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IoT Central Digest, January 17, 2017

Happy New Year to all our members old, new, near and far!

If you want to look ahead into 2017, make sure to read the 50 predictions we put together. With input from vendors large and small, including AT&T, Autodesk, Hitachi, Intel, Salesforce and many others, these predictions cover a lot of ground including security, buildings, big data, ecosystems, and standards. Be sure to check them out.

This issue also looks at cars and IoT, security and stopping IoT attacks, health and big data analytics, and the ongoing IoT standards discussion. Enjoy!

Reminder: All members are free to post on IoT Central. We feature the best content and share across our social networks and other channels. Consider contributing today. Our guidelines are here.

50 Predictions for the Internet of Things in 2017
Posted by David Oro


For 2017, we asked our members, industry contacts and friends for their predictions around the Internet of Things. What new technologies will appear? Which companies will succeed or fail? What platforms will take off? What security challenges will the industry face? Will enterprises finally realize the benefits of IoT?

12 Steps to Stop the Next IoT Attack in its Tracks
Posted by Rob Tiffany

The IoT attack against Domain Name Servers from a botnet of thousands of devices means it’s way past time to take IoT security seriously. The bad actors around the world who previously used PCs, servers and smartphones to carry out attacks have now set their sights on the growing tidal wave of IoT devices. It’s time for consumers and enterprises to protect themselves and others by locking down their devices, gateways and platforms.

IoT Future – 34 Billion new Devices in 4 Years?
Posted by Joao Lopes


The most recent studies indicate that in 2020 more than 34 billion devices will be connected to the internet, in many sectors (Industrial, Agriculture, Transportation, Wearable Devices, Smart Cities, Smart Houses, etc). Of these 34 billion, the IoT will be responsible for 23 billion devices, the others 11 billion will be represented by the regular devices, such as, smartphones, tablets, smartwatches, etc.

Internet of Health: Is medicine ready to Big Data Analytics?
Guest blog post by Olga Kolesnichenko

What is Big Data: data, process of analysis or concept? There are many definitions that describe Big Data as big amount of data or as some methods of analytics of big amount of data. But more applicable is the approach that Big Data is the concept that includes: data with specific characteristics (V3 - volume, velocity, variety, or V5 - plus value and veracity), methods of analytics (the number of different software is growing), and devices, infrastructure, and most important - the ideas how to configure all into needed solution.

This is how Analytics is changing the game of Sports!!
Posted by Sandeep raut

Today, every major professional sports team either has an analytics department or an analytics expert on staff.  From coaches and players to front offices and businesses, analytics can make a difference in scoring touchdowns, signing contracts or preventing injuries. This article highlights the use of devices and analytics for sports.

Cars and Car Transportation in The Internet of Things Era
Posted by Nate Vickery

Whenever we read about, or hear about, people confidently presenting their vision of any kind of future trends, we, of course, need to take it with a grain of salt. However, as a business technology expert, I know where to look for indications on the direction technology is taking, and for the fun of it, I will try to make some educated guesses.
Let’s see what some of the trends that could change the way we travel are.

How to secure your smarthome gadgets
By Ben Dickson

The holiday season is a big time for consumer electronics and smart home gadget sales. With so many advances and innovations that we saw in the Internet of Things in 2016, there’s a likely chance that one of those connected devices has found its way into your home, or that of one of your loved ones, this Christmas.

But while IoT devices make our homes more efficient, drive energy saving and reduce costs, you should also take note that IoT devices are a source of security headaches. A huge number of smarthome gadgets are developed without sound development practices and end up being used for evil purposes.

NB-IoT is Dead. Long Live NB-IoT.
Guest post by Nick Hunn

As the old adage goes, “while the cat’s away, the mice will play”. In the case of NB-IOT, “when the spec’s delayed, LPWAN will play”, which is exactly what’s happening in the Internet of Things market today. The problem is that 3GPP (the 3rd Generation Partnership Project), the standards body which has been responsible for the 3G, 4G and 5G mobile standards, dropped the ball as far as the Internet of Things is concerned. Seduced by the slabs of black glass which suck up both our attention and the mobile networks’ spectrum, the 3GPP engineers totally forgot to design something to replace the old 2G workhorse of GPRS, which is responsible for most of today’s machine to machine communications. Instead, they spent all of their time designing high power, high speed, expensive variants of 4G to support an ongoing dynasty of iPhones, Galaxys and Pixels, none of which were any use for the Internet of Things.


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Guest blog post by Olga Kolesnichenko

What is Big Data: data, process of analysis or concept? There are many definitions that describe Big Data as big amount of data or as some methods of analytics of big amount of data. But more applicable is the approach that Big Data is the concept that includes: data with specific characteristics (V3 - volume, velocity, variety, or V5 - plus value and veracity), methods of analytics (the number of different software is growing), and devices, infrastructure, and most important - the ideas how to configurate all into needed solution.

Another concept is the Internet of Things that based on Big Data Analytics. There are some established configurations of IoT: Smart Home, Smart Health, Smart Manufacturing, Smart City, Smart Mobility, Smart Energy, Smart Farming, Smart Earth & Ocean, Smart Circular Economy.

Smart Health or Internet of Health (or any IoT configuration) has the human in core of concept. I should point out that more easy to accept the medical approaches for different configurations of IoT than accept IoT approaches for Health Care. Why I can insist on this statement? My statement leans on long-term period of complexity of accepting biorhythmology and gravitational biology in medicine.

But biorhythmology and gravitational biology have the direct application for Internet of Health or IoT. The person controls own different medical data day by day during everyday life. And this new situation should be viewed as medical data collecting under gravitational forces and natural biorhythms influences to person.

Three sections of multifactorial regulation of human body should be mentioned: environmental, behavioral and homeostatic. Environmental section includes circannual rhythm (annual) and circadian rhythm (daily). Behavioral section includes body orientation towards gravitational forces (lying down, standing, sitting); active movement (walking, jogging, exercises); passive movement (lift and transport) with influence of acceleration forces; as well as sleep, emotional reactions, eating. Homeostatic section includes the processes of neurohumoral regulation of the body. This section consists of functional systems of the body, described by Russian scientist K.V. Sudakov and his following.

Thus creating Internet of Health configuration and implementing Big Data Analytics the medical data should be considered in terms of three sections of multifactorial regulation of human body. 

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50 Predictions for the Internet of Things in 2017

Last year we put together our first ever predictions list looking at what would come to the Industrial Internet of Things in 2016. No one predicted at the time the biggest IoT story of 2016: massive DDoS attacks via the Internet of Things. To their credit, there were many security predictions for 2016, but no one nailed #DDoS.

For 2017, we asked our members, industry contacts and friends for their predictions around the Internet of Things. What new technologies will appear? Which companies will succeed or fail? What platforms will take off? What security challenges will the industry face? Will enterprises finally realize the benefits of IoT?

The 50 predictions in this list focus on the Industrial side of the Internet of Things. The list is global in perspective and includes predictions from researchers, professors, independents, start-ups and major brand names like Autodesk, AT&T, Hewlett Packard Enterprise, Hitachi, Intel, Salesforce, and many others.

We received hundreds of inquiries and obviously couldn’t include them all, so if you submitted a prediction and it’s not listed here, know that we read it and appreciate your contribution. Thank you!

Without further adieu, here are the 50 Predictions for the Internet of Things in 2017.

Sanjay Sarma, head of MIT’s open and digital learning efforts and lead instructor “Internet of Things: Roadmap to a Connected World

2017 will be the year we start to bring order to the chaos of IoT. Right now, every connected device talks to each other in a different way. Devices remain siloed in their independent systems, creating a complex communication environment. What’s more, this patchwork of systems is difficult to maintain, upgrade and improve. This is a serious problem that will need to be addressed as the IoT expands, otherwise there is a real chance that the power of the IoT could be compromised.  

In the months to come, we will likely see focus shift to establishing paradigms for effective implementations and use of IoT. There will also be a push for government to get involved. For example, we might see initiatives or agencies that can be used to incubate academic institutions, labs and companies testing and working on best practices for the IoT.

