomas Renault: Université Paris, Ph.D. Student and Research Assistant

Four key areas of innovation – IoT, AI, Machine Learning and Big Data

Taking place in one of the world’s largest startup nations, Data Natives Tel Aviv focuses on four key areas of innovation: Big Data, IoT, AI and Machine Learning. Data Natives Tel Aviv is a vibrant, innovative and international daylong conference for the data-driven generation. The conference is hosted by Dataconomy, Europe’s leading media and events platform in data science, with a loyal community of more than 32,000 data enthusiasts online and offline.

Inspired by the successful 2015 Data Natives conference in Berlin, Germany, Data Natives Tel Aviv was created to spur the interaction between startups and businesses within the growing fields of Big Data, AI, Machine Learning and IoT – on a truly international scale within a country built on innovation and startups.

Data Natives 2015 attracted some of the world’s greatest Big Data influencers and industry leaders. Now is your chance to come and learn from theleading data scientists, startup founders, analysts, investors, economists and other industry thought leaders from across the globe.

Big Data – Data natives are complex.

Big Data technologies – particularly machine learning – are at the heart of much of the innovation we see today. At Data Natives we will host industry experts discussing how data science is disrupting a variety of industries, and the business value of data.

IoT – Data natives are hyperconnected.

The growth of the Internet of Things ensures that every aspect of our lives, on personal and industrial scales, is trackable and optimizable. This technological evolution represents a huge opportunity for business.

Machine Learning – Data Natives can predict certain outcomes.

Machine learning algorithms are being applied more frequently to predict certain outcomes and behaviors. At Data Natives Tel Aviv, we will demonstrate and discuss how machine learning is being applied to technology to predict user behavior and business intelligence.

AI – Data Natives rely on smart technologies.

AI enables the development of computers to take on human tasks, particularly those associated with human intelligence. Self-driving cars, smart homes and other revolutionary technologies rely on AI to function. Learn more about how AI is driving technology during Data Natives Tel Aviv.

Join us for Data Natives Tel Aviv 2016. Save your spot by registering today!

About Dataconomy

Dataconomy is the leading portal for news, events and expert opinion from the world of data-driven technology. Founded in Berlin, a hub for data science innovation, we provide a global network of industry-renowned contributors and local communities all across Europe. We focus on industry giants and disruptive startups alike, looking at only the most interesting applications of data technology. A top 100 Big Data brand according to Onalytica, and a top 5 Berlin startup as rated by Mattermark.

Image: Sebastian Geisel

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Cisco and Microsoft have recently invested in the Internet of Things (IoT) – indicating not only that IoT has reached massive scale, but that tech giants are clearly putting their bets behind it. Why? Because IoT is changing the game. Consider the collapse of the I-35 W Mississippi River Bridge in Minnesota that caused multiple fatalities and hundreds of injuries. When rebuilding the bridge, architects could equip smart cement with sensors to monitor for weaknesses that developed in the infrastructure over time. Those sensors could also communicate the presence of ice to sensors in one’s car, alerting drivers when they need to slow down, or if one was driving a smart car, have the car slow down itself. And that’s just a glimpse of what’s possible with IoT.

While we’re consistently discussing the potential of IoT and connected “things,” what’s often missing from the story is how to better develop security practices that evolve alongside. As the number of “things” continues to grow, so should an enterprise’s security program, adapting to the new tactics of cyber criminals and to address data fatigue. Yet enterprises aren’t properly investing in security for their increasingly complex networks filled with more and more mobile devices, or they’re applying security after the fact, when it’s too late. Cost issues and a confusing, overly-crowded market are just two factors that come to mind as to why organizations are not developing proper security programs, leaving them at a much higher risk for attacks.

Perimeter Defenses Lag Behind Modern Day Threats

Historically, organizations have relied on perimeter defenses – the Fort Knox solution – and monitoring solutions when the threats were known. Unfortunately, these tools have fallen short as attackers become more sophisticated and threats are increasingly unknown. This may seem a bit obvious, but I bring it up because rules and signatures are the foundation on which perimeter defenses and traditional security monitoring solutions have built their success. When threats are unknown, there are no signatures or rules to identify the advanced attacks that are regularly deployed by attackers. These are slow-and-grow attacks, occurring in multiple phases over long periods of time that either don’t trigger alarms from traditional defenses or if they do, activate warnings that by themselves appear harmless.

User behavior analytics (UBA) has emerged to help find unknown attacks that are being exploited in the wild. UBA creates baselines for normal user behavior, connects the dots between these separate, seemingly harmless events, and compares the normal baseline to the current activity, thereby revealing an attack. However, as IoT continues to grow and the attack landscape evolves, UBA will fail to keep up with the growing number of IoT devices – primarily because exploits of IoT vulnerabilities are generally not linked to a user, rather to a “thing.” For example, there are many types of network devices (e.g., servers, dropcams, etc.) within an organization that are not associated with a user. During a multi-stage attack these “headless” devices can become compromised, leaving organizations exposed.

Combining User And Entity Behavior Is The Answer

While profiling user behavior is necessary, it alone is not sufficient to satisfy enterprise security needs. To ensure an organization has the comprehensive visibility needed to combat attacks that will inevitably come from vulnerabilities introduced by IoT devices, it’s critical that any behavior analytics solution can not only establish a baseline for users, but also for entities (i.e., hosts, IP addresses, applications). Even Gartner’s thinking has evolved – the organization went from publishing a Market Guide on User Behavior Analytics in 2014 to publishing a Market Guide on User and Entity Behavior Analytics (UEBA) in 2015. Avivah Litan, who authored the most recent report, outlines the reason for this change:

“The letter “e” in the term UEBA recognizes the fact that other entities besides users are often profiled in order to more accurately pinpoint threats, in part by correlating the behavior of these other entities with user behavior.”

UEBA is at the crossroads of the next wave of security monitoring and attack management. However, the technology to natively handle the “e” part of UEBA cannot be added after the fact. Organizations must employ a security solution that integrates the “e” from the start because moving from a user-only view of the threat environment to the n-dimensional world of entities requires a fundamental overhaul of everything from data formats, data storage, compute scale, analytics modules, etc. Think of UBA alone as the equivalent of listening to a song with only the bass turned on. You’re hearing lots of volume, but it’s not until all the other sound components are enabled that the true nature of the piece becomes clear.

As the threat landscape evolves and as IoT increasingly adopts more “things” not covered by traditional monitoring and detection solutions, attackers have new vehicles to penetrate the network. The pervasiveness of IoT and connected devices means that cybercriminals have an even better chance to gain a foothold within the enterprise or to find a point of weakness to exploit as endpoints continue to increase in number and mobility. With UEBA, organizations can protect against external threats that make their way inside the perimeter as well as the insider threats that already exist – essentially protecting data from the inside-out. UEBA is designed to find attacks that have eluded real-time defenses. Investing in a long-term architecture and solution designed for both users and “things” through UEBA will provide the visibility needed to speed both attack detection and investigation, enhancing an organization’s response capabilities before more damage has been done.

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Many industry prognosticators (and not a few vendors) are pushing Internet of Things (IoT) technology but are somewhat vague as to how financial results for actual businesses will be materially improved. Instead, they tend to focus on concepts like “digital transformation,” which sound promising but is difficult to quantify. This places many businesses in a quandary—they are aware of the fact that IoT holds great promise but they can’t really move forward without being able to identify a tangible ROI. And make no mistake, unlike consumer IoT, and even government-sponsored IoT initiatives (think smart cities), without tangible and quantifiable improvements in financial outcomes (i.e. an ROI), businesses will be hard pressed to move forward with the vigor that industry prognosticators think they should.

Unfortunately, this lack of focus on tangible business uses cases has the potential to stall growth in IoT and deprive businesses of many of the benefits they expect to realize from it (and growth in M2M should not be interpreted as growth in IoT; M2M is simply connectivity while IoT typically involves multifaceted systems incorporating machine learning, data analytics, complex rule generation, and automated orchestration of actions).
Fortunately, there is an approach to IoT deployments that can, depending on the nature of the business, demonstrate fast and meaningful payback. This approach focuses almost exclusively on actual business-oriented IoT use cases but it differs from the “platform-first” strategies being pushed by many vendors and analysts.

The platform-first approach

Proponents of the platform-first school argue that organizations of all types should first make a corporate-wide decision on an IoT platform. Although the definition of IoT platform is somewhat broad, in general it is the system upon which applications can be built to take advantage of data being collected from myriad dissimilar devices. After standardizing on an IoT platform, organizations then need to purchase or develop applications, potentially integrate other enterprise systems and ingest device data. Only after they’ve done all of that can they begin to focus on IoT use cases that actually benefit the business and presumably, generate an ROI.

The challenges associated with this approach should be obvious. In most cases businesses are being asked to make significant financial investments with no clear view as to what, if any, payback will result. They are also assuming substantially more risk than would otherwise be required since they are being forced to make enterprise-wide decisions on what is still, in many cases, evolving technology. Finally, platforms are by definition incomplete systems; they need applications to be developed on them before they can be deployed in production. This aspect carries the downside that time-to-benefit is lengthened.

The use-case-first approach

The reality is that, as has been the case with most major technologies adopted by businesses over the last quarter century, initial production deployments of IoT technology will almost always be related to individual business initiatives. These initiatives are typically not viewed, nor should they be viewed, as IoT initiatives. Instead, they are focused on driving specific business outcomes—for example, improving asset uptime, reducing service and warranty costs, improving food safety, complying with government regulations, adding new revenue generating services, etc. It is only as organizations evaluate technologies that can help them meet these business objectives that they frequently discover data generated by various distributed devices can be harnessed, analyzed, and used to automatically drive business processes. In other words, these ROI-producing business initiatives begin to take on the aspects of IoT initiatives.

The important point here is that successful—and successful means that they (a) work, and (b) provide a financial return—IoT initiatives are those for which the main goal is a quantifiable business outcome and in which IoT only plays a supporting role, albeit a critical one.

How did we get here?

It is possible that the chief reason we see a bifurcation in approaches to industrial IoT is that different businesses may choose different departments to spearhead these efforts. For organizations that look to IT to lead the charge, the platform-first approach will be more attractive as it potentially represents a uniform architecture that can be deployed across the enterprise. However, it also represents an “IoT for IoT’s sake” approach that may fail to deliver a payback and leave the business somewhat frustrated with the results.

Use-case-first initiatives, on the other hand will almost always be sponsored by OT (operations technology; really any revenue generating line of business). As such, there is by definition an ROI-producing business objective driving things and IoT, while critical, is incidental to the initiative.

Is there a downside?

Some will argue that the use-case-first approach could result in dissimilar systems being deployed in different parts of the organization, long the bane of IT’s existence. While this could be the case, the potential downside is mitigated by the fact that—unlike the early days of computing (Mac versus Windows) and local area networking (Ethernet versus Token Ring)—IoT systems use well-established protocols and standards. This allows dissimilar systems not only to coexist but even to exchange information and leverage multiple data sources.

While this battle is likely to continue for some time it is also likely that OT will prevail, at least in the near term. The reason is simply that ROI drives everything for businesses (again, consumers and governments tend not to care whether their investments are well thought out) and IoT platforms, by themselves, are hard-pressed to generate meaningful financial returns.

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Developers are gatekeepers to the future of possibilities enabled by the Internet of Things. Billions of new devices will go online and connect to the cloud by 2020, from simple sensors to smart light bulbs, connected machinery and the gateways managing all those connections. Developers are responsible for equipping each of these devices with the software and applications needed to make them useful, but the developers have their work cut out for them. Interconnectivity will be critical to the continued development of IoT but a common framework for IoT development is still needed.

