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View accompanying graphics/visuals of dashboards here.

Insurance companies that find a partner, which analyzes, understands, and helps them to take advantage of IoT-based technologies, can reduce costs, paving the way for lower premiums and increased customer loyalty.

Many types of insurers record an unnecessary number of claims. By adding IoT sensors, real-time data can be attained and custom alerting can be configured to prevent claims from occurring. In addition, robust predictive models on historical sensor data can predict claims based on geography, even by a customer.

Let’s look at a situation where a company contracts out a service provider to monitor and maintain the health of four cold storage warehouses in South Florida. Each warehouse has many refrigerated rooms storing food, and each room is equipped with three sensors: one temperature, one humidity, and one open/closed sensor for the entry door.

By employing an app to use WebSockets, monitoring can be performed through dashboards, where individual data elements are refreshed without requiring any user interaction. Think of this view as a live status screen in the service provider’s operations center.

The system includes dashboards to monitor, alert, and proactively prevent claims. The warehouses and cold storage rooms dashboard provides a status summary for each room including how recently the sensors last updated. Room sensor data shows the graphical views of current and historical sensor data. Dials show some predefined thresholds for green, orange, and red alerts along with the current value. Then, line graphs show trends over the past few minutes, hours, and days.

IoT sensors catch anomalies and prevent claims before they happen. Because the refrigerated units are storing food, there are multiple scenarios that might result in a food spoilage claim. One might be if the temperature in the room crosses the threshold into orange alert and remains there for more than two hours. Another might be if the temperature ever crosses the threshold into red.

Alert notifications can be configured so that the policyholder gets notified via push notification, SMS and/or email when the temperature crosses the threshold into orange. Perhaps the door was left open, which can likely be resolved. However, if the temperature remains orange for more than 15 minutes, a message or work order is pushed to the service provider queue, which dispatches someone to investigate and proactively resolve the issue.

In addition, the system includes dashboards that insurance companies can leverage to generate business value. These predictive scenario dashboards slice the sensor data in various ways.

The manufacturer performance screen helps insurance companies determine the most effective sensor manufacturer, saving time and money from defective sensors. Another dashboard helps identify events and incidents, such as anomalies between the two sensors.

Another screen shows customer segmentation by sensor data. This helps insurance companies enable premium discounts based on lower claim probability. The historical loss ratio and claims analysis dashboard provides loss ratio by location and number of claims by location.

Additionally, claim prediction insights are provided. Using historical data, predictive models are created to predict the number of claims, total amount of those predicted claims, and number of claims avoided.

By incorporating IoT sensors into a telematics solution, policyholders can catch anomalies and proactively prevent claims. Insurance companies can leverage predictive scenario dashboards and predictive models to avoid claims, reduce costs, lower premiums, and therefore increase customer loyalty.

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Have We Already Bored of Predicting IoT?

If you have read my post “Will finally be 2017, the year of Internet of Things? I do not think so.” you will have confirmed there were some analysts and companies who guessed and others who did not hit the bullseye.

As usual, numerous predictions about the Internet of Things (IoT) appear at the end of the year, some with foundation, others by interests and others by opportunism. Although I notice a certain fatigue this year perhaps due to the appearance of other cooler technologies or very likely to the lack of success and few differences from previous predictions. 

It may also be the last time I write an IoT prediction article.

Let's start by reviewing some of the 2017 predictions.

Successes and failures of IoT 2017 predictions

Sorry Morgan Stanley but 2017 has not been The Year Of Internet Of Things however is true that there is less hype around IoT.

Yes Forrester, we continue worried that there will be a large-scale IoT security breach.

As not many large IoT projects in 2017, the role of System Integrators has not been as important as IDC predicted.

Have you seen, Analysys Mason, key developments in LPWA technologies, connected cars and smart cities?

Who now, MachNation if Internet of Things platform revenue grow 116% in 2017. There are only financial numbers but we all agree with Sandhill that still many doubts how “Choose your platform.

It is true Forbes “The Internet of Things (IoT) is still a popular buzzword, but adoption will continue to be slow.” 

I have to say that Judith Hurwitz and Associates, were right that the growth will be in industrial sector rather than the consumer sector.

Hard to fail if you consider what Moor Insights & Strategy predicted: IoT is still in its infancy in terms of dollars and deployments, and that can’t last much longer, before market frustration sets in

Brave, ADLINK and FreeWave Technologies, Inc predicted that Edge computing will become a mainstream term for IIoT. 