What’s clear is that we need a unified approach that creates genuine ‘interconnectness.’ In my view, this is what will allow us to realize the true potential of the IoT

Pieter van Schalkwyk, CEO, XMPro

IIoT Ecosystem will start to formalize

Industrial IoT requires an ecosystem of partners to provide advisory, technology and services that collectively create effective solutions for customers. In 2017 we will see that some of these relationships start to formalize in different industries and for different use cases as these partners become experienced in working together to create real IoT solutions.

Leadership in IIoT platforms for major vendors

Industrial B2B customers are historically more conservative than the consumer market and these customers will look to support platform offerings from their existing enterprise solution providers such as SAP and GE, rather than the hundreds of independent small consumer centric IoT platforms. The industrial IOT ecosystems that I mentioned earlier, will form around a few of these leading industrial IoT platforms.

ROI case studies for IIoT will start to emerge

In 2015 industrial companies were talking about IoT, in 2016 they started planning how to leverage IoT, and in 2017 we will see real examples and case studies emerge. It will highlight the areas where IoT can provide a significant ROI contribution, but also expose the hype areas where there is no real value other than the “coolness” factor. The combination of the ecosystems and the platforms will lead to real solutions that start to solve real problems.

Loudon Blair, Senior Director, Corporate Strategy, Ciena

IoT will open new opportunities for carriers. This year we’ll see carriers face up to the challenges of digital disruption: avoiding service commoditization and building compelling digital services to compete with Over-The-Top players. Their efforts will focus on the opportunities on offer in the emerging IoT market, and as they launch IoT services and platforms, carriers will benefit from two core differentiators. First, they already own the network: the key piece of infrastructure needed to realize consumer IoT services. Second, carriers ‘own’ the customers as subscribers to their networks.

Chris Penrose, Senior Vice President of IoT, AT&T

AT&T has identified five major IoT trends and predictions for 2017:

1) Global IoT will be impossible without a network scaled to match, and a robust, multi-layer integrated network approach will open new global IoT opportunities.

2) Companies both large and small will look to enterprise-level leaders to respond to their needs for a variety of connectivity solutions. Large-scale, optimized IoT solutions will enable companies to more efficiently manage assets and improve operations.

3) IoT will continue to blur between B2B and B2C solutions so that technologies advance both enterprise and consumer interests. Enterprises and innovators will join forces to manage and even evolve customer expectations, allowing them to do more than they ever thought possible.

4) Through a proliferation of sensors virtually everywhere, millions of new data points and thousands of use cases will be developed. As a result, the rate of data collection will increase algorithmically, not linearly, making more information available than ever before.

5) IoT security will remain a top concern in the upcoming year. Items like network-connected wearables or smart coffee pots will become of increasing interest to hackers due to the often limited attention paid to security in their development cycles.

Vincent Granville, Pioneering Data Scientist, Data Science Central

The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance. The frontier between AI, IoT, data science, machine learning, deep learning and operations research will become more fuzzy. Editor's Note: For a full list of Vincent's predictions go to our sister site Data Science Central here.

Dr. Chase Cunningham, Director of Cyber Security Operations, A10 Networks

IoT continues to pose a major threat. In late 2016, all eyes were on IoT-borne attacks. Threat actors were using Internet of Things devices to build botnets to launch massive distrubted denial of service (DDoS) attacks. In two instances, these botnets collected unsecured “smart” cameras. As IoT devices proliferate, and everything has a Web connection — refrigerators, medical devices, cameras, cars, tires, you name it — this problem will continue to grow unless proper precautions like two-factor authentication, strong password protection and others are taken.  Device manufacturers must also change behavior. They must scrap default passwords and either assign unique credentials to each device or apply modern password configuration techniques for the end user during setup. Editor's Note: For a full list of Chase's predictions go here.

Rod Schultz, VP of Product, Rubicon Labs

Business models will be the new ‘it’ thing to innovate (think Uber for anything and everything), as the value of IoT is realized. The transition from Capex to Opex-based business models, built directly on the power of IoT, will drive innovation and unlock new business models and subscription-driven services for industries that have been static for years.

Robert Vamosi, CISSP, Security Strategist, Synopsys, Inc.

Embedded Security will finally get serious.  Devices, once thought to be too small to include their own security, will undergo a more thorough analysis beginning with firmware testing. The software inside the chip is just as important as the application controlling it. Both need to be tested for security and quality. Some of the early IoT botnets have leveraged vulnerabilities and features within the device itself

Bryan Kester, Head of IoT, Autodesk

Editor’s Note: We interviewed Bryan earlier this year. Read about it here.

My predictions for 2017 in IoT:

1) In 2017, the focus on IoT will shift noticeably away from consumer use cases, like smart toasters and toothbrushes, to industrial and B2B use cases, like connected factories, warehouses, and robots. Edge platforms that focused on “makers” and IoT enthusiasts will pivot to the enterprise, following opportunities to monetize their offerings. However, a number of these IoT platforms will likely go out of business following unsuccessful pivots.

2) There will be another IoT security breach in the headlines. Odds are, someone will be careless with security and the media is on the lookout for these types of stories.

3) There will be an increased focus on case studies and real examples of IoT ROI as the 2013-2015 pioneer projects start generating compelling metrics. A handful of these examples may be in the emerging field of Machine Learning, and how it can fuse together with IoT for predictive and autonomous operations.

IoT Central members can see all the predictions here. Become a member today here. It's free!

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Guest blog by Ajit Jaokar. Ajit”s work spans research, entrepreneurship and academia relating to IoT, predictive analytics and Mobility. His current research focus is on applying data science algorithms to IoT applications. This includes Time series, sensor fusion and deep learning (mostly in R/Apache Spark). This research underpins his teaching at Oxford University (Data Science for IoT) and ‘City sciences’ program at UPM (Madrid). Ajit is also the Director of the newly founded AI/Deep Learning labs for Future cities at UPM (University of Madrid.) His book is included as a course book at Stanford University for Data Science for IoT. In 2015, Ajit was included in top 16 influencers (Data Science Central), Top 100 blogs( KDnuggets), Top 50 (IoT central), No 19 among top 50 twitter IOT influencers (IoT institute.) 

Introduction

In the last two decades, more than six billion devices have come online. All those connected "things" (collectively called - The Internet of Things) generate more than 2.5 quintillion bytes of data daily. That's enough to fill 57.5 billion 32 GB iPads per day (source Gartner). All this data is bound to significantly impact many business processes over the next few years. Thus, the concept of IoT Analytics (Data Science for IoT) is expected to drive the business models for IoT. According to Forbes, strong analytics skills are likely to lead to 3x more success with Internet of Things. We cover many of these ideas in the Data Science for IoT course.

Data Science for IoT has similarities but also some significant differences. Here are 10 differences between Data Science for IoT and traditional Data Science.

  1. Working with the Hardware and the radio layers
  2. Edge processing
  3. Specific analytics models used in IoT verticals
  4. Deep learning for IoT
  5. Pre-processing for IoT
  6. The role of Sensor fusion in IoT
  7. Real Time processing and IoT
  8. Privacy, Insurance, and Blockchain for IoT
  9. AI: Machines teaching each other(cloud robotics)
  10. IoT and AI layer for the Enterprise

1. Working with the Hardware and the radio layers

This may sound obvious, but it's easy to underestimate. IoT involves working with a range of devices and also a variety of radio technologies. It is a rapidly shifting ecosystem with new technologies like LoRa, LTE-M, Sigfox etc. The deployment of 5G will make a big difference because we would have both Local area and Wide area connectivity. Each of the verticals (we track Smart homes, Retail, Healthcare, Smart cities, Energy, Transportation, Manufacturing and Wearables) also have a specific set of IoT devices and radio technologies. For example, for wearables you see Bluetooth 4.0 in use but for Industrial IoT, you are likely to see cellular technologies which guarantee Quality of Service such as the GE Predix alliance with Verizon

2. Edge processing

In traditional Data Science, Big data usually resides in the Cloud. Not so for IoT!. Many vendors like Cisco and Intel call this as Edge Computing. I have covered the impact of Edge analytics and IoT in detail in a previous post: The evolution of IoT Edge analytics.