Too much code

Currently, coding for a sensor, a gateway and a light bulb require slightly different skills. It makes sense for more code to be needed as devices get bigger and more complex, however, even basic products require huge amount of software using current solutions. In addition, coding knowledge does not often transfer from project to project, particularly if they change the hardware class and operating system. While Linux and Android developers have access to an open source framework and tools many IoT developers build from the ground up. The command to trigger primary functions like rebooting a device to apply an update or accessing its serial port and other data should carry over from one IoT device to the next, regardless of size. An open source framework for IoT devices with an uniform and easy to use API would make life a lot easier for developers.

Uniform API would be just one of many benefits of an open source framework. Many IoT devices will be installed and need to be managed, accessed and updated remotely. There’s a broad assortment of hardware and operating systems on the market, but a uniform API and framework can streamline the development process across the board.

Developers hone their skills in certain coding languages and architectures, it doesn’t make sense for each IoT device to require reeducation. Innovation would accelerate if developers could apply a familiar architecture to a variety of devices. That motivated the Soletta Project. The capability to abstract code from one device to another is critical to developers. Additionally, Soletta allows them to create in high-level languages, optimizing the files for devices of different sizes.

The need for interconnectivity

A widely adopted IoT operating system would need to meet some key criteria, all relating back to the importance of interconnectivity. A common set of those basic on/off and access commands would be a good start. It would also need to be suitable for the wide variety of devices on and entering the market. A central gateway and all the sensors and other devices connected to it could run the same protocols. In order to do so, the software would need to be able to function on low energy and low memory as smaller devices have limited access to either. To be truly practical, the best operating framework will be able to work alongside others, including Linux or Zephyr OS. Last, but certainly not least, development needs to occur in a secure environment and lend itself to ongoing safety updates as security is chiefly important to the success of IoT.

The continued development of IoT will lead to a more efficient, connected future. That future, however, is largely in the hands of the developers working on IoT projects. While the wide variety of IoT devices lends itself to unique software, some features need to be standardized across all applications. In order to reach the goal of IoT interconnectivity developers need an open source framework they can all build upon.

image credit: Nando.uy

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How drones, sensors, and even Google Glass are making news better.

The Internet of Things is set to disrupt media like never before. Marketing and advertising will be reborn, understanding and reaching consumers with an unprecedented degree of precision. That is not, however, the only way media will be changing. The internet completely changed the way journalists could interact with data. They could find more information, reach more people, and uncover the truth faster. The IoT is already infiltrating the way journalists work, and it has happened so seamlessly that the world almost hasn’t noticed.

Drone and Sensor Journalism

Consumers are busy reading about drones. The increasingly popular technology has sparked incredible videos, intriguing stories, and debates on ethics. Yet, the role they are beginning to play in journalism is almost unnoticed. This is perhaps because the entire world is adapting to new technology, and it’s emergence in the journalistic industry seems natural. Thomas Hannen, Innovation Producer for the BBC’s Global Video Production Unit, told Wired his first thoughts on drone footage: “We were seeing these amazing videos appearing on the internet and I remember vividly saying to people, ‘Why doesn’t the BBC do this? It would be great’.” The results were quick, and the team now owns multiple haxacopters used to gather footage. Drones don’t just offer up pretty landscape shots, but important images that complete a story. All kinds of footage comes from drones—from images of the sunken Costa Concordia, to Nebraska’s intense drought in 2012. When the Donetsk Airport was devastated last year, drones were heavily relied on to find footage, and led many to at least one case study on the topic. Saverio Romeo, principal analyst at Beecham Research, paints an empowering image of IoT in media:

“Let’s take a journalist crew in war zones. Maybe, they want to use AR [augmented reality] smart glasses in their activities, maybe recording what they see, and send data to the studio. Maybe, they want to use drones and they want to monitor and control the drones, but are also able to take the data from the drone and real-time analyze that data. Here, things get a bit more IoT: different sources of data from different devices and sources.”

Understanding disaster areas, war zones, or simply vast landscape is made both easier and safer with the internet of things. In fact, this is the very reason the term “sensor journalism” is becoming more and more pervasive. Journalism is very intensive, and journalists can’t always do large-scale, powerful, evidence-based all on their own. The world moves at rapid speed and readers need truly up-to-date information and news. The ability of smart technology to constantly gather data means journalists can get their hands on reliable and current information. Sensor Journalism relies on data input from sensors to trigger and support stories. According to the O’Reilly Radar, the Spatial Information Design Lab At Columbia University partnered with the Associated Press to uncover the truth about air quality in Beijing. The Chinese government had been pressured to improve air quality in preparation for the 2008 Olympics. While they released some information on air quality, they did not provide raw data, and made it difficult for outsiders to see what was really happening. Using cellphone sensors, reporters and the Beijing Air Tracks Project measured air quality and got the story, themselves.

Finding and Telling Stories With Wearables

The image of futuristic reporters wearing Google Glass is almost so stereotypical it’s funny. “Glass journalism” seems incredibly awkward, as if a journalist is able to simultaneously interview and read a running list of text scrolling through their vision. That future is actually much more realistic than it seems. That’s why the University of Southern California’s Annenberg School of Journalism’s new Glass Journalism class has been covered by nearly every online news source around. The class explores how wearable devices will influence journalistic storytelling and news gathering. Glass and wearables in reporting will lead to better storytelling, as well as a slew of new apps and jobs. This is also why many see the future of reporting being intertwined with “citizen journalism.” The IoT enables individuals to be part of journalistic narratives, and to easily share unexpected stories on a very deep level. The “maker” nature of the IoT also means hacktivists and community will play a large role in the future of journalism. Hackers and makers can make open-source smart technology better, and get journalists (both full-time and “citizen”) better access to important data and stories.

Hype and Quandaries in the Internet of Media Things

The “internet of media things” is on the rise. It will enable new stories to be found, and be better told. However, as with any system that relies on data, there will major complications. Increased use of data tracking will inevitably lead to false conclusions. Mixing new, unstable technologies, with news sources that don’t yet know how to use them might lead to misreporting, or even manipulation. Third-party verification may become necessary—which will slow down the speedy stream of news. 2016 has also been termed the “year of the overdrone” in journalism. Directors may grab at anything technology will give them. Unnecessary imagery could lead to reporting that no one wants to see, or over reliance on its novelty. Drones and exploratory technology will, and already has, led to legal quandaries. Though connected tech can help journalists explore dangerous war zones, it also allows them into restricted areas. Many countries are struggling to create appropriate laws, and at least three unwitting BBC journalists were detained last year in Switzerland for using drones in no-fly zones.

The future is rife with narrative storytelling and technology, and it may happen quicker than readers expect. Robert Hernandez, the brain behind USC’s Glass Journalism class, puts it bluntly: “This is not sci-fi. It’s real stuff.”

image credit: Global Panorama

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Wearables continue to be a hugely popular item across different age groups and even continents. They track steps and sleep cycles, but just how valuable is that information? In reality, this data is only partially useful for the wearer. Without big data to decipher what is more or less healthy, those wearables are borderline useless. More importantly, there is a much bigger game at play. Researchers, developers, and startup entrepreneurs are all using that data to see the big picture. It might now matter how much sleep one girl living in New York gets. But if researchers could track how everyone in New York, or everyone in the world, sleeps, that could lead to incredible insights.

The Wearable Dynasties Have Mountains of Data

Jawbone makes several popular fitness trackers. They’ve sold millions of their “UP” tracker around the globe. While their users were busy tracking sleep cycles for their own purposes, Jawbone was compiling massive datasets. The study of sleep is by no means a new practice. But now, rather than having some hundred or hundreds of test subjects, companies have access to data from millions of individuals in their natural settings. By tracking millions of UP wearers in North America, Europe, Australia, Japan and China, Jawbone stumbled upon some weird facts, with large amounts of data to back it up.

Their data found that women, on average, sleep some 20 minutes longer than men. The company has some thoughts on why, including a biological need for women to get more sleep due to the birthing process; the reason might also lie in men being more prone to sleep complications. In reality, Jawbone, as a wearables company, is not likely to crack the complex biological functions that could be at play by themselves—but they’re procuring all the data needed for researchers to move the discussion forward. By discovering which countries’ citizens get the most sleep, they can find links and insight into other areas of life, including major problems like obesity. They’ve even stumbled into other highly specific data stories, like how going to sleep at a later time leads to higher heart rates in the morning, or how folks that commute tend to get less sleep.

Unexpected Solutions

Luckily, Jawbone isn’t alone in the field. FitBit, with 9.5 million active users, has gathered more data in one year than early sleep scientists would have seen in their entire careers. Fullpower technologies makes the monitoring software used in several of today’s most popular wearable devices. CEO Philippe Kahn told Fortune that today’s experiments are huge, worldwide endeavors that the field has never seen before. “We have 250 million nights of sleep in our database, and we’re using all the latest technologies to make sense of it.” Data from wearables still suffers from problems with inaccurate sensors and calculations, but it is slowly getting more accurate. Plus, by tracking users in their home environment, the data becomes much more valuable and realistic. Lab studies often involve being hooked up and spied on, and only absorbs a small portion of daily life. If companies can turn billions of numbers and data points into usable information, the future may include anything from smart pillows, to details on how our actions during the day affect our nights.

In reality, solutions like smart pillows are already taking off. They operate not only by tracking your sleep, but by, hopefully making it better. Studies have shown that certain frequencies can enhance the sleep cycle—so why not have technology that recognizes where you are in your cycle and play the right sounds to enhance it? Data has also shown that waking up during a light sleep phase makes a humongous impact on whether you have an energetic day or a “hit the snooze ten times” kind of day. Now there are tools to help users wake up feeling more refreshed by tapping into that information. Forget waking up at exactly 7:00 AM. Technology can recognize and wake you when you hit a light sleep cycle, which may be around 6:45. Don’t worry about missing those few precious minutes. The body should actually feel better waking up at the right point in its own cycle.

Where’s the Proof?

Who really wants to give up those last fifteen minutes of sleep just because “science” said to? There are actually plenty of success stories surrounding the use of data to achieve better sleep and more energy. One great example comes from Forbes, who shared the story of former Olympian cyclist Sky Christopherson, who was brought in to help the US women’s cycling team in 2012. He used his own “Optimized Athlete” program, where he focused on “data not drugs.” The team members generated huge amounts of data on their workouts, diets and daily patterns. By making seemingly small, data-driven changes, their individual performances went through the roof. One particularly unusual discovery included the role of temperature in the sleep cycle. One of the cyclists discovered that she performed much better if she slept at a lower temperature. So, they traded her ordinary bed for a temperature water-cooled mattress to keep her body at the perfect temperature. “This had the effect of giving her better deep sleep,” said Christopherson. “Which is when the body releases human growth hormones and testosterone naturally.

The elusive good night’s sleep isn’t so far out of reach. While fitness trackers are still mostly used on a small, personal scale basis, the future holds a very different story. If better sleep leads to better health, and also higher levels of happiness as Jawbone postulates, the future of healthcare has a big stake in turning data into good sleep. BCC Research predicts that sleep-aid products will be booming in the upcoming years, and those smart pillows, data-driven alarm clocks, and “data not drugs” approaches will no doubt help the tired, groggy, and grumpy get finally some much needed rest.

Each of the Jawbone studies also include several charts

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Every day it seems, there’s a new buzz worthy entrant into the IoT market – from smart plugs that efficiently connect you to your power source to connected cars that smartly help you avoid would be accidents.

But what if you don’t have an IoT device in the pipeline? What if you don’t have legions of R&D wizards at your beck and call? What if your business has nothing to do with smart homes, smart cars, or any of the other industries currently being heralded as first movers in IoT?

No worries. You can still enjoy your day in the sun.

Here are five things that probably wouldn’t be top of mind but can accelerate your entrance into the IoT market so you can claim your share of its projected $8.9 trillion in revenues.