Internet of Things Institute - “Recruiting Will Remain a Challenge for Organizations with IoT Initiatives” and sorry Teradata not many companies looking for Internet of Things architect role.

Tier-1 operators in the US and Europe happy with Northstream because IoT revenues contributing up to 3% of total revenue in 2017. 

Telefonica IoT and Cisco Jasper trusted that LPWA expansion to harness the growing IoT.

What will be of IoT in 2018?

According with Ericsson, in 2018, mobile phones are expected to be surpassed in numbers by IoT devices.

It seems that 2018 will be the year when AI and IoT will converge. But it will also be the year in which the CIOs will be busy integrating device management into overall IT infrastructure in a way that doesn’t overwhelm the organization. This is where the adoption of application robots, natural language processing (NLP) and AI automation of processes will come into their own, offering intelligent management of IoT deployments cheaply and efficiently. 

However, 2018 will not be the year of Blockchain and the IoT, because although Blockchain-based IoT adoption rises to 5%, Blockchain is not yet ready for large scale deployments requiring reliability, stability and seamless integration with existing technology infrastructure. But promising pilot projects are beginning to emerge and the maturation of IoT and blockchain technologies and products will drive blockchain adoption in 2018.

To reinforce the ongoing investment across the industry Gartner’s Strategic Trends for 2018 back up the focus on IoT with Intelligent Things, Digital Twins and Cloud to the Edge all making the list for the coming year. 

On the other hand, Forrester affirms that finally 2018 will be the year in which the Internet of Things moves from "experimentation to business scale". Forrester also predicts that IoT platform offerings will begin to specialize in “design” and “operate” scenarios.

Punctual to his annual appointment, IDC makes its Worldwide IoT 2018 Predictions. 

One more year, Citrix leading thinkers also share their predictions.

A small  startup, Imagimob considers 6 trends in the IoT and Industrial IoT-IIOT in 2018. As you can imagine Low Power Area Networks (LPWAN), Edge computing, AI on the edge and Blockchain are included.

IoT Security repeat predictions in 2018. Forrester in the same line predict More cyber threats and design specialization.

Fog Computing, Security, and Smarter Decisions are IoT Predictions for 2018 by Saar Yoskovitz, CEO of Augury, a preventive maintenance company.

The State of IoT In 2018 for Marketers: We’re going to experience a massive increase in the number of digitally connected devices, changing the game for marketers across the globe.

IoT 2018 – the next stage: the IoT of integration, value and action

IoT Will Move From Experimentation To Business Scale - 

5 IoT trends that will define 2018 - In 2018, IoT-based ventures will have greater access to startup capital and be taken more seriously in the market. 

Only one wish for IoT 2018 from my side

In spite, I am not in this list of 17 Experts Tell The Most Exciting IoT Trends to Watch for in 2018, I have a wish for 2018: 

“I hope that in 2018, all proofs of concept become successful projects and that the most innovative startups resist the temptation to be acquired." 

Thanks, in advance for your Likes and Shares.

 

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The phrase, “the future is here,” is overused and has evolved into a catchphrase for companies struggling to position themselves in times of technological or digital transformations. Still, the sentiment is understood, especially in times like today, where the Internet of Things is quite literally changing the way we think about hardware and software. We’d like to offer an addendum to the phrase: “The future is here more quickly than we thought it would be.”

Digital transformation, increased computing ability, smart hardware and the growth of connectivity capabilities created a perfect storm of accelerated industry, and many were left scrambling to sift through the large amounts of information and solutions available. With that in mind, we wanted to provide some advice for companies across the industrial sector for the best ways to optimize operations for the Industrial IoT.

1) Upgrade your network and throughput capabilities.

Nothing can kill the ROI of automated processes more quickly than the literal inability to function. It’s important to understand that as you upgrade machinery and invest in the software to run it all, those systems demand greater bandwidth in order to effectively utilize the big data and analytics capabilities. Several options exist, but for most companies some combination of industrial-strength broadband (WiFi), narrow-band, cellular and RF communications will create the most effective network for the needs.

2) Invest in smart hardware.

This may seem like a no-brainer, and really, in the not-too-distant future, you may not even have a choice, but the shift toward Fog Computing is gaining momentum and being able to run decentralized computing between hardware and the Cloud can not only create greater operational efficiency, but it can also allow your data transmission to run more smoothly as well. The beauty of a Fog Computing system is that it allows a greater number of devices to transmit smaller data packets, which frees up bandwidth and speeds real-time data analytics. The core of this lies in the smart hardware.