3. Specific analytics models used in IoT verticals

IoT needs an emphasis on different models and also these models depend on IoT verticals. In traditional Data Science, we use a variety of algorithms (Top Algorithms Used by Data Scientists). For IoT, time series models are often used. This means : ARIMA, Holt Winters, moving average. The difference is the volume of data but also more sophisticated real time implementations of the same models ex (pdf) : ARIIMA: Real IoT implementation of a machine learning architecture for reducing energy consumption. The use of models vary across IoT verticals. For example in Manufacturing : predictive maintenance, anomaly detection, forecasting and missing event interpolation are common. In Telecoms, traditional models like churn modelling, cross sell, upsell model , customer life time value could include IoT as an input.

4. Deep learning for IoT

If you consider Cameras as sensors, there are many applications of Deep Learning algorithms such as CNNs for security applications, eg from hertasecurity. Reinforcement learning also has applications for IoT as I discussed in a post by Brandon Rohrer for Reinforcement Learning and Internet of Things

5. Pre-processing for IoT

IoT datasets need a different form of Pre-processing. Sibanjan Das and I referred to it in Deep learning - IoT and H2O.

Deep learning algorithms play an important role in IoT analytics. Data from machines is sparse and/or has a temporal element in it. Even when we trust data from a specific device, devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Monitoring sensor data continuously is also cumbersome and expensive. Deep learning algorithms can help to mitigate these risks. Deep Learning algorithms learn on their own allowing the developer to concentrate on better things without worrying about training them.

6. The role of Sensor fusion in IoT

Sensor fusion involves combining of data from disparate sensors and sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. (adapted from Wikipedia). The term 'uncertainty reduction' in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view based on the combined information. Sensor fusion has always played a key role in applications like Aerospace:

In aerospace applications, accelerometers and gyroscopes are often coupled into an Inertial Measurement Unit (IMU), which measures orientation based on a number of sensor inputs, known as Degrees of Freedom (DOF). Inertial Navigation Systems (INS) for spacecraft and aircraft can cost thousands of dollars due to strict accuracy and drift tolerances as well as high reliability.

But increasingly, we are seeing sensor fusion in self driving cars and Droneswhere inputs from multiple sensors can be combined to infer more about an event.

7. Real Time processing and IoT

IoT involves both fast and big data. Hence, Real Time applications provide a natural synergy with IoT. Many IoT applications like Fleet management, Smart grid, Twitter stream processing etc have unique analytics requirements based on both fast and large data streaming. These include:

  • Real time tagging: As unstructured data flows from various sources, the only way to extract signal from noise is to classify the data as it comes. This could involve working with Schema on the fly concepts.
  • Real time aggregation: Any time you aggregate and compute data along a sliding time window you are doing real time aggregation: Find a user behaviour logging pattern in the last 5 seconds and compare it to the last 5 years to detect deviation
  • Real time temporal correlation: Ex: Identifying emerging events based on location and time, real-time event association from largescale streaming social media data (above adapted from logtrust)

8. Privacy, Insurance and Blockchain for IoT

I once sat through a meeting in the EU where the idea of 'silence of the chips' was proposed. The evocative title is modelled on the movie 'silence of the lambs'. The idea is: when you enter a new environment, you have the right to know every sensor which is monitoring you and to also selectively switch it on or off. This may sound extreme - but it does show the Boolean (On or Off) thinking that dominates much of the Privacy discussion today. However, future IoT discussions for privacy are likely to be much more nuanced - especially so, when Privacy, Insurance and Blockchain are considered together.

IoT is already seen to be a significant opportunity by Insurers according to AT Kearney . Organizations like Lloyds of London are also looking to handle large scale systemic risk by the introduction of new technology driven by sensors in urban areas ex Drones. Introduction of Blockchain to IoT creates other possibilities - like IBM says:

Applying the blockchain concept to the world of [Internet of Things] offers fascinating possibilities. Right from the time a product completes final assembly, it can be registered by the manufacturer into a universal blockchain representing its beginning of life. Once sold, a dealer or end customer can register it to a regional blockchain (a community, city or state).

9. AI: Machines teaching each other (cloud robotics)

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines. This idea is not so far-fetched.

Consider Fanuc, the world's largest maker of industrial robots. A Fanuc robot teaches itself to perform a task overnight by observation and through reinforcement learning. Fanuc's robot uses reinforcement learning to train itself. After eight hours or so it gets to 90 percent accuracy or above, which is almost the same as if an expert were to program it. The process can be accelerated if several robots work in parallel and then share what they have learned. This form of distributed learning is called cloud robotics

10. IoT and AI layer for the Enterprise

We can extend the idea of 'machines teaching other machines' more generically within the Enterprise. Enterprises are getting an 'AI layer'. Any entity in an enterprise can train other 'peer' entities in the Enterprise. That could be buildings learning from other buildings - or planes or oil rigs or even Printers! Training could be dynamic and on-going (for example one building learns about energy consumption and 'teaches' the next building). IoT is a key source of Data for the Enterprise IoT system. Currently, the best example of this approach is Salesforce.com and Einstein. Reinforcement learning is the key technology that drives IoT and AI layer for the Enterprise.

"By end of 2016 more than 80 of the world's 100 largest enterprise software companies by revenues will have integrated cognitive technologies into their products." (source Deloitte).

Far from the AI Winter, we seem to be suddenly in the midst of an AI spring. And IoT suddenly has a clear business model in this AI Spring.

Conclusion

From the above discussion, we see many similarities but also significant differences when it comes to Data Science for IoT. There are obvious differences (for example in the use of Hardware and Radio networks). But for me, the most exciting development is the fact that IoT powers exciting new greenfield domains such as Drones, Self driving cars, Enterprise AI, Cloud robotics and many more. We cover many of these ideas in the Data Science for IoT course.

Only a final few places remain with this batch.

Re-posted with permission from the author. Originally posted here.  

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Originally posted by Vincent Granville

It's time again to share your predictions for 2017. I did my homework and came with these 10 predictions. I invite you to post your predictions in the comment section, or write a blog about it. Ramon Chen's predictions are posted here, while you can read Tableau's prediction here. Top programming languages for 2017 can be found here. Gil Press' top 10 hot data science technologies is also worth reading. For those interested, here were the predictions for 2016. Finally, MariaDB discusses the future of analytics and data warehousing in their Dec 20 webinar.