1. Forget about gee-whiz appliances

With media sensations like Nest, it’s easy to get the idea that you need a highly sophisticated product or appliance to get in on IoT revenues. Truth is, IoT monetization opportunities go far beyond the self-adjusting thermostats and wearable fitness devices that hog all the headlines.

The real money to be made in IoT will come from the services that connected devices make possible. And in most cases, those devices, the “things,” of IoT, will consist of low-cost sensors and smart chips. If the data they generate creates incremental value for your customers, then it can be monetized.

The bottom line — you don’t need to offer a flashy product like Anki Drive to capture IoT revenues.

2. Look to early adopters for inspiration

Industries that are already monetizing IoT include smart homes, personalized consumer services, retail, transportation, healthcare, and manufacturing. Maybe you don’t see your place at the table because your business is not involved in any of these areas. Take a closer look.

Within each of these categories is an abundance of niche opportunities ripe for monetization by forward-thinking companies. Take personalized consumer services. With geo-location technology, you can now monitor your teen driver’s whereabouts, keep tabs on wayward Fido, and never lose your stuff. In healthcare you can rest assured you’ll always remember to take your medication.

The barrier to entry for applications like these is low. That’s because the solutions are simple and the cost of the chips that make them work is relatively low. The value is not in the products, but the services. Many of which can be billed on a recurring basis, by the way.

As IoT technology proliferates, so too are similar niche opportunities in areas as wide-ranging as connected cities, energy, infrastructure optimization, supply chain management, resource allocation, personal productivity, inventory control, logistics, government, military, agriculture and environmental management. The possibilities are seemingly only limited by your imagination.

3. Data capture added to your existing product or service is pure gold

One of the shortest routes to recurring IoT revenues is to offer useful data to your customers that enhances a product or service you already provide. For example, sensors built into industrial equipment can monitor machine performance in real time to prevent unplanned downtime.

This strategy works just as well with services. Auto insurance companies like Progressive and Allianz France use telematics to help their customers become better drivers and lower their premiums. Progressive’s drivers can monitor their performance online, while Allianz sends the data to its customers’ smartphones. The information gives customers insights to improve their driving habits and lower their fuel and maintenance costs.

Best of all, these companies didn’t invent these systems from scratch. Each service piggybacks on technology already built into cars. They plug into a car’s diagnostics port. In simple terms, all these companies are doing is harvesting data that cars already generate and packaging it in a way that’s valuable to their customers.

4. Court strategic partners

Allianz France didn’t create their IoT offering completely in house. To make their concept a reality, they needed outside expertise in GPS solution development. They teamed up with TomTom, the global GPS specialist.

Lindsay Corporation used the same strategy to develop their IoT application, FieldNET, which enables farmers to remotely manage crop irrigation from smartphones and tablets. The company’s lack of expertise in software development didn’t prevent it from jumping on a hot IoT opportunity.

There are many areas of specialization required to bring a successful IoT offer to market. Product development, usability, ergonomics, network optimization, customer engagement, marketing and agile billing are just a few areas of expertise that can be provided by a growing realm of IoT ecosystem providers.

5. Take an indirect route

Many projected use cases for IoT won’t directly generate revenues. To achieve greater efficiency, companies are deploying IoT sensors internally throughout their organizations to optimize their use of everything from computer servers to lighting to HVAC systems to employee productivity.

“Smart cities” are doing the same, only on a much larger scale. Amsterdam is leading the way – it’s employing IoT technology in everything from improving traffic flows in real time to emergency response to optimizing public transit use and maintenance.

On the surface, these particular applications of IoT primarily cut costs instead of generating revenue. However, they represent significant indirect revenue opportunities for savvy companies that can provide the many moving parts required to make IoT magic happen.

A short sample of IoT expertise that companies, cities, government entities and the military are eager to enlist include systems integration, sensor device deployment and management, mobile software development, big data capture and analytics, customer care, telecommunications and data security. In many cases, the contracts for these services will be ongoing and rely on a recurring revenue model.

Bringing the IoT into the enterprise isn’t only an issue of upgrading your tech, but also upgrading your people’s knowledge, flexibility and speed. As is the case with great technology endeavors, thinking outside the box is required to help you find a path to success.

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Understanding how the Internet of Things is going to change childhood is hard to imagine. The IoT (Internet of Things) describes the way objects can “talk” to each other via technology. Your smart watch talks to your phone. Your apps can talk to your new smart thermostat. Widespread fear about technology ruining childhood is in large part thanks to sensationalism. New technologies are often painted as either a soul-consuming monster or the flawless face of advanced society. Somehow, people often forget the role of parenting. Technology advances exponentially fast, but we won’t have connected robot nurses for several years. The final factor in how a child will be affected by technology still comes back to the parents. As parents, it’s important to understand what technology is and what it means for children as a life experience.

The Beautiful Positives of Connected Children

Yes, the internet of things does offer great opportunities for kids. With the rate that technology is developing, products that help children learn abound. Kids can (and have learned) to code on ever cheaper digital platforms like Raspberry Pi. If you thought Lego helped creativity and problem-solving, picture those hooked up to wires, screens and batteries. Kids don’t just create rocket ships; they can make apps that drive rocket ships. The internet is filled with resources for kids to learn through technology. Studies are even finding that technology greatly improves a child’s ability to learn. It keeps them engaged, and allows them to work on their own. Kids today can learn fast—faster than ever before.

For those that need to keep track of health information, the IoT is a godsend. Health monitoring can be vital for children, especially those too young to understand what it means. The perfect example is Teddy the Guardian, who is the smartest stuffed animal around. It checks baby’s heart rate, temperature and the oxygen saturation just by receiving hugs. It can alert mommy and daddy to any signs of trouble. Smart, practical tech exists. It doesn’t have to replace parenting or interfere with a child’s growth, and few parents could say “no” to something as logical as Teddy the Guardian.

The terrible, horrible truth about connectivity

The FiLIP watch takes helicopter parenting to a stalkerish low. It grants parents “the peace of mind they crave, while providing kids the freedom they need to be kids.” It embodies the possibility that parents will too far. FiLIP lets parents know where their children are at every point in the day. It is designed to give parents the ability to talk to their kid whenever they feel the need. Tech that acts like a leash on a child is not just unnecessary, but also kind of creepy. It could be as detrimental as hooking them up to a FitBit and counting their every step and calorie. It highlights a very strange disconnection. Many parents who fear technology are also the same who feel compelled to hook children up to a perpetual monitoring system.

A study printed in the October 2014 issue of Computers in Human Behavior also shows that interacting with technology makes it harder to interpret emotion. By taking two groups of children and allowing one to consume large amounts of media while constricting the other, researchers found a very strong correlation. Those who consumed less media could read facial and physical expressions more easily. However, another study from professor Doris Bergen in the Miami University Department of Educational Psychology may accidentally shed some light on this entire conversation.

Some see technology as signs that parents and educators are becoming lazy. Convince your kid to brush their teeth with a game app. Consult your phone to see when you need to talk to kid. Perhaps sensors could be used to track the eyes of a student to get a better idea of their ability. What if, as one major news outlet suggests, these numbers were used to track and grade engagement levels in school. These numbers would help grade students on effort and send the better performers to university. Don’t worry, that model is far too expensive for any school in the near future to even consider. Technology in the classroom is not hyper-connected, and will not be for many, many years. The IoT has not brought doomsday to education and childhood, quite yet.

The Grey Matter

The brain of an infant grows fast. Within the first few years of life, a lot of rules are set for things to come. While the infant brain has been extensively mapped, not much information actually exists on the relationships between baby and tech. Studies are not always conclusive. Research has shown tech to be both a blessing and boon. As Bergen explains, “If young children spend more time in technology-augmented play, this type of engagement may result in fewer interactions with parents, other caregivers, other children, and even with physical objects in the environment. Thus, brain developmental patterns and inactive cognition in such children may differ from that of children in past generations.”

One of the biggest fears parents (and bystanders) seem to have about children and the IoT is not the technology itself. Rather, they fear how adults will use it. Given how little hard evidence exists, and that new technology is popping up every day, the best option may be to step back. Many who argue that connected technology is good also follow the “interaction not isolation” mantra. The world doesn’t need MIT studies to tell it that learning from experience and interaction can be better than through video and games. There is no “all-in” or “out” with technology and children.

Adults of all ages often see new technologies as newfangled nonsense they would never have been allowed to play with as children. Actually, most of us did play with tech as children. What twenty- or thirty-old hasn’t owned a gameboy? Who hasn’t heard of AOL chat rooms, or napster? The internet of things is very different from the technology of yesteryear; yet, perhaps, today’s children are not as different as they seem.

One vital step forward has been the removal of the mouse. Removing the mouse and big, klunky keyboards has lifted the wall between user and technology. The “swipe” has been the focus of many psychology essays. A four-year old can now interact with a computer in a highly direct manner. This could lead to removing mental barriers between the self and technology, itself, allowing kids to grow up and completely change our ideas about technology.

Growing up in a connected world also means data. The topic of protecting children’s’ data in a connected world is a paper all its own. Data generation and analysis, however, does mean greater effectivity and opportunity. Playthings, education, health. These all benefit from the circular nature of data. The big looming question is the time and manner in which children interact with technology. Research has shown the children only begin to understand the symbol nature of a screen at about three years of age. That may be the only golden number when it comes to the Internet of Things and your child.

image credit: Marcus Kwan

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This year we saw the next generation of aircraft powered by the capabilities of Internet of Things (IoT) and Big Data hit scales that have never seen before.

At last year’s Paris Air Show, we saw the latest state of the art aircraft sporting engines that are able to monitor up to 5000 elements every second, some of which are also capable of generating up to 10 TB of data per flight. In comparison, at the end of 2014, it was estimated that Facebook accumulated around 600 TB of data per day; but with thousands of planes in the air at any one time, there is the potential to download zeta bytes of data. It seems therefore, that the data generated by the aerospace industry alone could soon surpass the magnitude of the consumer internet.

Not only is more data created by industry than in the consumer world, it’s more valuable; validating the sentiment that not all big data is created equal. Instead, the data created by industrial equipment such as jet engines, gas turbines and MRI machines has more potential business value ona size-adjusted basis than other types of big data being generated from the social web, consumer internet and other sources.

For someone who has built engine health monitoring solutions on big data platforms and demonstrated a reduction in the processing time from days to minutes, working now with this volume of data has totally changed the game. These scales were beyond imaginable 10 years ago and the kind of data storage and computing infrastructure required to handle such data is truly mind blowing.

Questions could be asked around why so much data needs to be collected. Pratt& Whitney’s GTF engine uses great swathes of data to build artificial intelligence and predict the demands of the engine in order to adjust thrust levels. As a result, GTF engines are demonstrating a reduction in fuel consumption by 10% to 15%, alongside impressive performance improvements in engine noise and emissions.

Increased revenue streams

Ultimately these Internet of Aircraft Things solutions lead to additional business for engine manufacturers, OEMs and even operators. Bombardier recently announced that it has signed an agreement with Pratt to use their eFAST Health Monitoring System on the CSeries aircraft. Bombardier can earn more revenue by receiving data on the real-time performance of their engines, so they can adjust the way planes are flown and take care of potential issues before they become real problems that end up grounding airplanes.

The other major players are also on the same path when it comes to embracing the Internet of Aircraft Things. The new generation of GEnx engines started pumping 5 to 10 TB of data per day. GE expects to gain up to 40 per cent improvement in factory efficiencies by the application of IoT and Big Data Analytics. Rolls Royce collects similar amounts of data from 12,000 engines across the globe is its data centres.