3) Be proactive about application development.

Smart hardware means that it has the ability to host applications designed specifically for your needs. Previously, many companies shied away from app development because it required highly skilled developers and devices capable of hosting those apps – a combination that wasn’t readily available. Today, the scene has changed. With the rise of Node-RED, it is much easier today to create proprietary applications without a computer engineering degree, and any company serious about leveraging IIoT technology needs to be able to to use the full scope of its data.

4) Secure your communications.

There isn’t much more to be said about the importance of cybersecurity. If the last few years of massive data breaches haven’t rung alarm bells, then you aren’t paying attention. Cybersecurity today is a multi-layered need. Most companies building smart hardware are beginning to build encryption directly into the devices. But, since many companies use Cloud applications for computing and analytics, it is important to invest in strong security measures at that level as well. Unfortunately, the sophistication of cyber-attacks are only going to increase, along with the increase in importance of the data needing to be protected. It pays to be paranoid and act accordingly.

Further Reading:

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Embedded systems have become part and parcel of electronic equipment such as mobile phones, routers, modems, washing machines, microwave ovens, remote controls, RFID tags, PDAs, etc. They are low power consumption units that are used to perform some specific function of the device. For example, embedded systems are used in home automation with wired or wireless networking to control or regulate lights, security devices, sensors, audio/visual systems, sense climate change, monitoring etc. They are also used as networked thermostats in HVAC systems i.e. Heating, Ventilation and Air Conditioning systems. Furthermore, in the coming years embedded systems will be the mainstay for the deployment of many IoT solutions, especially within Industrial IoT applications. The leading players in embedded systems are engaged in hardware and software development, and are looking forward to bringing these transformations into their products to take advantage of the thriving IoT market.

The chief areas which are going to be transformed are microcontrollers, microprocessors and Real-Time Operating Systems (RTOS), followed by networking and memory devices, open source communities and developers. By 2020, humongous growth will be seen in the market for embedded systems. It is predicted that the market will grow with a CAGR of 22.5% to reach $226 billion by then. On the other hand, IoT will bring up a host of challenges for developers of embedded systems, as they need to develop devices which allow flawless and uninterrupted connectivity. To assist them meet the challenges posed by the internet of things, a real-time operating system (RTOS) must be designed that delivers flawless connectivity, scalability, modularity, and safety. 

What does internet of things, IoT mean for an embedded developer?

As the internet of things, IoT solutions are present across several industries, it gives a wonderful opportunity for embedded system developers too. For an embedded developer, it’s not all about linking multiple devices to the internet. There is much more than just connecting devices to the internet. Internet of things (IoT) for embedded systems is more about gathering, monitoring, and analyzing large amounts of disparate data from different sources and summarizing it into useful and actionable information to enhance the way services and devices are being used today.  

Hope you find this post helpful. If you did, share it with your colleagues and friends. For any query related to this post and career in IoT, you can comment down below. 

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The current political events in Barcelona provide us with a barely-needed reminder that we live in changing times.  I was in the city as part of the Trustonic team exhibiting at IoT Solutions World Congress last week and took some time to speak with fellow vendors. I soon saw some fantastic product demonstrations that drew my attention - I wanted to learn more. Frequently though, the response to: “This looks great - how is it secured? How do we know the data is trustworthy?” was a puzzled look and a “It uses our cloud and we secure that” or “It runs on a secure OS”.  Sometimes the response was worse: “It’s a closed network. You couldn’t attack it”.

It didn’t fill me with confidence. Everyone has a secure solution, it seems. But how do we know that it’s secure? Who has validated it? The questions and the perplexed looks continued. I slept uneasily.

I don’t want to criticise the IoT solutions that I saw – they were interesting and point to an exciting future for us all. Unfortunately, securing these solutions isn’t exciting and probably won’t draw a crowd to your stand. It’s rare to see ground-breaking security solutions making the news – consumers just expect it these days. Of course, you can expect a media frenzy if you’re breached. There have been some horrifying examples already and we are still in the early days of this industry. IoT solutions need to be secure by design – or, to put it another way, the components of the solution must already be secure when they are deployed. With the headache (and tedium) of security taken care of, the industry would be free to innovate and dream up even more exciting products.