My Predictions

  1. Data science and machine learning will become more mainstream, especially in the following industries: energy, finance (banking, insurance), agriculture (precision farming), transportation, urban planning, healthcare (customized treatments), even government.
  2. Some, with no familiarity with data science, will want to create a legal framework about how data can be analyzed, how the algorithms should behave, and to force public disclosure of algorithm secrets. I believe that they will fail, though Obamacare is an example where predictive algorithms were required to ignore metrics such as gender or age, to compute premiums, resulting in more expensive premiums for everyone.
  3. The rise of sensor data - that is, IoT - will create data inflation. Data quality, data relevancy, and security will continue to be of critical importance.
  4. With the rise of IoT, more processes will be automated (piloting, medical diagnosis and treatment) using machine-to-machine or device-to-device communications powered by algorithms relying on artificial intelligence (AI), deep learning, and automated data science. I am currently writing an article that describes the differences between machine learning, IoT, AI, deep learning and data science. You can sign-up on DSC to make sure that you won't miss it. 
  5. The frontier between AI, IoT, data science, machine learning, deep learning and operations research will become more fuzzy. Statistical engineering will be present in more and more applications, be it machine learning, AI or data science. 
  6. Many systems will continue to not work properly. The solution will have to be found not in algorithms, but in people. Read my article Why so many Machine Learning Implementations Fail. An example is Google analytics, which fails to catch huge amounts of robotic traffic that is so rudimentary and so obvious, you don't need any statistical or data science knowledge to filter it or block it. People publish elementary solutions to address these issues, yet it continues unabated. Fake reviews, fake news, undetected hate speech on Twitter, undetected plagiarism by Google search, are in the same category. Eventually it leaves room for new players to jump in and build a system that will actually work. 
  7. Reliance on public data and public news will come with bigger scrutiny. Some say that the failure to predict the elections is a data science failure. In my opinion, it is a different type of failure: it is the failure to recognize that the media are biased (they publish whatever predictions that fit with their agenda) and maybe even those doing the surveys are biased or incompetent (there are lies, damn lies, and statistics as the saying goes). It is also a failure to recognize the very high volatility in these elections, and the fact that day-to-day variations were huge. Anyone able to compute sound confidence intervals that incorporates historical data,  would have said that the results were not reliably predictable. Finally, I always thought that the winner would be the one best able at manipulation and playing tricks, be it hacking or paying the media.
  8. More and more data cleaning, pre-processing, and exploratory data analysis will be automated. We will also face more unstructured data, with powerful ways to structure them.  Multiple algorithms and models will be more and more blended together to provide the best pattern recognition and predictive systems, and boost accuracy. 
  9. Data science education will evolve, with perhaps a come back of strong university curricula run by leading practitioners, and fewer people finding a job through data science camps only, as many of these camps do not train you to become a data scientist, but instead a Python / R / SQL coder with classic, elementary, even outdated and dangerous statistical knowledge. Or data camps will have to evolve, or otherwise risk becoming another kind of Phoenix university.
  10. Attacks against data-dependent infrastructure will switch from stealing or erasing data, to modifying data. Some will be launched from IoT devices if security holes are not fixed.

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7 Trends of IoT in 2017

By Ahmed Banafa

IoT is one of the transformational trends that will shape the future of businesses in 2017 and beyond. Many firms see big opportunity in #IoT uses and enterprises start to believe that IoT holds the promise to enhance customer relationships and drive business growth by improving quality, productivity, and reliability on one side, and on the other side reducing costs, risk, and theft. By having the right IoT model companies will be rewarded with new customers, better insights, and improved customer satisfaction to mention few benefits.

With all this in mind, let’s explore some of the trends of IoT impacting business and technology in 2017:

1) IoT and Blockchain Will Converge

Blockchain is more than a concept now and has applications in many verticals besides FinTech including IoT. #Blockchain technology is considered by many experts as the missing link to settle scalability, privacy, and reliability concerns in the Internet of Things. Blockchain technology can be used in tracking billions of connected devices, enable the processing of transactions and coordination between devices; allow for significant savings to IoT industry manufacturers. This decentralized approach would eliminate single points of failure, creating a more resilient ecosystem for devices to run on. The cryptographic algorithms used by Blockchain would make consumer data more private. In 2017 IoT will converge with Blockchain for better security and privacy opening the door for a new category in applications, hardware, and talents.

 2) IoT Devices and More DDoS Attacks

Forrester thinks that the recent #DDoS attack that hit a whopping 1600 websites in the United States was just the tip of the iceberg when it comes to the threat that the connected device poses to the world. That attack confirmed the fear of vulnerability of IoT devices with a massive distributed denial of service attack that crippled the servers of services like Twitter, NetFlix , NYTimes, and PayPal across the U.S. on October 21st , 2016. It’s the result of an immense assault that involved millions of Internet addresses and malicious software, according to #Dyn, the prime victim of that attack. "One source of the traffic for the attacks was devices infected by the Mirai botnet". All indications suggest that countless Internet of Things (IoT) devices that power everyday technology like closed-circuit cameras and smart-home devices were hijacked by the malware, and used against the servers.

3) IoT and Many Mobile Moments

IoT is creating new opportunities and providing a competitive advantage for businesses in current and new markets. It touches everything—not just the data, but how, when, where and why you collect it. The technologies that have created the Internet of Things aren’t changing the internet only, but rather change the things connected to the internet. More mobile moments (the moments in which a person pulls out a mobile device to get what he or she wants, immediately and in context) will appear on the connected device, right from home appliances to cars to smartwatches and virtual assistants. All these connected devices will have the potential of offering a rich stream of data that will then be used by product and service owners to interact with their consumers.

4) IoT, Artificial Intelligence, and Containers

In an IoT situation, #AI can help companies take the billions of data points they have and boil them down to what’s really meaningful. The general premise is the same as in the retail applications – review and analyzes the data you’ve collected to find patterns or similarities that can be learned from so that better decisions can be made.

The year 2017 would see Internet of Things software being distributed across cloud services, edge devices, and gateways. The year would also witness IoT solutions being built on modern Microservices (an approach to application development in which a large application is built as a suite of modular services. Each module supports a specific business goal and uses a simple, well-defined interface to communicate with other modules) and containers (lightweight virtualization) that would work across this distributed architecture. Further, machine-learning cloud services and Artificial Intelligence will be put to use to mine the data that would be coming in from IoT devices.

5) IoT and Connectivity:

Connecting the different parts of IoT to the sensors can be done by different technologies including Wi-Fi, Bluetooth, Low Power Wi-Fi , Wi-Max, Ethernet , Long Term Evolution (LTE) and the recent promising technology of #Li-Fi(using light as a medium of communication between the different parts of a typical network including sensors). In 2017, new forms of wireless connections, such as 3GPP’s narrowband #NB-IoT, #LoRaWAN, or #Sigfox will be tested. Forcing IoT decision-makers to evaluate more than 20 wireless connectivity options and protocols, which is one step in the right direction of having standards for connectivity.

6) IoT and Talent-Shortage

Organizations launching IoT projects including smart cities and industrial facilities face a tougher time in recruiting talent. Complicating matters is that it remains a challenge to find enough workers to secure the Internet of Things. 45 percent of IoT companies struggle to find security professionals, according to a TEKsystems survey. 30 percent report having difficulty finding digital marketers. In 2017, industrial major vendors will invest in IoT training and certifications and make it part of the mainstream training programs in the tech industry.

7IoT and New Business Models

The bottom line is a big motivation for starting, investing in, and operating any business, without a sound and solid business models for IoT we will have another bubble , this model must satisfy all the requirements for all kinds of e-commerce; vertical markets, horizontal markets, and consumer markets. A new business model including sharing cost of devices with consumers, reducing the cost of ownership and making UX less hassle and more joyful. 2017 will see new categories being added to smart markets. One key element is to bundle service with the product, for example, devices like Amazon’s Alexa will be considered just another wireless speaker without the services provided like voice recognition, music streaming, and booking Uber service to mention few.

The Road Ahead

The Internet of Things (IoT) is an ecosystem of ever-increasing complexity; it is the next level of automation of every object in our life and convergence of new technologies will make IoT implementation much easier and faster, which in turn will improve many aspects of our life at home and at work and in between. From refrigerators to parking spaces to smart houses, IoT is bringing more and more things into the digital fold every day, which will likely make IoT a multi-trillion dollar industry in the near future. One possible outcome in the near future is the introduction of “IoT as a Service” technology. If that service offered and used the same way we use other flavors of “as a service” technologies today the possibilities of applications in real life will be unlimited. But we have a long way to achieving that dream; we need to overcome many obstacles and barriers at many fronts before we can see the benefits of such technology.

This article originally appeared here.

References:

http://www.iotworldnews.com/author.asp?section_id=508&doc_id=727678&

http://www.indianweb2.com/2016/11/08/internet-things-iot-2017-predictions-forrester/

http://www.ioti.com/iot-trends-and-analysis/11-iot-predictions-2017

https://www.linkedin.com/pulse/iot-standardization-implementation-challenges-ahmed-banafa?trk=mp-author-card

https://www.linkedin.com/pulse/wake-up-call-iot-ahmed-banafa?trk=mp-author-card

https://www.linkedin.com/pulse/securing-internet-things-iot-blockchain-ahmed-banafa?trk=mp-author-card

https://www.linkedin.com/pulse/last-mile-iot-artificial-intelligence-ai-ahmed-banafa?trk=mp-author-card

https://www.linkedin.com/pulse/iot-implementation-challenges-ahmed-banafa?trk=mp-author-card

 

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Guest blog post by Shay Pal

This article has been contributed by Alain Louchez (Georgia Tech Research Institute)

The Internet of Things already integrates a new phase beyond prescriptive analytics. 