While engines are leading the charge and embracing the IoT and data generation, avionics systems are also having to catch up to this trend and do so quickly. Traditional avionics systems transfer data up to a maximum of 12.5 KB/s whereas Boeing 787 Dreamliners and A350s are using Ethernet based, next-generation aircraft data networks, called AFDX that allows up to 12.5 MB/s. This makes it quicker and easier to transmit the information from avionics systems to the maintenance teams on the ground about current flying conditions, as well as any faults that have occurred during the flight.

While avionics vendors have been slow to introduce big data applications, they are today increasingly open to these proposals. The Internet of Aircraft Things is also helping manufacturers manage their Pay by Hour engagement models and long term maintenance contracts at lower costs.

With rapid advancements being made in the Internet of Aircraft Things and data analytics, it’s a truly exciting time to be working in the avionics industry. Soon, thousands of sensors will be embedded in each aircraft, allowing data to be streamed down to the ground in real-time. And who knows, in time, this could drive the famous black box to simply become a backup device!

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How does an IoT start­up stand out against a sea of competitors? Looking in new directions and finding those profitable IoT niches is one way.

The Internet of Things is everywhere. A report from VisionMobile found that there are 300,000 developers in IoT today, and that number will grow exponentially by 2020. There will be some 4 million developers in the world. So how does it keep growing?

New markets. Much like the push behind android and iPhones, consumers will provide the fuel for these new markets. One of the more intriguing jobs related to IoT are IT Solution Architects. Given just how complex connected systems can be, these architects connect the business needs and the technical system, taking responsibility to ensure that everything runs smoothly. It’s a big job, and it requires a thorough understanding of every aspect of IoT. This means both specialized technical skills and an understanding of the overall, business goal. Similarly, unearthing new markets will require a great deal of creative thought as well as knowledge.

Customers are, of course, central to understanding your product. It is impossible to know what problem you are solving if you don’t have the customer in mind. One of the first decisions to make is whether you want to work with big companies or consumers. Furthermore, there are several different aspects of IoT usage. IoT ­related jobs don’t just involve sensors. They involve data analysis, machines, and even marketing. In fact, much of the real money isn’t just in the physical tech but in the services that connect them. Moreover, there is never just one way to realize an idea. There are a multitude of outlets and possibilities. While pop culture might take better notice of trendy start­ups, like those focused on wearables, the real money, according to many, is elsewhere.

Security is one of the biggest concerns among consumers and designers as the IoT continues to expand. It causes several problems that need to be addressed in order to make the field safer, and to help customers feel secure. Not everyone has been jumping on the smart gear bandwagon due to fears of what connected tech (and those behind it) might do. With IoT expanding into other fields of tech, all kinds of IT employees are going to have to become comfortable with cyber security. One great aspect of the security field is that it has so many options. Physical or cyber, corporate­sized or individual­ personalized solutions, it offers opportunities everywhere something is connected. Skills in Java, JavaScript, Android and CSS are good backgrounds for jumping into this field. Being highly skilled in this area is a very desirable quality, as security and IoT will only become more complex over time. Cisco’s 2014 Security Report found that the world is already short some 1 million security professionals. Finding the right people to manage a highly connected world will be a big job with lots of open doors.

Industrial IoT is already home to the popular yet seemingly mythical Smart Warehouse. Industrial Data Scientists, engineers and designers will become vital as companies make the move to connected warehouses. Whether you want to design the smart robots that pick things up and put them down, or work to create connections that keep vehicles online, the logistics industry will be a booming place for IoT. As the system can be incredibly fragmented, with different companies using different standards and styles, it is an open opportunity for developers. Echoing several other papers and companies, a study by IoT Analytics and Boston Consoling Group found that “Germany for example would see a net increase of 350k (+5%) Industrial IoT jobs over the next 10 years.” We already know that manufacturing is a booming hotbed for IoT. The industrial internet of things is hardly a niche on its own; it’s the multitude of moving parts that offer plenty of opportunities to those willing to look.

3D Printing. For many, it’s a novelty. You can print toys and D&D dice, but what else? The biggest use of printing isn’t in the home, but in the tech world. It is vital for prototyping new technology. Speedo, Nike, Ford, NASA and two­ thirds of the top manufacturing firms rely on 3D development and printing. This also means that prototyping IoT tech can be done in much less time than before. Printing means faster models, faster changes, and moving to market faster. Of course, it is also used regularly in the medical field. This field requires a bit of creativity and the ability to really appreciate the cross­over between two technologies. One example is the BOOMcast, a 3D printed foot cast embedded with electronics. It not only behaves in a way that helps the wearer feel more comfortable, it can communicate how the patient is doing to their health professional. The real beauty of these oddly practical maker­driven ideas is that they highlight the importance of cross­over. IoT does not exist in a vacuum. Combining tech with other fields, and also other skills or areas of knowledge, lead to the discovery of undeserved niches. Plus, the result is always interesting.

Agriculture and IoT may be the route for those looking to combine business and helping the world. Earlier this year, AgTech raised $2.06 billion in funding. According to numbers on CrunchBase, Precision Agricultural Technologies have blown up this year, raising $400 million in the first half of 2015 alone. Compare that to the $276 million raised in all of 2014. Using IoT to gather data and make better decisions makes farmers’ lives easier, can cut expenses and, of course, create more food and better practices. With the world population growing, there will be push to update the farming system. While this area may not sky­rocket quite like logistics, it is a field with a lot of money, consumers and, most importantly, needs. Pairing connected tech with the right data capabilities might just be a golden ticket.

Still can’t find that niche? There are plenty of other areas to consider. The ever­ popular wearables, smart grids and smart homes are major fields that aren’t quite ready to go away. Much like security, cloud specialists will be a necessity in the future. Consider the quirky and strange Netflix Switch that hooks up to your house, and let’s you have the popular “Netflix and Chill” experience. It orders take out, dims lights, and the internet is going crazy for it. Not every IoT idea is a web of sensors and life­ altering programming. It’s creativity.

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image credit: meridican

The Internet of Things (IoT) has taken off and is slowly revolutionising the world we live in. Smartphones, smart cars, even smart fridges – all now boast connectivity designed to make our lives easier and more efficient. This is expanding to a citywide scale, with over one billion connected devices currently in use in smart cities around the world (a number expected to grow to ten billion by 2020). One of the key drivers of this revolution is the increasing use of identity management amongst these smart devices, allowing them to communicate directly with one another (as well as with people) for the first time. So, with this in mind, what areas of our cities are likely to benefit most from the IoT revolution?

Below are five core aspects of city living where the growth of identity-driven IoT and smart devices is likely to have the most significant impact on citizens’ lives in the near future:

1. Transport

The transport and travel sector will reap the benefits of the smart city initiative immensely. Gone will be the days of sitting in sluggish traffic, or tirelessly driving round and round the multi-story car park searching for a space. Connected smart traffic systems will be able to monitor and collect real-time traffic information, traffic volume/flow, speeds, and hazards. This data will then be sent directly to commuters (via their unique digital identities) to warn of delays on their usual routes and suggest better alternatives. This same data will also be used to pinpoint regular traffic trends and black spots, helping city planners to develop efficient plans for improved transport infrastructure in the future. This initiative has already started to roll out in the UK; for example, Manchester has introduced ‘talkative bus stops’ as part of its £10m Smart City plan.

Give a smart city an inch and it’ll take a mile. Smart parking measures can also be brought into play. Smart meters will monitor parking availability, notifying drivers of free space locations as soon as they enter the vicinity. Once parked, smart payment systems can time the duration of the stay, capping it as soon as the car is moving again. Charges incurred can then be automatically paid via a pre-registered account, removing the need to queue at payment machines or carry large amounts of change for parking meters.

2. Sanitation

Another benefit of utilising connected devices in the IoT is the improvement it can bring to overall city cleanliness and sanitation. Thinking with the outlook that no ‘thing’ is too big or too small to have its own digital identity, public bins fitted with smart sensors could be used to alert council refuse collectors when they are full and need emptying; an initiative that has already been introduced in Milton Keynes and Camden. A network of ‘smart bins’ would help to improve the efficiency of rubbish collection routes throughout the city, preventing build up and significantly improving hygiene as a result.

On a more personal level, strategically placed motion sensors linked to a smart meter could be used to alert homeowners to any pest/rodent infestations in their homes. If required, pest control specialists could be automatically contacted to deal with issues as soon as they arise, saving nasty shocks (and potentially costly repairs) further down the line, and preventing the infestation from spreading.

3. Energy Saving

Many of us have already installed a smart home hub that can automatically regulate temperature and/or be accessed remotely by homeowners. But why stop there? The same smart hub system can be deployed on a citywide scale, used to monitor much larger public spaces such as museums, office buildings, and shopping centres. Glasgow has introduced energy efficiency through the smart city initiative, by connecting the city’s energy grid to buildings with smart capabilities, in order to manage energy consumption. In addition to temperature monitoring, smart sensors can also be used to make significant energy savings in areas such as lighting or escalator use in public spaces by ensuring the systems are only activated when citizens are in the vicinity.

4. Emergency Response

We are already coming to see how identity-driven IoT and Smart Cities can help make our lives easier on a day-to-day basis, but could they be utilised to save lives? For instance, if a smart fire detector in a building picks up smoke, it can immediately send an alert to the nearest fire station, instigating a series of pre-planned emergency response measures. As the emergency services make their way to the incident, the collaboration with smart transport meters allows them to receive a real-time update on traffic, avoiding any congestion. Similarly, their unique vehicle identities can be tracked and traffic lights automatically changed as they approach to ease their journey to the incident site. Each stage of what could have been a complicated operation is made simple and efficient. This kind of initiative is already being introduced in the UK – for example, through ‘Uber for fire engines’, recently launched in London.

5. Overall Public Safety

Many of the above-mentioned technologies will help to make cities a safer place to live by protecting citizens from issues including acts of God, transport overcrowding, and poor sanitation. However, this is just the tip of the iceberg of what identity-powered IoT can do. The same smart monitors used to save energy in the home could also detect gas leaks, alerting key parties to the leak based on key variables such as time of day, location of the homeowner, and severity of the leak. If the leak is not severe and the homeowner is within a five-minute radius, emergency services would not need to be alerted as well. This level of automated situational analysis means emergency services would avoid unnecessary call outs and can remain available should another more serious situation develop elsewhere.

Similarly, other smart systems can be deployed in different ways to help improve overall public safety within a city. For instance, smart street lighting can be used to deter street crime by increasing lighting intensity and alerting authorities if significant/unusual movement is detected in the vicinity or suspicious noises are heard.

In making our cities smarter, not only do we make our lives easier and more efficient, but we also make them safer. As the IoT continues to adopt an increasingly identity-driven approach, a wealth of new opportunities is opening up that can do all of the above and so much more. For the citizens who live, work, and socialise in smart cities, there are exciting times ahead.

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Preliminary research on how the Internet of Things will impact education may lead you to believe students will soon be connected to an iPad, RFID scanning objects and getting their own personalized curriculum delivered to their desk. It’s a dreamy new world of individually tailored lessons. It might be prudent to remember how computers were supposed to completely alter the way students learn decades ago. Yet anyone who took a “computer 101” class in high school may know tech in the classroom is not the futuristic bonanza we want it to be.

Teaching is not an easy job. Only half of the work involves class time and, depending on where that classroom is located, teachers may have very different objectives. Of course they want students to learn, but a much larger education system and government often dictate the “how.”

Many of the daydreams for IoT in education involve students taking advantage of new technologies to complete cool new projects. Students in science classes might use RFID to tag sample specimens in the wild so they can take notes without leaving the classroom. Textbooks could be scanned to receive instant additional resources and assignments. Despite the fact the IoT is above all else about creativity, these common suggestions do not do it justice. When textbooks came with CDs of additional materials and assignments, who even used them? This is the dead-end of IoT in the classroom. Once the cool factor is gone, it isn’t so revolutionary.