I was showing an IoT security demo built on a Samsung ARTIK board, which already has Trustonic TEE technology embedded. It showed an IoT device connecting to Amazon Web Services (AWS), cryptographically proving itself to be secure and having a trusted identity, thus enabling it to become automatically registered on the system. Perhaps not as exciting as an IoT boat or sports bike sharing data in real time, but it demonstrated that, by embedding a truly secure OS (one that’s Common Criteria certified and FIPS-140-2 approved) combined with a Root of Trust installed in the factory (think of this like a digital birthmark), an IoT device can be trusted pretty much automatically. Once you have an inherently trusted device, you can be confident that data from its sensors is also trustworthy.

Shakespeare wrote “Love all, trust a few”. So, love all the cool and exciting IoT products – but only trust the few which are truly secure.

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A recent study by Cisco suggests that 75% of IoT initiatives will fail. However, there is growing pressure to invest in IoT. Ensuring the success of enterprise IoT initiatives is definitely not easy given technology immaturity, culture obstacles as well as well as the challenges of traditional organizational structure. So put the odds of success back into your favor using a customer-centric, integrated team (IT) philosophy.
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IoT Use Case - Battery Powered Device

Battery-powered devices are comfortable to use but have also some major issues. Especially during the development, some important aspects must be taken into consideration, unfortunately it is not just replacing the power plug. In the first episode of my new series, "IoT Use Case", I want to focus on the topic of "battery-powered devices" and give you some of my main take aways from the experience I was able to gain in real projects.
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The manufacturing industry is undergoing many changes. Those specializing in traditional manufacturing are finding it difficult to keep up with the changes. Perhaps the biggest change has been how traditional manufacturing has come under pressure to manage vast amounts of data captured from different sources. Here are some of the reasons the Internet of Things (IoT) can help.

1. KEEPING AN EYE ON SUPPLIERS

Quality control has become easier because IoT helps keep an eye on suppliers. This makes for easier manufacturing processes. Keeping an eye on suppliers is all about looking at all the constituents that the supplier offers. Capturing data about these constituents through IoT helps make for faster data processing and better quality control.

2. MORE PRODUCTIVITY

Thanks to IoT, many manufacturers are now building self-correcting systems. Missing parts are replaced and parts are replenished, giving rise to greater productivity. Since manufacturing industries are looking in particular for ways to boost productivity, there is no way for them to overlook what IoT can do for them. In addition to greater productivity, there is also more convenience since the need for human labor reduces.

3. MAINTAINING SUPPLY LINES

The Internet of Things is expected to help manufacturers stick to lean manufacturing while at the same time helping maintain supply lines. Since lean manufacturing often requires smart management of the supply lines – to ensure that components are never in short supply but there is no overstock – IoT is expected to help resolve many problems. It will help ensure that suppliers located in different regions can be kept in the loop and supply lines can be managed smoothly so that there is no shortage. It will also help reduce waste and optimize the use of resources.

4. UNINTERRUPTED MANUFACTURING PROCESS

Usually, manufacturing is divided into many processes, from sourcing of raw materials to production, transportation and reaching the customer. However, with the Internet of Things, experts envision something extra. The entire process will be smooth and effective. The raw materials will be already marked for production, intended to reach a particular buyer. This is how experts see things play out as IoT advances to new levels.

5. REDUCED COST

As IoT gains more efficiency, manufacturers can expect to see lowered costs. This is one of the primary reasons manufacturing experts are enthusiastic about the role of IoT. It will become easier to track information about products and processes and more automation would help bring about greater efficiency, thus eventually reducing costs. Lowered costs are expected to boost profit margins. If your manufacturing plant has not invested in IoT yet, this might be the right time to start.

6. LAUNCH NEW PRODUCTS

With IoT, studying needs and launching new products becomes easier. There is less jostle and inefficiency than traditional systems. Manufacturing is thus one of the key areas where you can expect a lot of improvement, thanks to the Internet of Things.

7. INTEGRATING OFFLINE AND ONLINE PROCESSES

Traditionally data and manufacturing have been treated as separate entities. However, in manufacturing industries where IoT advances, this is expected to change. As products begin to carry information about them, it becomes easier to assign a processing and logistics path to them. This is why it becomes critical to involve IoT in your manufacturing plant.

8. CONNECTED TO THE CONSUMER

Products are, in the end, manufactured to suit the consumer. Thanks to IoT, it becomes easier to stay connected to the consumer and create products that match their requirements. This offers two-way benefits, as the consumer gets the best products and the manufacturing plant is able to manufacture products per exact specification. There are a lot of benefits that manufacturers can expect in the long term, thanks to the Internet of Things.As manufacturing processes undergo change, it becomes imperative for manufacturers to make the most of the coming revolution. Supply chains and logistics will become smoother thanks to the industrial Internet of Things. According to many experts, we are at the cusp of another major revolution that will change not only how things are manufactured but also the market economy. It is a good idea to be prepared for these changes by investing in the right IoT system.