There is no shortage of attention lately on the “Internet of Things”. As a case in point, see the “Developing Innovation and Growing the Internet of Things Act” or “DIGIT Act”, i.e., S. 2607, a  bill introduced in the Senate on March 1, 2016 and amended on September 28, 2016,  “to ensure appropriate spectrum planning and inter-agency coordination to support the Internet of Things” – A companion bill, H.R. 5117, was introduced in the House of Representatives on April 28, 2016.

However, since there is no “internet” dedicated to “things”, it is fair to state that the Internet of Things does not exist as such.  We are left with a definitional vacuum, but it is hammering the obvious to acknowledge that there is no dearth of attempts around the world to fill the gap. Perhaps as a helpful shortcut, we could view the expression as a metaphor that captures the arrival of almost anything and everything, until now out of scope, into the communications space.

Source for picture: click here

While the proposed DIGIT Act sees the Internet of Things as referring to the “growing number of connected and interconnected devices”, we could argue that the smartness of those devices is what makes IoT truly unique; hence the “interconnection of intelligent things” may be a more accurate descriptor.

In sum, the term “Internet of Things” heralds the advent of what we could call a true “pulsating world” arising from sending data from and to smart devices.

Extracting information, and therefore value, from the data captured at the edge of the Internet of Things network is increasingly becoming a core focus of IoT solution providers.

As the Internet of Things is bound to become a gargantuan reservoir of data, it close interaction with data analytics has been well documented in academia and business.

Charles McLellan writing for a special feature of ZDNet in March 2015 on the Power of IoT and Big Data: Unlocking the Power, agrees that “the IoT will massively increase the amount of data available for analysis by all manner of organizations,” but cautions that “the volume, velocity and variety (not to mention variable veracity) of IoT-generated data makes it no easy task to select or build an analytics solution that can generate useful business insight.”

Business Analytics frameworks such as IBM’s and Gartner’s are widely used to chart the course of the business insight extraction. The former identifies three analytic capabilities, i.e., descriptive, predictive and prescriptive while the latter inserts a diagnostic stage between descriptive and predictive. Table 1 summarizes both frameworks:

Table 1: Existing Business Analytics Categories

Business Analytics Category

IBM

Gartner

Descriptive Analytics

"What has happened?"

"What happened?

Diagnostic Analytics

N.A.

“Why did it happen?

Predictive Analytics

"What could happen?”

"What will happen?”

Prescriptive

"What should we do?”

“How can we make it happen?

 

Extending the data analytics discussion to the Internet of Things (a.k.a. “the analytics of things”), Professor Thomas Davenport, in a March 2016 Data Informed article, wonders When Will the Analytics of Things Grow Up? He expresses the concern that “most of the ‘analytics of things’ thus far have been descriptive analytics – bar (and Heaven forbid, pie) charts, means and medians, and alerts for out-of-bounds data,” and highlights areas where business analytics can make a difference in IoT beyond descriptive (dashboard-type report on performance) such as diagnostic (alerts that need attention), predictive (e.g., breakdown potential) and prescriptive (recommendations based on predictions, experiments, or optimizations).

Prescriptive has often been described as the “final frontier” of data analytics. Yet, or perhaps because of this, a recent academic paper observes that “there are very limited examples of good prescriptive analytics in the real world” (1). The same paper proposes an overall definition “in general, prescriptive solutions assist business analysts in decision-making by determining actions and assessing their impact regarding business objectives, requirements, and constraints. For example, what if simulators have helped provide insights regarding the plausible options that a business could choose to implement in order to maintain or strengthen its current position in the market.” Let’s note in passing the “assisting” role of said solutions. Indeed advising outcomes and providing recommendations are presently associated with this final phase of business analytics. This seems to somewhat contradict the notion of “prescription” since its very meaning implies imposition of one single direction or rule. However, the current generally-accepted use gives room for interpretation or non-compliance.

Professor Davenport’s conclusion in the above-mentioned article anticipates the need for another type of prescription in the IoT world: “In some IoT environments such as smart cities analytics will need to provide automated prescriptive action /…/ In such settings, the amount of data and the need for rapid decision making will swamp human abilities to make decisions on it.”

Davenport’s prescient clarity is strengthened when we realize that, in the IoT space, the future, with “automated prescriptive action”, is actually already here (e.g., IoT devices with actuators, industrial robots, etc.). Consequently, we might have to recognize a possible fifth phase (a new final frontier?), i.e., normative analytics (or automated prescription), whose conceptual tenets lean on system engineering. For all intents and purposes, normative analytics is a self-adaptive system, which adjusts dynamically to changes in external and internal conditions (a point of comparison could be rocket launch technologies).

While not yet mainstream, the following two examples with life and death consequences, i.e., driverless cars and autonomous robots for surgery, show the interactive analytics continuum.

Data analysis for driverless cars (or more generally, unmanned vehicles) has to go through at least five stages, i.e., 1) descriptive (e.g., wear and tear of a multitude of components); 2) diagnostic (e.g., what’s wrong?); 3) predictive (e.g., impact of traffic jams, weather, detours); 4) prescriptive (based on previous analytics layers, what acceptable options does the vehicle have?) and finally 5) a normative stage (what the vehicle must do). All these phases must happen concurrently and seamlessly; there is no time to delay the final decision, i.e., the car must stop or move. The consequences of a delay, however minute, could be catastrophic.

The same can be said of surgical robots. For instance, a team of researchers at the Sheikh Zayed Institute for Pediatric Innovation within the Children’s National Health System, and Johns Hopkins University recently announced that they had developed a “Smart Tissue Autonomous Robot” (STAR) for soft tissue surgery, which is “a difficult task for a robot given tissue deformity and mobility” (2). Quite remarkably, they claim that they have demonstrated that “supervised autonomy with STAR not only is feasible but also, by some metrics, surpasses the performance of accepted surgical procedures including RAS (Robot-Assisted Surgery), LAP (Laparoscopy), and manual surgery” and that “autonomous surgery can bring better efficacy, safety, and access to the best surgical techniques regardless of human factors, including surgeon experience.” Is there a better example of automated and integrated analytics intimately connected with action?

The above two IoT-related use cases exemplify the smooth transition from the virtual to the physical world through analytics and may be used as a template/model for business analytics (see Table 2).

Table 2: Analytics Categories with a New “Final Frontier”

Business Analytics Category

Output

Category          Differentiator

Human Factor

Descriptive Analytics

"What has happened"    (Inform)

"It is what it is"

Except for the selection of statistical techniques and areas of interest, does not shape the output

Diagnostic Analytics

“Why did it happen?” (Understand)

“This is what needs attention”

Except for the selection of statistical techniques and areas of interest, does not shape the output

Predictive Analytics

"What could happen?"    (Forecast)

"This is what the future may look like"

Except for the selection of statistical techniques and areas of interest, does not shape the output

Prescriptive Analytics

"What should we do?"       (Advise)

"These are the optimal options we have" - Possible delay between analysis and decision

Shape the output, but post-analysis - Human decides on execution of potential actions.

Normative Analytics

"This is what must happen."                 (Execute)

Based on predictive analytics, next step is automatically triggered and instrumented - No delay between analysis and decision/action

Shape the output with action criteria embedded in this phase - Except for monitoring, human is not involved in execution.

 

 

Conclusion

Occam’s razor notwithstanding, there is a case to be made for specifying a new phase in data analytics, i.e., normative analytics, which, whether identified or not, already exists, especially in the Internet of Things universe. This is the step where, fed by the other analytics phases, action is automatically triggered and executed.