Connectivity Must Be Used Creatively

The truth is, connectivity in schools is about far more than making lives “easier.” Microsoft boasts that their newest products can start-up 80% faster, saving teachers time. This is fabulous, but that in itself does not mean a better education. Real changes will come from fostering a better—not faster—learning climate. Thus far, better connected computers have mostly been making the work of teachers easier. Teachers are able to save time finding, connecting and implementing new resources thanks to their connected technologies. But that is only the beginning.

Employees at Bosch have done their part to improve the school “atmosphere.” Climate control and energy saving measures are some of the places the IoT will hopefully be affecting students. Bosch has taken an image of Einstein and turned him into a visual representation of climate. When the temperature or air changes, so does Einstein. The product was tested at the Bundesgymnasium Dornbirn grammar school in Austria with great results. Students were always aware of their Einstein. It helped notify them and teachers about minor shifts, enabling them to always create the ideal atmosphere and focus better. Students and teachers spend some eight hours in school every day, and these minor shifts in weather can drastically alter the mood. Furthermore, the Einstein actively taught students about climate, and even gave them a chance to get involved. It’s creative, highly interactive and practical designs like these that will prove useful over time.

What about the non-techies? Those who don’t identify with extreme interconnectivity? English teacher Robyn L. Howton told Education Week how she prefers to let students gain experience through doing rather than listening. Her classes begin with a brief introduction and then the students are given tasks. Using iPads, groups begin preparing and creating presentations to be shared with the class. Though this is a huge step forward, it is is hardly the sexy over-connectivity we have come to expect in the year 2015. The class researches social topics, most notably the Ferguson protests, or related subjects that spark their curiosity. The ability of technology to bring the outside world so quickly into the classroom is one of its greatest powers when used wisely. Howton’s classroom, however, should also be taken with a grain of salt:

“I decided my personal goal was to turn my classroom into a model so other teachers who want to start down this pathway have someone to come and [observe].”

Not every teacher is the same, and not every class can function like Howton’s. That’s why developers and teachers will have to get creative. With a combination of obvious heroes like Smartboards and Google docs with new, niche technologies like Bosch’s Eintein and nerdy kits that teach how to code or engineer, teachers and students will slowly have access to all kinds of learning tools.

Technology Is A Life Skill

Others want to take the IoT in the classroom to a much higher level. They want to focus not on utilizing technology, but teaching it. Students will be early adaptors to new technologies. This is one primary reason that eight UK schools are running an £800k pilot program to anchor education in connectivity. Such programs are about preparing for the future, and creating minds that can move through the complex IoT with ease. Surprisingly, much of their funding went straight into creating an appropriate cloud. Connected tech creates ample data, and it is vital that it be easy to share, store and access. The program is also deeply rooted in the desire to share knowledge and data between schools. What one class learns could be shared with other students. The exact usefulness of such data could be unlocked through proper data analysis, or simply creating new channels to communicate and socialize in schools.

The UK hopes to eventually roll this program out to far more schools, but are currently stuck on testing and cutting back costs—the latter being one of the most dangerous parts of these endeavors. Finding funds to bring in effective changes will be a hard sell in many countries where schools are already facing plenty of funding problems. Furthermore, the divide between rich and poor areas is not going to be an easy or happy discussion when it comes to IoT implementations. It may be easier to get IoT into schools in the form of cutting energy costs. Better automation and monitoring can save money, supplying real-world proof that the results outweigh the costs. When the “results” come in the intangible form of a student’s (often untestable) knowledge, it will be much harder for cash-strapped schools to find funding. There will be no way to make this shift easily.

The IoT Will Shape Education…Eventually

Successful integration of the Internet of Things into the education system will come slowly, and in very nuanced ways. Some schools may use it to save money or harness data; some will prepare students to be highly tech-literate; others will find creative uses for their specific needs. The dream of personalized, detailed instructions and seamlessly interactive technology will run head-to-head with the funding issues as well as current test-based accountability systems. Shifting the focus on education to include the IoT will mean a massive shift in understanding what education means and the companies that benefit will be the creative minds that can create practical, reasonable products the teachers, students and administrators can get behind.

The tech world is abuzz with how the Internet of Things (IoT) will change everything—usually for the better. It could be the dawn of incredible connectivity, speed and efficiency. While consumers need only concern themselves with what new gadgets and toys they can connect, manufacturers have a long road ahead. All of these changes don’t just mean cost-cutting and heightened visibility; they mean unparalleled complexity. Not only will forward-thinking companies thrive, those who can’t jump on quickly will be fighting against the current.

Success stories like General Electric highlight the difference between companies of the past, and the future. For over a century, GE was known for selling industrial hardwares and services. The name GE does not, at first, inspire thoughts of new software, cloud computing or other hot trending buzzwords. Rather than waiting for the future to render them obsolete, GE reinvented themselves using the industrial internet, and transformed their business by relying on sensors and the IoT. They went from one of the most “physical” entities imaginable, to a data-based company. As described in a white paper from GT Nexus, “every company will need to become, to some extent, a technology company.” Of course, utilizing IoT does not end at slapping RFID tags on some products. Companies of the future will have to completely redefine their business, and cut out any legacy thinking.

The Real Cost of Connectivity

With estimates from Cisco that, by 2020, there will be “more than 50 billion devices connected to the Internet,” only 17% of which will be computers, there is no doubt that the IoT will consume just about everything. Moreover, studies repeatedly show that one of the most important IoT fields will be in Logistics and the Supply Chain. In fact, the same Cisco study estimates the fields will make up $1.9 trillion at stake in IoT’s future. As technology develops and companies adapt, consumer demand for speed and customization will sky rocket. Manufacturers will be forced to operate at an exceptional level and be ready to change and modify operations in real-time, or risk falling behind. Rutgers’ Business School will even begin offering an accelerated certificate program specifically on the Supply Chain in a Digitalized Network later this year.

In pop culture, the IoT sounds like the final frontier, especially for businessmen. It offers superb optimization of processes, detailed tracking of goods and supply chain analysis, as well as transparency and new business models. Manufacturers can know the exact status of every piece, parcel and person; they can actively monitor assets and make quick, effective changes. Automated processes means lower costs, and incredible optimization—theoretically. However, it is not only paramount that companies fully embrace the changing climate, they must prepare for the shift beforehand. IoT will bring about at least two major shifts for supply chains and logistics. First, mass production will become a buyers’ market. Per-unit and specialized shipments will become normalized. Second, exceptional micro-logistics will become vital in order to achieve full speed, efficiency and consumer satisfaction.

The first point speaks to the fun, trendy IoT that gets even non-techies excited. Imagine a factory room covered in sensors, each speaking to each other; it’s the factory of the future. Each item is a specially created building block. They can monitor and track themselves. They even act autonomously, while simultaneously contributing to the whole. The atmosphere and system will be oriented around decentralized decision making. Each smart unit will be expected to make decisions—sounds a bit dangerous, doesn’t it? If not done properly, it would be a very precarious system.

To make things worse, “micro-logistics” really does refer to the smallest “micro” possible. Every speck in the chain must work properly. The lack of common standards and a frustrating skill gap means implementing these dreamy space-age solutions is not going to be normalized for some years to come. Finally, once all of these technologies are connected, the real battle begins. Lighting up dark assets, data that had never been collected, a realistic process must be in place to analyze it. Not all manufacturers are big data experts. Insights gleaned from smart forklifts, smart boxes and even smart lighting are not just useless but detrimental to a company that isn’t designed to deal with them.

A paper from Bosch’s Software Innovations division sheds some light on another aspect of the connected fleet and supply chain. Namely, how can vehicles all over the country—and world—be effectively connected? What if a truck drives through a particularly winding stretch of mountain road? Or, more generally, if there is any kind of disruption. Losing connection is simply not an option for these huge programs. Plus, connections must always be secure. IoT always includes questions of security, and companies will have to deal with this on a new scale. Unfortunately, companies can hardly build a private wide-spread infrastructure. Bosch suggests IoT carriers partner with network providers. This could enable all types of communication management, and perhaps lead to specialized tariffs and resources for manufacturers.

Lastly, presuming that companies can get a handle on the unwieldy new technologies, relationships between businesses are going to change. Currently, logistics is an incredibly fragmented industry. There are countless suppliers working at all kinds of speeds and standards. From the local to the international level, processes can be a bit of a mess. They are not the streamlined, connected operations necessary for good IoT-based logistics. Logistics is about networking and finding the best route as quickly as possible. This is only possible when companies are connected inside and out.

Finding Success within the Mega-trend

Despite the many hurdles, IoT will reinvent the supply chain and logistics. It has already begun. With major companies like Whirlpool and General Motors on the bandwagon, it’s clear there is no stopping the mega-trend from taking over. There are already success stories emerging, like GE. Start-ups and smaller companies seem to be taking a shine to the IoT technology must more quickly. From specializing in helping companies transition, to using IoT in small doses to up productivity, the trend is clearly profitable, and popular. The integration of the IoT into the supply chain will no doubt lead to many interesting stories, raising the question of who will adapt, and who will fall behind.

(image credit: CCO 1.0 , IoT )

Last year, London made numerous popular IoT additions to their public transit. Helsinki, Seoul and other major cities are employing the new technology in their own ways. Tackling concerns about safety and energy as well as the obvious desire for maximum efficiency, the Internet of Things is making its way on the streets in more ways than one. In fact, according to the GSM association, by 2025 there won’t be a single new car on the road that isn’t connected.

While driverless cars are the exciting buzzword, there are countless other real and tangible applications. Moreover, while it seems obvious that connectivity will make driving easier, safer, and more efficient, the most important (and fun) facts are in the details. So where might you actually be seeing change in your future?

Usage-Based Insurance

Insurance companies can be terrifying. Whether a wreck has occurred or was scarcely avoided, the subsequent long sigh of relief isn’t just for you and your car, but your wallet. Even with the kindest of agencies and plans, it’s hard to know what will happen to your rate—even if it wasn’t your fault.

By some estimates, 50% of drivers will have Usage-Based Insurance by 2020. UBI works by transmitting the policyholder’s actual driving data directly to the provider. Now, a company can see exactly how you drive, allowing you to become far more than a list of claims. While it seems few companies have found a way to properly market the new technology, once it catches on there will be no going back. UBI brings the wildly popular Pay-as-you-go scheme to the roads. Whether you want Pay-As-You-Drive (PAYD), Pay-How-You-Drive (PHYD), Pay-As-You-Go, or Distance-based insurance, companies will be able to offer better premiums that fit any customer’s specific needs. For those who can’t afford costly annual contracts, this could mean a world of change and saved money. Plus, the program incentivizes better, more efficient driving practices. Pay-as-you-go revolutionized phone contracts — why not cars?

[bctt tweet=”Pay-as-you-go revolutionized phone contracts — why not cars?”]

Micronavigation

For some, taking public transit is a nightmare. If there aren’t exact directions given, or a trip includes multiple stops and changes, a short ride quickly becomes a nightmare. Bad experiences and uncertainty can even lead would-be passengers to skip out on the public system entirely—opting for taxis, or another route filled with less variables.

Perhaps this is why every car seems to be plugged into GPS. Even a five-minute drive to the grocery store and the system tells you exactly which lane to drive in and where to turn. Helpful guidance systems are pivotal in a world where travel is unavoidable. Smart apps already exist for most major cities to help travelers maneuver transit systems. However, these are only a drop in the bucket. When there are twenty buses in front of you, and maps and labels all in a foreign language, how helpful are these apps? If cars, trains and buses are all connected—what’s stopping an app from telling users exactly which bus to take, or whether a user is actually on the correct bus?