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Your IoT platform is the heart of your entire IoT solution. Building a reliable and scalable IoT platform is not a piece of cake, which is why these days the market is booming with hundreds of thousands of IoT PaaS (Platform as a Service) vendors. Choosing the right IoT platform for your solution has become more complex than it was ever before. That’s why, in this blog post we have covered some of the best selection criteria to pick the right IoT cloud platform for your needs. Before we delve into this, you first need to know what an IoT platform is. 

What is an IoT Platform?

In simple terms, a platform is a comprehensive set of tools and services which allow developers to build and run an application. However, an IoT platform could have diverse meanings depending on whom you are talking to in the internet of things, IoT ecosystem. For instance, an IoT platform for cloud service providers is their infrastructure, where a developer creates an application. For hardware vendors, an IoT platform is the embedded board where you could write your IoT applications. For the sake of clarity, we are considering an IoT platform as the middleware layer responsible for consuming data from the devices and sensors and providing meaningful and actionable results based on that insight. Generally, an IoT platform offers a device software development kit a.k.a SDK or well defined APIs through which developers and programmers could easily connect to any hardware platform and avail of their cloud-based services.

If you have attended any IoT expo recently, most probably you would have noticed that almost every IoT platform provider claims to be better, faster, safer and smarter than others. Now, how do you make a wise decision in such a competitive landscape and pick the right platform that will reduce your solution risk? Don’t fret, we’ve mentioned below some key selection criteria to choose the right IoT platform. Let us take a quick look. 

Considerations In Choosing The Right IoT Platform

Alas! Today, a cloud IoT platform is opted for based on the effectiveness of the vendor sales pitch. This is mainly because the companies that are trying to get a handle on digital transformation do not possess the requisite knowledge or training in IoT specific areas, and IoT vendors usually woo their customers based on their impressive customer references.  There are some important technical evaluation criteria which are often overlooked.  These need to be kept in mind for choosing the right IoT platform. Let's take a look at them:

#1 Bandwidth

#2 Scalability

#3 Protocol

#4 Security

#5 System Performance

#6 Redundancy and Disaster Recovery

#7 Interoperability

#8 Edge Intelligence 

#9 Budget, developmental skills, and capacity of your in-house team

#10 Your business model and its specific requirements that must be met  

Hope you find this post helpful! If you did, share it with your colleagues and friends as well. For any query related to this post and IoT training in India, you can comment down below. Thanks for your time! 

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While media has extensively reported in recent years on the estimated 30 billion devices or “things” that are expected to be connected to the Internet by 2020, there has been little discussion regarding the development and education of the next generation of engineers who will need to be trained to meet the market demands and challenges these devices will create.
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The Digital Twin: Key Component of IoT

A Digital Twin uses data from sensors installed on physical systems to represent their near real-time status, working condition or position. This modelling technology allows us to see what is happening inside the system without having to be able to get inside the system. It forms a critical step in the information value chain without which it is often impossible to get from raw data to insight, and therefore to value. As the Internet of Things grows, Digital Twins will become a standard tool for Data Scientists and Engineers wishing to use all this new data to automatically understand and respond to what is going on in the real world.
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Believe it or not, but the possibilities that the Internet of Things, IoT brings to the table are countless. The internet of things, IoT continues to be the next big thing in technology, and now the new phase of the internet of things is pushing everyone hard to ask questions about the data collected by sensors and devices of IoT.  

Undoubtedly, the internet of things, IoT will generate a tsunami of data, with the swift expansion of sensors and devices connected to the IoT. The sheer volume of data being produced by the internet of things will rise exponentially in the upcoming years. This generated data can provide extremely valuable insight to figure out what’s working well and what’s not. Moreover, the internet of things, IoT, will point out the issues that often arise and provide meaningful and actionable insight into new business opportunities and potential risks as correlations and associations are made. 