It would be hard to merge prescriptive analytics (as currently defined in the analytics space) with the proposed normative analytics. There will always be situations where the need for injecting human input into the final decision preempts process automation. In any event, prescriptive analytics remains the necessary step that must be gone through and successfully passed before normative analytics kicks in.

Note that this model can be applied to any Cyber-Physical System (CPS), whatever its size. The National Institute of Standards and Technology (NIST) defines Cyber-Physical Systems as “smart systems that include engineered interacting networks of physical and computational components,” and underscores that “CPS and related systems (including the Internet of Things (IoT) and the Industrial Internet) are widely recognized as having great potential to enable innovative applications and impact multiple economic sectors in the worldwide economy”. It also foresees that CPS’ new capabilities will fuse with other important evolutions of technologies such as big data analytics, which “are expected to bring transformational changes to economies, societies, our knowledge of the world, and ultimately the way people live” (3).

Dr. Frey and Professor Osborne’s abundantly-quoted study on computerization serves as a reminder that the broad diffusion of automated prescription might just be around the corner. According to their estimate, “47 percent of total US employment is in the high risk category, meaning that associated occupations are potentially automatable over some unspecified number of years, perhaps a decade or two” (4).

This prospect cannot materialize without normative analytics resting on optimization techniques. The irony is that as high technology increasingly pervades all economic functions, non-technology considerations become part and parcel of the whole framework. Policy, regulation, ethical and other critical perspectives need to be factored in the optimization calculation.

Among its many applications, normative analytics could open the door to a new regulatory domain, which has been called “governance by things” (5) or “algorithmic regulation” (6), i.e., a new way of implementing rules and norms through analytics not, however, without misgivings (7).

Regardless of the attractiveness of this new concept, the introduction of normative analytics as an integral phase of the data analytics spectrum must be further validated, and prudence advocated the same way William Vorhies not too long ago cautioned against the possible groundless addition of prescriptive analytics in the data analytics nomenclature, i.e., “with about 1/3rd of companies having yet adopted predictive analytics the last thing we need is introducing ‘the next big thing’ into that conversation unless there is some real value to be had” (8).

-------------------------------------

(1)    Uthayasankar Sivarajah, Muhammad Mustafa Kamal, Zahir Irani, and Vishanth Weerakkody, “Critical analysis of Big Data challenges and analytical methods”, Journal of Business Research, Elsevier, August 10, 2016, available at http://www.sciencedirect.com/science/article/pii/S014829631630488X

 

(2)    Azad Shademan, Ryan S. Decker, Justin D. Opfermann, Simon Leonard, Axel Krieger, and Peter C. W. Kim, May 4, 2016, “Supervised autonomous robotic soft tissue surgery”, Science Translational Medicine 8 (337), available at http://stm.sciencemag.org/content/8/337/337ra64.full

 

(3)    See Framework for Cyber-Physical Systems – Release 1.0 – May 2016, document prepared by the Cyber-Physical Systems Public Working Group (CPS PWG), an open public forum established by the National Institute of Standards and Technology (NIST), available at https://s3.amazonaws.com/nist-sgcps/cpspwg/files/pwgglobal/CPS_PWG_Framework_for_Cyber_Physical_Systems_Release_1_0Final.pdf

 

(4)    Carl Benedikt Frey, and Michael A. Osborne, “The Future of Employment: How Susceptible are Jobs to Computerization?”, September 17, 2013, Oxford Martin Press, available at http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf

 

(5)    Wolfgang Shultz and Kevin Dankert, ’Governance by Things’ as a challenge to regulation by law, Internet Policy Review, Volume 5, Issue 2, June 30, 2016, available at http://policyreview.info/articles/analysis/governance-things-challenge-regulation-law

 

(6)    Brett Goldstein, “When Government Joins the Internet of Things”, The New York Times, September 8, 2013, available at http://www.nytimes.com/roomfordebate/2013/09/08/privacy-and-the-internet-of-things/when-government-joins-the-internet-of-things

 

(7)    Evgeny Morozov, “The Rise of Data and the Death of Politics, The Guardian (UK), July 19, 2014, available at https://www.theguardian.com/technology/2014/jul/20/rise-of-data-death-of-politics-evgeny-morozov-agorithmic-regulation

 

(8)    William Vorhies, “Prescriptive versus Predictive Analytics - A Distinction without a Difference?”, October 23, 2014, Decision Science Central, available at http://www.datasciencecentral.com/profiles/blogs/prescriptive-versus-predictive-analytics-a-distinction-without-a

 

 

The views expressed in this article are solely the author’s and do not necessarily represent those of the Georgia Institute of Technology (“Georgia Tech”), the Georgia Tech CDAIT members, the University System of (U.S. State of) Georgia or the (U.S.) State of Georgia.

Alain Louchez is the Managing Director of the Center for the Development and Application of Internet of Things Technologies (CDAIT pronounced “sedate”) at the Georgia Institute of Technology (“Georgia Tech”), Atlanta, Georgia. CDAIT’s purpose is to expand and promote the Internet of Things (IoT)’s huge potential and transformational capabilities through research, education and industry outreach. The Center is sponsored by global companies headquartered in North America, Europe, Asia and Australia.  Alain was recently selected by Instituto Tecnológico y de Estudios Superiores de Monterrey (Guadalajara) as an international advisor to “Centro de Innovación, Desarrollo Tecnológico y Aplicaciones de Internet de las Cosas” (a.k.a. “Center of Innovación in Internet of Things or CIIoT”) after bid approval by the government of Mexico in June 2016. Prior to joining Georgia Tech, Alain held various executive positions including member of the board of directors of leading companies in the high tech industry, in Europe and the United States.

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IOT enables physical world objects like appliances, smartphones, cars and various machines to connect to the internet and will usher an era of machine-to-machine communications (M2M) devices that will interact with humans.

IoT offers advanced connectivity of smart devices and systems that empower users to perform the tasks in an automated fashion than manual intervention.

IOT & Health Care

The Transformation of the health care industry has begun. Patients will be able to leverage the continuously increasing information about healthcare & medical and they are combining it with their real-time data which includes genetics, lifestyle, behavior and environment data.

The mobile app based healthcare transformation was begun with fitness apps that measure your daily workouts and provides calorie burning charts and other stats which millions of users has downloaded from app store.

Healthcare apps got a huge boost when Apple release its Healthkit framework for developers and that really started a new trend of building innovative apps and solutions in healthcare by current generation developers & entrepreneurs.

Healthcare domain is a very different playfield compare to other domains because of its technicalities, a large amount of complex data structures, data privacy, and other compliance factors. Due to such factors, healthcare domain was earlier dominated by EHR and PHR companies like Cerner, Epic Systems, AllScripts, NextGen Healthcare and few more however with the rise of IoT, the scenario has started shifting towards the innovative solutions that are tied to mobile technology.

#IoT has enabled the current generation hospitals to have smart beds which can sense whether the patient is laying on the bed or not, it also senses when the patient gets up from his bed and collects all the data.

Home Medical dispensers can be equipped with the system which sends the data to the cloud when the medication doesn’t take place with the help of IoT.

Various devices gather data and sharing it with healthcare staff for improved efficiency and patient care. Such examples can include ECG monitors, Glucose monitors, pulse oximeters, clothes with sensing devices and more!