Connectivity

If all cars in 2025 are going to be connected, what does that mean? It will, first and foremost, create more hotspots and get more people connected to the internet. In the push to connect entire cities and give internet access to all its citizens, connected cars are one of the most obvious means of gaining traction. Having connected cars opens doors for drivers, passengers, and even companies.
In recent years, cars have become a bit more akin to, well, spaceships. There are digital dashes, and it seems to need more software updates than a new smartphone. Worst of all, there is nothing more infuriating than lights on the dash signaling trouble, prompting a visit to the mechanic where it’s discovered all your car needs is a software update. Being connected could mean over-the-air (OTA) updates. When new information is released, an update is far less painful. With a record number of car recalls made last year—many due to software issues—it’s high time customers had easy access to important, and often necessary, updates.

By creating new hotspots, companies will also have access to a brand new set of data. This means better fleet management, and more efficient decision-making. Data gleaned from a car’s countless sensors can also yield interesting information. While driving patterns, vehicle conditions and similar data is not highly valuable to a driver, it can help companies and regulators pinpoint opportunities for innovation. Much of the focus of connected cars is on advertising—yes, advertisers will likely have a brand new way to learn about and market to customers in their cars; however, anyone from city planners to engineers can put untapped data like this to good use. It’s not only about marketing products better, but building better products.

And then, the VW Emissions scandal…

VW has been reeling since the reveal that they lied to U.S. regulators, actively used software to trick emissions tests and, of course, pumped some 1 million tons of unwanted pollutants in the air. While this raises many questions of security in the use of data and the IoT, it also illustrates the importance of interconnectivity. People are curious, and, quite often, skeptical. While the technology and ability to play with VW’s software is currently a bit tough to grasp, normalizing IoT in the automotive sector could lead to more people keeping tabs on their equipment. Creator of the Raspberry Pi computer, Eben Upton, explains that people are willing to experiment and see if what companies tell them is true. Rather than always placing faith in companies, the current culture is one to take the bull by the horns—as is made readily apparent by the popularity of OBD-II apps for consumers. With better connectivity and available IoT solutions, companies may have to look out for new watchdogs.

While shiny new IoT-based apps and add-ons help users parallel park and avoid overloaded cars, those are only the tip of the iceberg. With an area as volatile as IoT, it’s nearly impossible to dream of what innovators will create. Most importantly, the best disruptions will likely come from far more exciting fields than advertising, and dip into unexpected, seemingly unrelated areas. If designers do their jobs, users may not even notice how prevalent IoT is in their future cars and subway systems. Either way, there’s going to be a long, complicated future as the internet takes over the road.

Louisa Heinrich is the Founder of Superhuman Limited. She brings over 23 years’ total experience (18 in Digital), working with businesses and governments to design strategies, products, services and organisational structures that use digital technology to improve individual lives, make a positive contribution to society, and achieve commercial results.

She has held many titles, including Design Director in the first dotcom boom, Executive for Future Platforms at the BBC, and most recently Head of Strategy for international Service Design agency Fjord. She has led teams worldwide for multi-national businesses, is a thought leader and a recognised speaker on the intersection of people, technology and business.

We are proud to have her presenting at Data Natives 2015!

What led you to the creation of Superhuman?

I saw some trends and challenges emerging across the digital world that I didn’t feel could be properly tackled from inside the existing agency model, or from inside a single corporate – trends in the way human beings are considered (or not) in product development, the way new ideas are developed and monetized, the way success is measured, the way strategies are adapted and adjusted as circumstances, technology and people’s preferences change. I created Superhuman to be as nimble as possible, responding to changes in the landscape and helping businesses respond to those changes as they emerge, in whatever way makes the most sense for the business and the challenge at hand.

You’ve been a startup mentor at Wayra and Highway1 for some time now – what do you find yourself telling new founders over and over again?

That the customer needs to be embedded in every part of the company, not just Marketing – a lot of entrepreneurs still subscribe to the notion that you can make whatever you like and then marketing will determine whether it succeeds in the marketplace or not. In reality, the most successful products and services are built from the start around a need, desire or challenge that people have. Marketing can’t fix a poor experience, and a great customer experience is the best investment a young business can make.

Your time at the BBC must have been fascinating. How did you go about fostering innovation within a national broadcasting company of that size?

The BBC is actually full of brilliant people who have a genuine desire to be innovative, to push things forward, to make the world a better place. It’s remarkable and humbling and wonderful, and I’m still friends with many of the fine folk I worked with while I was there. Of course it’s also rife with politics, like any organization of that size, but finding and connecting with like-minded folk from across the divisions can go a long way. Ad-hoc and under-the-radar projects often end up feeding into much larger programmes of work – some of the threads we kicked off years ago are still running now, albeit in different forms. Innovation is a state of mind, not a team or a process. Making things happen is often just a matter of having the drive and the patience to find a way through. And having such a volume of amazing content to work with is an ongoing inspiration!

[bctt tweet=”‘Innovation is a state of mind, not a team or a process.’ – @customdeluxe”]

The Internet of Things is one of the most heavily hyped subjects in tech – what’s the truth behind the buzzword?

The truth is that, outside of some operational contexts (supply chain management, manufacturing and logistics, etc.), nobody yet knows what the Internet of Things is going to be. How could we? The idea that virtually any object could have the capacity to communicate – it’s both mind-boggling and strangely banal. On the one hand, do you really want or need your toaster to talk to you? On the other, how wonderful would it be to feel close to a loved one who’s thousands of miles away, without having to look at a screen? I think it will take some time and experimentation before we really understand what all this means.

Are there any IoT applications that particularly excite or impress you?

I’m really excited about the idea of using technology to facilitate human interaction, to enable us to be more present in the real world around us. For example, we talk a lot about ‘Smart Cities’ but at the moment, most projects that fall under that heading are focused on infrastructure. What about the people and the communities that exist inside those cities? Aren’t they the real heart of the city? I think IoT could facilitate an ‘Internet of Neighbourhoods’ where people come together to form more close-knit communities, assisted by connected objects and places. We’re starting so see some of these things happen with the Things Network in Amsterdam, and I’m hopeful we’ll see more and more.

I’m also really impressed with connected objects that are completely intuitive and, at the same time, highly flexible and adaptable. One of my favourite examples is the Good Night Lamp by Alex Deschamps-Sonsino. It’s a family of internet-connected lamps – a ‘parent’ lamp and one or more miniature ones. When the parent lamp is switched on, the babies turn on as well. It’s so simple that everyone gets it instantly, and yet it can be used to mean whatever its owners want it to – a hello between friends, a check-in with a family member, an invitation, anything. And most importantly, all you do to ‘set it up’ is take it out of the box and plug it in.

I guess the things I most admire in the IoT space are the ones that genuinely make things simpler or better for people – a lot of the projects I hear about might add automation or features, but also come with a lot of overhead in the form of control apps, settings panels and so forth.

What superpower do you think technology could give us next?

Ha! What a question. Technology has already given us lots of superpowers: we can fly (in planes and helicopters and flight simulators), we can see and hear things that are happening thousands of miles away in real-time (Periscope and other live-streaming tools), we can be in multiple places at once (telepresence and VR tech like Oculus Rift), we can control things with our thoughts (Emotiv, MUSE, MindWave, etc.), we can remember thousands of names and numbers (any mobile phone)… I suppose for me it’s more about how we apply technology than what it’s capable of. The same technologies that enable us to do all this magical stuff could also be used in some rather dark and dystopian ways. So I’m more interested in the how than in the what.

I would really, really like a teleporter though.

Which of the other talks at Data Natives are you looking forward to checking out?

Alex’s of course, and Suzy Moat’s talk looks interesting. I’m also curious as to what Dr. Belusa will have to say on nanotechnology and health.

Which companies individuals inspire you, and keep you motivated to achieve great things?

How much time have you got? It’s a long list. But to name a few: my partner and collaborators in Superhuman: Ayman, Alex and Simon. My brilliant hacker/artist friends like Shardcore and Henry Cooke. My family and friends outside the tech world, who challenge me and call me on my bullsh*t, and who also happen to be the people we’re actually making all this stuff for – especially my godchildren, who will have to deal with the consequences. All the people I know who’ve sacrificed personal comfort – physical or mental – to do important and meaningful things. People who are unafraid to try something new, take a risk, speak their mind. My favourite poet, e.e. cummings, my favourite physicist, Niels Bohr, my favourite anthropologist, Joseph Campbell, and many many more. I tend to be inspired by people rather than companies. It’s people who make the companies go anyway, right?

(image credit: GX Software)

Data Natives is coming soon! The conference focuses on three key areas of innovation: Big Data, IoT and FinTech. The intersection of these fields is home to the most exciting technology innovation happening today.

Whether it’s for individual consumers or multi-billion dollar industries, the opportunity is immense. Come and learn more from leading scientists, founders, analysts, investors and economists.

Early bird tickets are still available – don’t miss out on a great opportunity for yourself, your colleagues or your employees! If you need a little more convincing:

Data Natives has 7 Key Goals

We will leave you:

1 – Inspired for the future

Data Natives focuses on real-world applications of cutting edge technology to paint an exciting picture of the future. We aim to help attendees understand either the potential impact of the technology they are creating as technical professionals, or the scale of the opportunity that technology is providing for the rest of us.

When technological advancements are shaping the way we work in fundamental ways, it’s important to stay inspired, optimistic, and open-minded about finding more effective ways to operate.

2 – Empowered to seek business opportunities

This conference is a chance to look at some of the most innovative applications of technology, and the business value they have unlocked. Whether it’s hearing how an industry giant leveraged data to increase their margins, or how a scrappy startup is using connected devices to offer something revolutionary – you will leave the conference looking at problems in new ways, inspired to solve problems that previously you were resigned to simply coping with.

There will be plenty of opportunities to network with companies offering – or looking for – innovative solutions, or experts who could help add value to your business.

3 – Encouraged to be data-driven

As more and more data is available to us, an analytical mindset is a more and more valuable mindset. Whether you’re in marketing, HR, product design, project management or a technical field, chances are you work with data every day. Learning to ask the right questions and how to interpret data correctly is key to success.

By looking at how industry leading companies are using data in all aspects of their business, you will come away from Data Natives with a clear picture of how your companies processes may be improved. There will also be a workshop available from Alistair Croll on Lean Analytics.

4 – Learning from other industries

It is easy to exist within a bubble, networking and knowledge sharing within your own industry, when an exciting and crucial development may be happing in an adjacent industry. Don’t miss the chance to broaden your mind.

Our speakers will describe the transformation of major verticals, as well as the horizontal applications of data-driven technology. Whether your background is advertising, e-commerce, finance, transportation… There are valuable lessons to be learned from how other industries are finding value in their data, utilizing connected devices, or managing their finances.

5 – Experienced with hands-on workshops and expert guidance

In additional to a packed schedule of insightful talks from 50 guest speakers, we will also be hosting four workshop sessions with experts, covering key areas of interest and giving our audience a chance to get hands-on with the subjects that matter to you.

See the full schedule for details on those workshops as they are announced!

6 – Enjoying the ultimate curation of data driven content

The schedule of Data Natives has not been pulled out of thin-air – it comes from the lessons learned from >1,800 articles, and >100 past presentations at our events all over Europe.

Not just a standard, boring industry conference, it is a curation of the most valuable and interesting content we have found over the last two years. Many of our speakers come from long standing relationships and past collaborations, with talks that have been applied and refined over multiple events.

7 – Absorbing diverse input and fresh perspectives

This is not a conference to come and hear the same talking heads saying the same things you’ve been reading about online for months already.

We are hosting talks from data scientists, investors, analysts, consultants, academics and economists. It is our aim to provide a complete view of the technology innovations and opportunities. Inspiring, informative and accessible. Most of all, useful.