Examples of IoT Data:  

  • Data that improves productivity of industries through predictive maintenance of equipment and machinery 
  • Data that assists smart cities in predicting crime rates and accidents   
  • Data that creates truly smart living homes with connected devices    
  • Data that provides doctors real-time insight into information from biochips to pacemakers 
  • Data that gives critical communication between self-driven automobiles          

That’s great news, but it’s not possible for humans to monitor, analyze and understand all of this data using traditional methods. Even if they reduce the sample size, it will simply consume too much of their time.  Undoubtedly, finding actionable insights in terabytes of machine data is not a cakewalk, just ask a data scientist. The biggest challenge is to find ways to analyze the deluge of performance data and information that the internet of things, IoT devices creates. The only possible way to keep up with the terabytes of data generated by IoT devices and sensors and gain the hidden insights that it holds is using Artificial Intelligence, commonly known as AI.  

Artificial Intelligence (AI) and IoT    

Artificial intelligence, also known as machine intelligence (MI) is the intelligence that is exhibited by machines or software. John McCarthy, the person who coined this terminology back in 1955, describes it as "the science and engineering of making intelligent machines". In a nutshell, AI is a branch of computer science that emphasizes the creation of an intelligent machine that thinks intelligently, the way intelligent humans think and works and reacts like humans.   

In an IoT environment, Artificial Intelligence (AI) can aid business enterprises take the billions of data points they have and prune them down to what’s really helpful and actionable. The general principle is akin to that in retail applications i.e. review and analyze the data you have collected from different sources to find out similarities or patterns, so that better business decisions can be made.  

To be able to figure out the potential risks or problems, the collected data has to be analyzed in terms of what’s normal and what’s not. Abnormalities, correlations, and similarities need to be identified based on the real-time streams of data generated. The collected data combined with Artificial Intelligence makes life easier with predictive analytics, intelligent automation, and proactive intervention. 

Artificial Intelligence in IoT Applications  

  • New sensors will enable computers and smart devices to “hear,” gather sonic information about the user’s ambience   
  • Visual big data will allow computers and smart devices to gain a deeper insight of images on the screen, with the new AI app that understands the context of images

These are some of the promising applications of Artificial Intelligence in the internet of things, IoT ecosystem. The potential for highly personalized services are countless and will dramatically change the way people live. For example, Amazon.com can suggest what other books and movies you may like, helping Saavn and Gaana to determine what other songs you may love listening, and your family doctor would receive notification if you’re not feeling comfortable.  

Here Are Some Challenges Facing AI in IoT

  • Artificial Stupidity
  • Complexity
  • Safety
  • Ethical and legal Issues
  • Compatibility
  • Privacy/Security 

What’s Next? 

Gartner has predicted that by the end of next year, 6 billion connected devices will be requesting support, which means that processes, technologies, and strategies will have to be in place to respond to them. It is important to think of connected devices less as ‘things’, but more as customers or consumers of services in themselves. The need for Artificial intelligence, AI will become more prominent at the stage when the number of connected devices and sensors increase manifold.

Hope you find this post helpful. If you did, share it with your friends and colleagues. For AI and IoT Courses Online, you can do some research on Google to find the best institute that suits your needs and budget.

For any query related to this post, you can comment down below. Thanks for your time. 

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The other day we were discussing and debating on a solution to be designed to meet the sensing needs for access, temperature and humidity for some devices with form part of a networking infrastructure ecosystem. The idea was to build a IoT based system for monitoring and control.

The design discussions veered around the ability to collect data from the sensors and the types of short range communication protocols which could be deployed .Questions and clarification were raised if we were compliant to use short range communication protocols in sensitive areas as customer Data Centres which are like owned and  that they may be custodians of data of their end customers .

The hidden perils of data acquisition and data ownership reared its head which needed to be addressed as we moved forward .

The data which is acquired by sensors is essentially Machine Generated Data (MGD) .This post will  dwell on the subject of data ownership of MGD as follows :

  1. Sensors ( Data Acquisition and Communication )
  2. Machine Generated Data
  3. The Lifecycle of the MGD and the Ownership Paradigm
  4. Who should be the owner of the MGD?
  5. Sensors (Data Acquisition and Communication):

In the IoT ecosystem, the physical computing frontier is managed by the Sensors .Sensors essentially include three fundamental functions:

  • The act of sensing and acquiring the data
  • Communication of the data through appropriate protocols to communicate their readings to internet cloud services for further aggregation and trend analysis
  • The activity is energised by power supply,

The additional functions would include processing/system management and user interface

The Digital Computing part comprises the IoT application. This   is determined by the types of sensors, cloud connectivity, power sources, and (optionally) user interface used in an IoT sensor device. The following diagram showcases the primacy of sensors in a typical IoT Ecosystem.