Some of the useful use cases of IoT in healthcare are,

  • Data from devices, including hospital room sensors, lab equipment, employee wearable and patient monitoring devices will enable the industry to accelerate the transformation to digital.
  • Devices for measuring patient pulse rate, blood pressure, blood sugar are already started its footprints in the market. Hire Mobile Developer team is expecting that such devices will eventually evolve and it will help patients from many undesired health diseases with its sharp analysis of historical data in the next 2 to 3 years of time.
  • Remote patient monitoring will automatically feed patient records with real-time data, perform analysis and send coaching notifications to both providers and patients which make healthcare convenient for 24/7 that will be web enabled and personalized.
  • With the help of #IoT in healthcare, we can have more advanced and faster devices useful in health care which are more accurate and can transmit health records faster.
  • Researchers, business execs, doctors and IT professionals will collaborate to provide better overall care to patients. Vendors will increase their focus on integrating platforms, applications and data.
  • Hospitals can develop long-term strategies to leverage sensors and wearable throughout their operations in order to build a real-time sense-and-respond intelligent operation that cuts costs and improves patient experiences and outcomes. Researchers, nurses and doctors will spend less time doing administrative work and more time with patients.
  • We have already passed the fitness tracker app era and we are moving towards the new trend of health tracker apps. Health tracker apps will be the next big thing as per our assumption in the next 3 years of time.
  • The apps that will help patients to improve their quality of life will become one of the most popular apps in the developed and developing countries equally.
  • Besides this it results in decreased costs, improved outcomes of treatment, improved disease management, reduce errors, enhanced patient experience, enhanced management of drugs.

Challenges of IOT in Healthcare

  • Need immense Analysis – the product need to be meaningful, scalable and easy to use.
  • Data Privacy – Concerns may arise for the privacy of the data which needs to be taken care so the system is absolutely fool proof.
  • The two patients with the same disease may have different level of sensitivity so providing the best result for both the patient for any app is a challenge. This needs very well crafted apps can deal with the complexities of human body as well as the medical world together.
  • IoT is still new so there are many healthcare device manufacturers are not yet ready to build smart devices and still relying on their age old manufacturing practices. They need to open up their hardware for the app developers!

If you have an idea or you are in a healthcare domain then feel free to get in touch with me and let me share our expertise with you to build your next gen IoT & Mobile based healthcare app!

Hire Mobile Developer (CrossShore Solutions Subsidiary Company) has provided its mobile expertise to some of the well known companies and helped them to expand their enterprise level EHR and PHR to bring on mobile and tablets for providers and patients.

I firmly believe that IoT & Mobile App combination holds the potential to transform the healthcare industry so that the current pain points can be eliminated efficiently.

Please feel free to connect with me on LinkedIn. Please feel free to share this post.

By Harshul Shah. This article originally appeared here.

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Six Stats for Manufacturers and Enterprise IoT

The IoT Trends and Situations study was conducted by Autodesk and Taxal over the course of 2016. Its objective was to study manufacturers’ situation, trends, enablers and inhibitors in areas of IoT. The study consisted of in-depth, one-on-one phone interviews with over 280 companies across a range of industrial markets and geographies. The study was designed to capture both statistically relevant information as well as qualitative and subjective feedback with over 85% of those interviewed being company executives and managers.

  • 56% of companies are currently active in IoT projects, and
  • 78% will be in the next 3 years
  • 50% of companies believe that IoT helps them to differentiate their offerings, and
  • 58% feel that it helps them better compete with others in their markets
  • 52% believe that IoT will allow them to develop new services, and
  • 49% see IoT as a technology that helps improve the uptime of their products

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IoT Central Digest, December 5, 2016

We are looking for your predictions in IoT for 2017. What are the big and small moves that will mark IoT in 2017? Will there be platform consolidation? What will happen with security? All our members and friends are invited to send predictions to us. For inspiration grab a crystal ball, or review our 50 Predictions for the Internet of Things in 2016. Send your predictions directly to me with a message here. I will compile the best predictions and publish later this month.

Valueable contributions appear in this issue of IoT Central digest including a MQTT library demo, a look at the open source for IoT software stacks, the software platforms that matter and more.

If you're interested in being featured, we always welcome your contributions on all things IoT Infrastructure, IoT Application Development, IoT Data and IoT Security, and more. All members can post on IoT Central. Consider contributing today. Our guidelines are here.

What Bruce Schneier teaches us about IoT and cybersecurity

Business Intelligence Goes Mobile: MobileFirst is Getting IBM Watson Cognitive Capabilities

IoT Software Platforms - The 11 Providers That Matter Most And How They Stack Up

Three Software Stacks Required for the Internet of Things (IoT)

Guest post by Ian Skerrett, Eclipse Foundation.

In parallel to the emerging IoT industry, the general software industry has moved towards open source as being a key supplier of critical software components. The phrase “software is eating the world” reflects the importance of software in general, but in reality the software industry is now dominated by open source. This is true for key software categories, including Operating Systems (Linux), Big Data (Apache Hadoop, Apache Cassandra), Middleware (Apache HTTP Server, Apache Tomcat, Eclipse Jetty), Cloud (OpenStack, Cloud Foundry, Kubernetes), and Microservices (Docker). The purpose of this article is to look at the new technology requirements and architectures required for IoT solutions.

Two Hot Growth Areas for IoT

Guest blog post by Bill Vorhies

If you want to capitalize on all the amazing advancements in data science take a look at these two hot growth areas for IoT.  It's likely that these will be where a lot of venture capital is invested over the next year or two. A lot of well deserved attention is being directed at speech, image, and text processing.  The tools in this area are the CNNs and RNNs we've reviewed in recent articles.  We'll continue to exploit and refine these capabilities probably for several more years but if you want to get out in front you really need to be looking for the next wave.  We think we've spotted two areas of emerging opportunities where there's not yet a lot of competition but soon will be.

What is Deep Learning ?

Posted by Sandeep raut 

Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? 
Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on. Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed.  Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning

Why The Aviation Industry Needs to Hurry Up With IoT Implementation

Guest blog post by Raj Dalal

With their billions of annual captive customers, one would think that airports, and by logical extension, airlines, were prime candidates for the implementation of the Internet of Things (IoT) technology to improve passenger experience, yet, there’s not been much progress there. Yes, airlines and airports have started “experimenting” with IoT, but unlike say the health or medicine sector, progress has been comparatively slow. 

Will There Be A Dominant IIoT Cloud Platform?

When you think about consumer cloud platforms, which ones come to mind? Amazon AWSMicrosoft Azure and Google’s Cloud Platform are likely to be at the top of your list. But what about industrial cloud platforms? Which ones rise to the top for you? Well, GE’s PredixSiemen's MindSphere, and the recently announced Honeywell Sentience are likely to be on any short list of industrial cloud platforms. But they aren’t the only ones in this space. Cisco's JasperIBM’s Watson IoTMeshifyUptake, and at least 20 others are competing to manage all those billions of sensors that are expected to encompass the Industrial Internet of Things (IIoT). Which one do you think will end up dominating the market?

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Guest blog post by Eduardo Siman

If you have ever launched a web page on a Raspberry Pi or Arduino, you know that it feels a bit like magic. How is it possible that a device the size of a credit card can be a web server? Its awe inspiring to be sure. For me, it leads to one of the key questions I have about the Internet of Things and how it will affect our world. How are we going to organize and search for all of these billions of devices?

When the web began, there were millions of web pages that were not properly organized or connected. I remember before google existed using AltaVista for images, Go.com for sports, and AOL for news. The next iteration was the hierarchical taxonomy that Yahoo introduced, were you could browse through categories to find what you wanted by getting more and more specific. And then Google arrived and it quickly became clear that free-form text search was going to be the way forward.

How will this process evolve for the Internet of Things? I recently listened to an episode on my favorite podcast Software Engineering Daily with the inventor of TCP/IP - the legendary Vint Cerf. One of his key ideas around IoT was the need for filtering out meaningful data sources. I suppose this isn’t very different from pagerank algorithms that originally helped decide what would show up on your Google search. But how do you conduct pagerank for objects? On the web, websites connect to each other because they are part of a topic or feature set – sports news connects to team pages which connect to fan pages. In IoT, objects will be connected based on proximity regardless of their common features. That might not be very helpful when counting the number of inbound and outbound connections to an object. A pagerank algo might tell you that the most important device in your search is the one that is closest to the highest number of other devices. This would be a sort of centroid device - in the machine learning/clustering sense of the word. But finding the centroid might not be very helpful when you are looking for one device in the middle of nowhere. 

Imagine its 10 years in the future and you go on your IoT search engine. You need to find a temperature sensor in Antartica that will give you the current reading. So you type in the words “sensor + Antartica”.