See the schedule (so far) and get your tickets here:
datanatives.io

(image credit: Christian Scholz, CC2.0)

Named #2 in the Top 100 Internet of Things Thought Leaders, Alexandra Deschamps-Sonsino brings a wealth of experience in building consumer-facing internet of things products, such as the Good Night Lamp and helping clients such as BBC R&D, Nokia, British Gas, EDF and British Telecom. She’s altogether an interaction & product designer, entrepreneur, speaker & curator focused on the limitless potential of IoT.

What makes you a ‘data native’?

Well I manage 21 Twitter accounts, so that’s got to count for something 🙂

Can you describe the journey that led to where you are today, and how your interest in IoT developed?

I’m an unlikely data native I think as I studied industrial design a pretty data-free field in the early 2000s. I went on to do a masters degree in interaction design where my peers were developers, computer scientists and UX people. That’s when my understanding and approach to the web developed. I understood through learning HMTL, CSS and PHP what my peers needed to hear when I wanted to make a web-enabled physical product. I started Tinker (tinkerlondon.com) (the first UK distributor of the Arduino) in London in 2007 and that was the beginning of getting involved in growing a maker movement in the UK around that tool. We worked with some fantastic clients who were also interested in understanding how those types of tools could help them experiment inside their business.

When I closed the studio in late 2010 I went back to consulting and work now more on a strategic basis with clients. I started curating the London Internet of Things meetup in 2011 for Pachube and it’s now the largest global community dedicated to the topic (6K+ members now). Over the last 3 years, I have developed the Good Night Lamp (goodnightlamp.com) a connected product for global families. This is an idea I came up with in 2005 during my masters degree. I work with a wood fabrication studio in London (Tom Cecil studio) and an M2M company outside of London (Eseye) to deliver the product to people all over the world.

What major technology milestones have stood out to you during your time in the industry?

I don’t think there have been any radically new technologies that have evolved recently (although LoRaWAN networks sound interesting but still tricky) but the audience for existing technologies have changed and the application space has changed too. Mobile phones have a lot to answer for when it comes to the development of the internet of things and you could argue that it’s slowed down the pace of development of some ideas because it’s easier to make an app than to develop a product. But product still have a big role in the lives of consumers otherwise we’d all be living in empty white boxes. The price of hardware components has dropped and there is enough information online to allow pretty much anyone to experiment with a on off prototype of a physical device that does something interesting to them.

[bctt tweet=”‘Mobile phones have a lot to answer for’ – @iotwatch #IoT”]

Crowd-funding means that you also don’t have to be subject to a very conservative investment landscape but can find and sell to a market more easily. These are all subtle changes in who has had access to particular technologies, not new in themselves.

What are you waiting (or hoping) for to happen next in terms of technology development for IoT?

I think I’d like to see more stable connectivity offerings from the M2M sector as they learn how to collaborate with whitespace offerings. We’ve struggled a lot with the Good Night Lamp to find global coverage. In Canada for example, I can’t send my parents a set of lamps because the 2G coverage is poor there. Connecting things is still quite tricky when you don’t have local wifi to rely on.

Of the projects you’ve worked on at Designswarm so far, of which are you most proud?

I still love Homesense – a bottom-up smart home project we ran at Tinker in 2009. We gave 6 households across Europe a toolkit that was Arduino based and matched them with a local developer. Each home developed their own application based on their own needs and the report is still online. The kit ended up in the New York Museum of Modern Art as part of their permanent collection. The whole project was built under creative commons as we wanted to treat is as open research. I still think the level of discourse around smart homes needs more reality and appreciation of the granularity of everyone’s home.

Are there any industries or sectors you see as ripe for IoT applications, or projects you would particularly love to tackle?

I’m currently working with Wintec Innovate, a research institute in New Zealand and they’re interested in technology-led rural innovation. I think there’s a lot to do away from cities in helping people connect to jobs, healthcare services and the source of their food, i.e. agriculture.

What connected devices do you use on a day-to-day basis, and why?

I have a Hive Home, a connected thermostat. It’s great as I can turn the heating on in my apartment 20 minutes before I get there and come home to a warm house!

Which companies individuals inspire you, and keep you motivated to achieve great things?

Even if they are now closed, the work of my friends at Berg was sensational. They really explored what physical interactions within a digital landscape meant and their work continues to inspire me.

Smart watches are trendy. Smart thermostats are popular. But what else can you do with IoT technology? Whether you’re a tech­lover, lazy, or like trying new things, smart gear is able to do a lot more than track your calorie goals.

The Practical

Goji: Home security is important, and we are tired of the whole lock­and­key routine. There has to be something better than just heavy metal bolts. Smart locks like Goji let you use your phone as a key, whether you’re at the door or across town. Here’s why Goji is special: it can send you picture alerts. Goji will automatically take a picture of visitors and send them to your phone. Second, you can grant access to anyone with a supported smartphone just by selecting a date and time with the Goji app.

BodyGuardian: You know FitBit, Nike FuelBand and the dozens of other fitness trackers… but there is a whole other equally important use for smart watches: actual health monitoring. BodyGuardian was approved earlier this year by the FDA and monitors vital signs like heart rate and EKG rhythms. Small, wireless and Bluetooth­compatible, the BodyGuardian syncs up with Samsung tablets to share up­to­date information. This means not only better monitoring, but the ability to respond to signs of cardiac arrest, and to better analyze a person’s physical health and state. We also love the Tempo watch, which acts as a “rhythm journal” so you can keep a close watch on loved ones.

Drop ­Kitchen Scale: Baking is fun, right? What about those of us that don’t really like measuring, being precise, and are generally bad at following directions? Drop makes sure you don’t screw up your recipes with improper measuring. The scale automatically weighs itself as your pour in each ingredient. Connected with the Drop app, it let’s you know when you are ready to move on to the next ingredient.

The Fun

WeMo Smart CrockPot: WeMo has been making oodles of fun smart appliances, and this is just the best. As if using a crock pot wasn’t easy enough, it just got so much better. If you’re away, you can start your slow cooker, turn the temperature up, down, or whatever you might need. Who doesn’t want to come home to the aroma of freshly cooked dinner?

Parrot Pot: This flower pot is equipped with a water­ sensor to automatically water your plants when they’re thirsty. With a 2.2L reservoir, it can take care of plants for up to a month, giving you peace of mind and happy plants. The machine can even switch to conservation mode when there is little water left. With a database of several thousand plants, and sensors to observe light and heat, it definitely knows more about how to take care of your plants than you do.

Belkin WeMo: Currently billed as the crowd ­favorite smart­-plug, it can replace a host of other costly “smart” items simply by plugging into normal machines. Rather than investing some hundred dollars on a coffee machine specifically because it’s “smart,” why not buy a smart plug and attach it to your favorite coffee maker? You can then use the WeMo app from your android or iPhone to track your energy usage, or brew a cup of coffee. It’s even Amazon Echo compatible.

SmartMat: Yoga has become increasingly widespread and popular these days, leaving a trail of both enthusiasm and injuries in its wake. Improper Practice doesn’t just mean you’re ineffective, it can mean real damage for your body. The SmartMat is a yoga mat equipped with pressure sensors to sense not just which pose you are doing, but how your weight is distributed. These sensors hook up to an app that can give you the full details on your movements. It can also lead you through a full class, complete with real­time feedback, or go into silent mode, tracking your moves for you to analyze later.

The Brand Spanking New

Heddoko Smartwear: Want to work out properly? Heddoko clothing gives you 3D visuals of your movements. Real­time instruction and feedback as well as movement analysis mean this could be incredibly useful for athletes, coaches, and physical therapists everywhere. Hook it up to the Heddoko app and see how your body moves. Heddoko is still in testing but, golly, we cannot wait to try it out.

Mycroft: There are oodles of smart home devices. There’s the Apple HomeKit and Ivee (both of which suffer from less than stellar reviews), as well as the popular Amazon Echo. Automating your entire home is, perhaps, not something we are totally prepared for. It’s a work in progress.

Unfortunately, hubs like Apple’s HomeKit are going to geared towards Apple products, means lots of complications down the road. So, what’s so exciting about Mycroft? It’s open source. When it comes to new technology, you do not want to be trapped into using apps and tech from only one company. Combining Arduino and Raspberry Pi, who could ask for more?

Vinli: This little box transforms any car into a 4G LTE­connected smart(er) machine. While plenty of apps exist to connect your phone to your car, this cuts the phone out of the equation and hooks directly to your car. By plugging into the OBD­II (Onboard vehicle diagnostic), the Vinli can track just about anything and is limited only by the apps developers can dream up. With more and more phone companies investing in Vinli and jumping on board, we are keeping a close on eye on this baby.

It’s easy to get wrapped up in the wearables ­craze, or think that IoT is only good for giving us more environmentally helpful light bulbs, and smart washing machines. IoT is changing the way we think about, well, everything. From health, to hobbies, to money and the environment, interconnected tech provides real, practical opportunities. Besides, how can we not get excited about the adorable open source Mycroft, or the possibility of the Drop scale saving us from baking disasters?

More than 30 firms including Intel, BT and Vodafone recently announced they would band together to create an industry body to vet internet connected devices for security flaws. You would be forgiven for missing this news, on paper it’s not terribly exciting. However, it has important implications for the Internet of Things (IoT) and will impact consumers and businesses.

The IoT is still in its infancy. It is missing the killer device that will spur widespread adoption and kick it into the consumer mainstream. While we wait for ‘the iPhone of smart devices’, there is a great opportunity to standardise how the IoT works.

Standardisation is crucial because of just how many devices the IoT could be made up of. With the potential for any object to be made ‘smart’ there could be an unprecedented number of collectors and transmitters of personal data. The fact that these devices need to talk to each other also means that data could be shared and used by a huge number of different companies.

Thankfully, some of the companies that are set to play a major role in the IoT have recognised this opportunity and decided to act. By seeking to create a minimum standard for security on IoT devices, this industry body will help to safeguard people’s data and create a level playing field for technology companies.

However, seeking to identify and weed out devices with inferior safety provisions is only one piece of the puzzle. Although it should help to defend against hackers, it won’t address the looming problem of how companies will collect, use and inform consumers about their personal data. For the IoT to gain widespread appeal and have longevity, users need to be able to trust smart devices and the companies that make them. Without minimum ethical standards or codes of conduct to govern the IoT there is a real risk that personal data will be misused. If this happens it will cause a consumer backlash that will threaten the whole industry, or provoke government regulation that could hamper innovation.

A minimum ethical standard for the use of data should not be a difficult document to create. Many of the companies involved in the industry body will already have their own standards in relation to how they use data on other devices. Harmonising these rules should be a no-brainer.

Consumers need to know what data is being collected on them and how it is used. Therefore, alongside security, transparency needs to be the foundation of the IoT. After all, it is everyone’s interest that the companies involved in the IoT act responsibly with personal data.

Standardisation can also extend beyond security provisions. The format and network these devices use should also be homogenised where possible. Consider the recent history of new technological devices. There have been reoccurring format wars, such as Betamax and VHS, Minidisk and MP3, and in the past few years, HD DVD and Blu-ray. As with most wars there were losers, casualties and a lot of money wasted. On one hand, there were the manufacturers and suppliers that threw their lot in on the wrong side. For some that was the end of the road, for others it precipitated a painful pivot.

Those who won had to incur plenty of needless costs in marketing and lobbying distributors. On the other hand, there was the annoyed consumer who forked out a lot of cash on devices and their favourite songs or movies in the right format only to find out they had to spend it all over again.

Cooperation between the businesses that operate in the IoT will help the sector develop faster, save money, support innovation and protect consumers. The formation of a group to address security is a welcome first step. However, we need to realise the ‘Internet of Things’ is really the ‘Internet of People’ and respecting the privacy of individuals is essential.