When making physical measurements such as temperature, strain, or pressure, we need a sensor to convert the physical properties into an electrical signal, usually voltage. Then, the signal must be converted to the proper amplitude and filtered for noise before being digitized, displayed, stored, or used to make a decision. Data-acquisition systems use ADCs (analog-to-digital converters) to digitize the signals with adequate signal conditioning.

Sensor data communication to the cloud can be done in multiple ways from wireline to wireless communication of various complexities. While wire line communication has some important benefits (such as reliability, privacy, and power delivery over the same wires), wireless communication is the technology that is the key catalyst in the majority of IoT applications that were not previously practical with wired systems. Reliability, channel security, long range, low power consumption, ease of use, and low cost are now reaching new levels, previously thought infeasible

Some examples of recently popular IoT wireless communication types: Wi-Fi, Bluetooth Low Energy (aka Smart), Zigbee (and other mesh 802.15.4 variants), cellular, LPWA (Low-Power, Wide-Area network variants: Ingenu, LoRaWAN, Sigfox, NB-LTE, Weightless), and Iridium satellite.

  1. Machine Generated Data (MGD)  :

Sensor data is the integral component of the increasing reality of the Internet of Things (IoT) environment. With IpV6 , anything can be outfitted with a unique ip address with  the capacity to transfer data over a network. Sensor data  is essentially Machine Generated Data . MGD is that is produced entirely by devices / machines though an event or observation.

Here we would define human-generated data, what is recorded is the direct result of human choices. Examples are buying on the web, making an inquiry, filling in a form , making payments with corresponding updates on database. We would not consider the ownership of this data in the post and would be limiting our post to MGD.

  1. The journey of the MCD and the Ownership Paradigm:

The different phases exist in the typical  journey of Machine Generated Data .

Capture and Acquisition of Data– This is a machine or a device based function through signal reception.

Processing and Synthesis of the Data – This is a function which ensures enrichment and integration of Data

Publication of the Data – This is done by expert systems and analysts who work on exception management , triggers and trends .

Usage of Data – The action which need to be taken on the processed and reported information is used by the end user .

Archival and Purging of Data – This function is essentially done by the data maintenance team with supervision.

Now let us dwell on the Ownership Paradigms .They range from the origination of data , adding value to the data through make over , monetising of data through insights generated. Interestingly, let us explore if there is any conclusive method for determining how ownership should be assigned. A number of players may be involved in the journey of the data (e.g. the user, hardware manufacturer, application developer, provider of database architecture and the purchaser of data, each having an equal lay of the claim in different stages of this journey )

  1. Who should be the owner of MGD :

Let me share the multiple and conflicting views  :

  1. The owner of the device which records Data .In essence, the owner of machine-generated data(MGD), is the entity who holds title to the device that recordw the data. In other words, the entity that owns the IoT device also owns the data produced by that device.

But there could be a  lack of clarity if the device is leased rather than owned.. When real-world constructs such as lease holdings of (say servers) come into play, it indeed gets complex and even murky.

  1. Who should be the owner of MGD :

Let me share the multiple and conflicting views  :

The owner of the device which records Data .In essence, the owner of machine-generated data(MGD), is the entity who holds title to the device that recordw the data. In other words, the entity that owns the IoT device also owns the data produced by that device.

But there could be a  lack of clarity if the device is leased rather than owned.. When real-world constructs such as lease holdings of (say servers) come into play, it indeed gets complex and even murky.

The owner is the user of the Data :The other dimension is data may be owned by one party and controlled by another. Possession of data does not necessarily equate to title. Through possession there is control. Title is ownership. Referred to as usage rights, each time data sets are copied, recopied and transmitted, control of the data follows it. There could be cases where the owner of the device could be the user of the data.

 The maker of the Database who essentially invests in aggregating, processing and making the data usable is the owner of the Data :This has a number of buyers of this paradigm . The owner of a smart thermostat does not, for example, own the data about how he uses it. The only thing that is ‘ownable’ is an aggregation or collection of such data provided there has been a relevant investment in carrying out that aggregation or collection (the individual user is very unlikely to have made that investment). The owner here could be the Home automation company . The value which could be generated though this investment could be producing market intelligence , exploiting the insights form data to build market presence and differentiation ,

The purchaser of Data could be the owner of the Data: An auto insurance company could buy the  vehicle generated data ( from the makers of automobiles )  and could design a product for  targeted offerings to specific market segments based on say driving behaviour patterns  and  demographics  .This may not be as easy as this seems – refer the url  :  http://joebarkai.com/who-owns-car-data/ which states that the owner of the vehicle and not the maker of the car owns the data collected from the electronic data recorder .