(image credit: Ervins Strauhmanis, CC2.0)

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Energy is a huge conversation and big data is already being used to make incredible changes. It’s leveraged to create better oil and gas practices, keep better tabs on expenditures, and even ramp up the renewable energy movement. With not just one, but countless companies integrating big data practices, the entire nature of energy and energy consumption may change in surprising ways. Big data is even being called to help combat climate change. The right data could not only raise awareness but lead to real ­world results. But let’s start a little smaller, with something more tangible: the smart meter.

Smart meters are electronic devices that record energy consumption on a usually hourly basis. Compared to usual methods where energy is gauged quarterly or even annually, smart meters keep consumers and providers up­to­date with accurate, relevant information. They help businesses and households understand their energy consumption as well as how to decrease it. Once consumers see how much energy they are wasting and where, there is no doubt many of them will cut back. Whether for financial or environmental reasons, most of us don’t particularly enjoy wasting energy.

There are five components to energy consumption: water, air, gas, electricity and steam. Energy analytic software records and analyzes the exact expenditures, breaking consumption down into bite­size pieces. Data technology illuminates that exact point where waste is occurring. For the consumer level, there is (or was) Apple’s smart meter “Nest.” The meter learns your patterns and adjusts energy automatically. For companies, there’s intelligence provider FirstFuel. According to CEO Swapnil Shah “We don’t just say you need more energy­efficient lights. We can tell the building manager he needs to replace five lights on the 14 technologies are often more accurate than an on­site audit, and might leave you baffled by their results. Such analyses helped the GSA discover two large exhaust fans running unnecessarily. The realization led to the fans being reset, and saved about $800,000 in just one year. Strangely enough, Microsoft also used data technology to uncover rogue fans wasting some $60,000.

If it’s so easy and wonderful, why don’t we all have Smart meters?

GE’S chief development officer Paul Rogers noted that such machines could eliminate up to $150 billion in waste, and yet most of us are still doing things the old ­fashioned way.

One huge hang­up about integrating big data and energy consumption is that it is, quite simply, complicated. It’s hard to understand; it’s hard to collect. The last thing a busy person wants to hear is “let me explain to you how big data can save you money.” Trying to shove more information down a consumer’s throat is almost always going to end badly. This is why companies properly utilizing big data are necessary to help those same consumers understand and overcome these hang­-ups.

[bctt tweet=”Let me explain to you how #BigData can save you money.”]

The Laissez­faire approach

Companies like IBM have already gone ahead and wrangled in big data to save big money. The IBM Insights Foundation for Energy isn’t just helping other companies, it represents IBM’s own, very real conservation measures. Again, whether it’s cutting down on expenditures for the environment or your own bottom dollar, the result is the same. IBM saved $43 billion in one year just by cutting back on energy consumption. This cause came about not because of political or social pressure, but because it made sense financially It seems that everyone wins when big data meets energy.

Reaffirming the idea going green equals more green, the manager of Industrial Cyber Security at GE explained just how many billions of dollars some of the world’s largest energy users could save by being more efficient. He adds that data gleaned from connected machines could lead to over $60 billion in savings in the gas power sector alone. It’s clear that big data can help clean up a messy industry by cutting back on wasting precious fuels. But up next, the future.

Big Data and sustainable energy

Building energy efficient cities is a huge field of study that has yielded tangible results. Studies indicate that two­thirds of the population will be living in cities by 2050. This doesn’t simply mean waving goodbye to suburbia, it means facing a host of new problems. How can we accommodate all of those automobiles? What does that mean for power plants and distribution?

Study after study has indicated that big data will be integral to keeping the city of the future running smoothly. Many are suggesting that cities do away with the “ship in goods, ship out waste” paradigm. Hence, the smart grid.

Though it truly sounds like a crazy hyper­-connected sci­-fi world, the smart grid relies on interconnected smart meters, appliances and renewable energy that works as a unit. It can identify faulty parts, and let’s providers predict where energy is most needed or wasted.

The only thing missing in our city of the future is optimized renewable energy. This doesn’t just mean strapping solar panels on every side of an apartment building. Renewable resources are notoriously finicky. They seem unpredictable and intermittent. Only by properly collecting and analyzing data can the potential of these resources be fully harnessed. Hard up­-to-date evidence is necessary for understanding and properly managing these well­ intentioned endeavors.

IBM, once again, proves to be a great example. In 2011 they mixed business with accidental environmental friendliness. Teaming up with energy company Vestas Wind System, IBM used their big data skills to pinpoint exactly where wind turbines would function most efficiently. Making better business decisions meant that Vestas could simultaneously accelerate their own growth while addressing some of the world’s biggest problems. Now, consulting big data before installation, or even visiting the installation site has become widespread.

Perhaps the biggest indicator of how big data will impact our future relationship with energy can be seen in the way global companies have already been utilizing it for years. With names like Microsoft and GE already on the bandwagon, it’s a matter of time until we get to experience it first hand.

The leading car manufacturers are currently investing huge amounts of money integrating Big Data knowhow into their manufacturing processes. In the future, it will also undoubtedly have a direct impact on the driving experience – and before long your car will drive itself.

If your in-car computer tells you that your engine is 70% likely to fail, most people would want to do something about it. If it tells you that your braking distance is 85% likely to cause a rear-end collision, you might drive with a little more caution…. Data has the power to change behaviours – more than education or prohibition ever could.

[bctt tweet=”Data has the power to change behaviours – more than education or prohibition ever could.”]

This emerging trend in Big Data will be driven by the businesses who will stand to profit from it (they will sell more cars, parts, etc), but it will soon move across to other areas of our lives, and it may not be universally welcome.

If, after a detailed examination of your marriage, you were told that you had a 95% chance of getting a divorce over the next five years, would you feel any less determination to work at it? When emotions are turned into cold and hard facts, they are much harder to ignore. “We’ll get through it, we’ll be fine” might be so much harder to believe in. There are lots of things in life that aren’t just about the “data”, but they will be analysed anyway.

On the other hand, within the healthcare sphere, preventative predictions will cause a revolution in the way we understand out bodies and the affects of what we are doing to them. If you understand that not going on that morning run has a 75% chance of shortening your life by three years, most of us would rush to put on our trainers…. If you understand that a stroke was imminent, you could rush to a hospital and get yourself treated before it took hold.

From a recruitment perspective, what if your employer could tap into your online habits to tell when you might start to look for a new job. The moment you start to browse certain websites is the beginning of the end. What may have been a flirtation with temptation previously might now be viewed as a heinous betrayal. “How dare you check out their career page. You’re obviously not fully engaged anymore. Goodbye.” Sounds silly, but a variation on this may not be so far away.

With the advent of the “Internet of Things,” this predictive analysis could turn us into little robots, governed by algorithms and not by our hearts. There could be a Google Glass type headset to tells us how we should be interacting with others based on their reactions. It could analyse all the tiny “micro-expressions” that our intuition takes for granted and give us hints as to how we should be behaving…. This for me would be the beginning of the end!

Preventative predictions will form a big part of the Big Data revolution, but we should be careful not to let their conclusions get out of hand.

(Image credit: Teradata)

Enterprise Hadoop provider Hortonworks announced it has signed a definitive agreement to acquire Onyara, the creator of and key contributor to Apache NiFi, a top-level open source project. The acquisition aims to provide customers automated and secure data flow technology. As a result of the acquisition, Hortonworks is introducing Hortonworks DataFlow powered by Apache NiFi as a compliment to their Hortonworks Data Platform.

A new data paradigm that includes data from machines, sensors, geo-location devices, social feeds, clickstreams, server logs and more is fueling the Internet of Things (IoT) and driving the need for trusted insights from data at the very edge to the data lake in real-time with full fidelity. Many IoT applications need two way connections and security from the edge to the datacenter. This results in a “jagged edge” that increases the need for security but also data protection, governance and provenance. These applications also need access to both data in-motion and data at-rest.

“Hortonworks is focused on doing everything possible to enable our customers to transform their business through data-driven insights and actions,” said Rob Bearden, chief executive officer at Hortonworks. “Onyara’s impressive work on security and simplicity in NiFi, combined with their commitment to open source makes for a perfect addition to our technology team.”

Apache NiFi was made available through the NSA Technology Transfer Program in the fall of 2014. Over the past eight years, Onyara’s engineers were the key contributors to the U.S. government software project that evolved into Apache NiFi. In July 2015, NiFi became a Top-Level Project, signifying that its community and technology have been successfully governed under the Apache Software Foundation.

The earliest adopters of the Apple Watch are now in possession of the much-hyped wearable, but behind the scenes, companies are still scrambling to be a part of the action. The Apple Watch is expected to do well in sales this year, and it’s sparked an arms race among brands to get their name attached to the latest technology. That being said, the real usefulness of apps on such a small screen tethered to a wrist remains to be seen. An app’s success on Apple Watch depends on its target audience, use cases, and other factors. Not all are a practical fit.

We looked at a slew of FinTech apps currently or soon-to-be available on the Apple Watch, categorized them and rated each category on usefulness. Should you take or leave that budget tracking app? Read on to find out.

Stock alerts

Scutify, NewsHedge, E*TRADE, PortfolioWatch, Call Levels, Charles Schwab, Fidelity

Verdict: Take it

Daniel Chia, co-founder of Call Levels, believes the key to an app’s success on the Apple Watch lies in “properly providing push notifications on the watch to add value to users.” We tend to agree with him. Consider the reasons why you’d want to look at your small watch screen instead of your phone. Important alerts are the obvious answer. Call Levels also claims to be the one portfolio app that’s perfectly suited for the Apple Watch form factor.

If you’re loyal to one of the giants like E*TRADE (pictured below), Charles Schwab and Fidelity, their apps may be worth checking out – however, make note of these others: Scutify adds a social networking emphasis and has amassed a loyal following, calling itself “The #1 Financial Social Network.” NewsHedge is known for its audio broadcasts. PortfolioWatch comes with a fee for personalized portfolio monitoring, but it also allows you to manage your holdings, view charts, and check individual stock performance.

Trading stocks

IG Group Holdings

Verdict: Leave it

Buy, sell, and trade stocks on that tiny little screen? Where’s the fun in that? Admit it – you’d probably be holding your iPhone in your other hand, anyways.

Tracking spending

Unspent, MoneyWiz 2, Pennies, Mint

Verdict: Leave it

While all four of these apps have snazzy interfaces, that won’t matter much when you get annoyed from tapping your wrist all day long.

Take a look at the Pennies introduction video and you’ll see a young woman walking through life counting her spending. Yikes. Who wants to input all that data instead of looking up and out at life? If staring at numbers all day long on a 42mm screen floats your boat, fine – just don’t blame yourself when you miss that love connection because you were too busy inputting your toilet paper expense.

Multi-purpose banking

Citi Mobile Lite, DAB Bank, iBank, BankMobile

Verdict: Leave it

Again, this seems like a bit of a no-brainer. You’ll probably have an easier time with your account balances – and everything else related to banking – on your phone.

The Citi Mobile Lite app will allow Citibank customers to check account balances and view recent credit card transactions. The iBank app (pictured below) will feature account balances, however many of iBank’s current users on other devices are concerned and commenting that existing bugs in the company’s apps won’t be fixed. If BankMobile’s app is anywhere near as counterintuitive as its website, it won’t be a worthy addition to your wrist.

Account security

BillGuard

Verdict: Take it

In an ideal world we’ll know about fraud before it even happens, but until then, Apple Watch alerts will suffice. Bonus points to BillGuard or other security apps if they can get international coverage up and running ASAP.

Paying bills

Prism, Chronicle

Verdict: Take it or leave it

If you’re the kind of person who needs a nag or two about paying bills, then the bill pay apps for Apple Watch will probably serve you well. But for actually making the payment, a phone still seems like the better option.

Both Chronicle (left) and Prism (right) seem to be very similar in terms of features, however the interfaces are quite different.

Credit card rewards