The value chain of who owns the data can be a complex one with multiple claimants . As one aggregates more sources it just gets more complicated. A good example is in the making of smart cities. The sources of data can be from multiple layers and operational areas . City authorities would be making the effort to make use of the data in areas of waste management , traffic congestion , air pollution etc . So does the city authority own the data?

My personal take is , if someone in the MGD value chain  is making the data usable for  a larger good , and  in the process may monetize the data to cover the investments , that entity deserves to  be the owner of the data  as that is where value is generated .


Posted on August 14, 2017

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In the United States, precision agriculture is one of the largest industries by both operational scale and economic impact. The technology utilized is typically on the cutting edge, especially for automation and control. Things like sensors, programmable IoT radios and generally more complex software applications have allowed that industry to evolve, domestically, to a point where land and other resources are used optimally. Internationally, although there have been ‘smart’ or ‘precision’ practices in certain sectors of agriculture, many countries are just now starting to adopt the technology to its fullest extent, including the ability to innovate via start-ups and new practices.

India & the Digital Agriculture Revolution

According to an article in India Times (image credit), the country is aiming to secure a 20 percent stake in the IoT market share in the next five years through its ‘Digital India’ initiative. While many might look at India and think of the sprawling and diverse urban environments that could offer some potential complications for IoT, it is rural areas seeing the most interesting developments. There has been a noticeable growth in tele-medicine operations, which can allow patients in remote areas to interact with doctors for consultation, eliminating the need to get to a city, or vice versa. Perhaps an even greater area of growth lies in the agricultural realm. According to the article, agriculture employs 50 percent of the country’s population, so the potential for a digital revolution is high. Farmers are just starting to implement sensor technology, automation hardware, and even leading-edge tools like voluntary milking systems the allow cows to be milked on an automated machine according to biological needs.

Israel’s Precision Ag Start-Up Community

In Israel, where IoT technology is starting to mature, the name of the game is data collection and analytics. Mobile applications, sensor data collection hardware, and advanced analytics software are three areas that Israel is seeing significant market growth, according to Israel21c:

Israel stands out in precision-ag subsectors of water management, data science, drones and sensors, says Stephane Itzigsohn, investment associate at OurCrowd. … “Multiple startups are aiming toward the same goal — providing good agricultural data — but approaching it from slightly different angles,” Itzigsohn tells ISRAEL21c. “One might use satellite images or aerial photography; another might use autonomous tractors. Not all will get to that peak in the long journey of farming becoming more efficient.”

For example, CropX, an investor-backed advanced adaptive irrigation software solution, can be placed throughout a farming area and synced with a smart phone, allowing the operators to receive real-time data updates on things like soil and weather conditions. CropX is based in both Tel Aviv and San Francisco, indicating that the technology may be poised for wide international adoption in the future.

Analytics Drive Italy’s Drought Recovery

Italy is perhaps best known for a single agricultural export: wine. However, many would be surprised to find out that it is one of the top corn producers in the European Union, producing more than 7 million tons of corn in 2015, according to an RCR Wireless report. In 2016, the EU’s total corn output dropped noticeably due to year-long droughts affecting production. In Italy, start-up companies collaborated with industrial ag operations develop and deploy widespread soil sensor and water automation technology to help streamline farming practices and create a more efficient system for resource use. The technology allowed farmers to get a comprehensive look at their operations and identify high and low yield areas in order to better utilize the available space.

Precision Agriculture and the Industrial IoT

The continued maturation of IIoT technology is enabling countries around the globe to better utilize resources like water, energy, and land area to create better agricultural operations. As populations continue to expand, and food production becomes even more important, being able to connect these technologies across the globe could become a key factor in optimizing crop output in critical areas. Imagine the above farm in Italy being able to send its data to data scientists in Germany or the Eastern Europe who could in turn analyze it and provide actionable feedback. Or an industrial farm in Israel managing its yields sending that information in real-time around the country. These possibilities are not far off, and as the networks, hardware and software continue to be adapted, the future of precision ag internationally, will become the present.

For additional reading:

India Times: http://www.indiatimes.com/news/india/how-the-internet-of-things-is-digitizing-agriculture-speeding-up-rural-development-in-india-326546.html

Israel 21c: https://www.israel21c.org/5-israeli-precision-ag-technologies-making-farms-smarter/

RCRWireless: http://www.rcrwireless.com/20161005/big-data-analytics/precision-agriculture-omica-tag31-tag99

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