What happens next? Well, if every sensor in the world is running its own web server and that web server is connected to the internet and there are other pages linking to and from it, you might find it using traditional search algorithms. But that seems unlikely. 

After all, why would you want to use the precious resources of a micro sensor to run a web server? Wouldn’t you rather delete Node.JS and add some additional computational capability or store more temperature readings on its limited memory? So lets assume no web browser on your temperature sensor. Now what? How in the world do you find it?

Well here’s an idea, create a new IP naming convention that can include geographical features such as latitude and longitude. And add a feature that explains the type of device you are encoding. If you type in -77.375894, 0.069533 in Google Maps you will find yourself in the middle of Antartica. What if the IoT IP address for your temperature sensor was (-77.375894, 0.069533, TEMP)? I suppose you would have to change it if it moved – but then again we are constantly changing IP addresses in our current state of the world. And of course the MAC address would still identify the hardware you are looking for. I’m sure that I’m not the first one to think of a taxonomy for IoT that includes location. In fact, I wouldn’t be surprised if there are hundreds of proposed hierarchical naming structures already out there. 

What I do know, is we are going to need something that doesn’t depend on IoT devices being on the web. And whatever that naming convention is, will have to be pretty flexible and have a whole lot of digits. 

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Guest blog post by PG Madhavan

Many people worry that "AI" will usher in a new Industrial revolution where machines replace humans. My take is that it will be more like the Printing press revolution that launched the Age of Enlightenment! The effect will be less of soaring productivity but more of better decision-making leading to a SMARTER society.

 

Part of the problem is the misnomer, "AI or artificial intelligence" (which may be great marketing). Instead, if we called it what it is, i.e., "ML or machine learning", things will sound less ominous and convey a more realistic picture.

 

Machine learning does not float all boats. Baumol effect will persist in markets such as healthcare and education since they are inelastic as far the eye can see; not very likely that IoT or MOOCs will make these sectors significantly more productive (there may be tempting counterexamples such as e-commerce but I do not contribute to the view that Retail Commerce has been totally disrupted and that it will ever be; it will remain labor-intensive). However, there is a silver lining . . . In Neural Plasticity & Machine Learning blog, I make the point that the Internet and the current Machine Learning revolutions are NOT like the Industrial Revolution (of steam engine and electrical machines) which caused productivity to soar between 1920 and 1970; it is more like the Printing Press revolution of the 1400s!

 

Printing press and movable type played a key role in the development of Renaissance, Reformation and the Age of Enlightenment. Printing press created a disruptive change in “information spread” via augmentation of “memory”. Oral tradition depended on how much one can hold in one’s memory; on the printed page, memories last forever (well, almost) and travel anywhere.

 

Similarly, “IoT Machine Learning” (IoT is the framework for deploying ML and hence go together) is in the early stages of creating disruptive changes in “decision making” via augmentation based on Big Data analysis. Humans can process only a very limited portion of Big Data in their heads; networked sensors and devices along with Data Science can make sense of Big Data impacting virtually every sphere of human activity.

 

Will “IoT Machine Learning revolution” create more Michelangelo paintings, fracture religions or give birth to another Scientific method? Hard to know . . . the effects may be in some totally unforeseen domains. However, it is likely that humans will do everything SMARTER with machine learning augmentation – what “enlightenment” may that lead to?!

 

If you are a futurist engaged in advising major corporations on what will happen in the next decade or two, Machine Learning being the next “printing press” (and NOT “industrial”) revolution will have consequences. Machine Learning revolution will not create an industrial-scale soaring of productivity . . . but may launch a NEW “age of enlightenment”! Major corporations ought to position themselves to hasten the growth of this new age of enlightenment and find ways to create value for themselves.

 

I for one am excited about the possibilities and surprises in store in the next few decades.

 

PG Madhavan, Ph.D. - “Data Science Player+Coach with deep & balanced track record in Machine Learning algorithms, products & business”

http://www.linkedin.com/in/pgmad

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TPS-based Office Aircon Controller Application

Figure:1 TPS-based aircon controller

TPS-based aircon controller

Air conditioning is a consumptive business while air conditioners are big-ticket to run. In general, AC systems, older ones, in particular, do not have any real temperature feedback. You set the temperature on your remote, but alas, it has absolutely nothing to do with the actual temperature in the room. Even when it gets colder outsides, many aircons keep blasting cold air into your space. As a result, you have to constantly readjust the temperature as needed for optimal comfort throughout the day.

No doubt, AC systems are improving day by day, but there are still old systems that cannot get updated. In some instances, it’s absolutely impossible to invest in a new system. Sometimes, it is just a catch 22 to rip the old aircon out and install a new one. A basic aircon has many parts that typically are split between an outside and inside configuration, hence you may have to undergo a drastic interior renovation. In Tibbo office in Taipei, we have got trapped in an identical situation. We just have to get by with the AC system we’ve got. Our aircon is controlled with a dozen of infrared remotes lying around.

Some time ago, we set out to create a management system for our dated HVAC system. We used Tibbo Project System (TPS) for this endeavor. Our spec for the aircon controller consisted of exactly two items:

  • The aircon must run or not run depending on whether the lights are on or off. The formula is simple: no lights = no people = no need to run the AC.
  • The temperature in the room must be monitored by the device that stops the aircon whenever the temperature is cooled off to the preset point.

To achieve our goal, we used a TPS2L system equipped with these Tibbits:

  • Ambient temperature probe
  • IR code processor Tibbit (#26)
  • IR front-end Tibbit (#27)
  • Ambient light sensor Tibbit (#28)

Let us tell you about the probe.The probe replaces the ambient temperature meter (Tibbit #29). It is nice to have the meter built right into the TPS. The problem is, the meter is affected by the internal heat of the TPSsystem itself. This influence is especially noticeable for the TPS2L device – it’s LCD really warms up the box! The new probe has the same circuit as the Tibbit #29, with the added benefit of being external to the TPSdevice. Now the measurements are accurate.

Here is a look at the items you need to set up in the menu:

IR commands. This is where you train your IR code processor to be able to transmit two commands: “On,” and “Off.” For the “On” command, use the lowest temperature that your aircon’s remote allows you to set (usually 16 degrees C). The logic here is that when you need to lower the temperature in the room you can use the coldest temperature setting, and when the room cools down to the preset temperature, the aircon is turned off. So really, you only need two commands.

Target temperature. You don’t need to set it here. There are dedicated buttons on the main screen.

Pre-cool start time. This is something we added along the way. Now it is possible to turn the aircon on, once a day, even before we all arrive at the office. Our day starts at 9 am. We set this time for 8:30 am, and by the time we get in, the office is nice and cool (while the scorching Taipei summer keeps on raging outside). The pre-cool timer is hardcoded for 45 minutes. If the lights are still off at 9:15 the aircon is turned off.

*Brightness threshold. *This is the brightness that the TPS will consider to correspond to “lights on.” The value is not expressed in any standard measurement units; it’s just the value the Tibbit #28 returns. So, how do you know what number to set here? Simple: the brightness is displayed on the main screen, like this: “Light level: 718”. Note the value with the lights off and on, then set the threshold to some value in the middle between the two.

Temp. meas. adjustment. This is useful for when you choose to use the Tibbit #29. As we’ve explained above, its measurements are affected by the internal heat of the TPS itself. You can use a regular thermometer and determine the measurement error. For example, if your thermometer reads 25C, and TPS shows 28C, then you must adjust the temperature by 3 degrees C. The data returned by the new external probe need no adjustment.

Further work

In phase 2 of this project we will connect our aircon controller to an AggreGate server. It will be possible to control the system via a smartphone app, which we going to design for this purpose. Now you know why our configuration menu has items like Network, AggreGate, etc. Stay tuned!

Figure:2 Aircon

Figure 2  Aircon

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Guest blog post by Raj Dalal

With their billions of annual captive customers, one would think that airports, and by logical extension, airlines, were prime candidates for the implementation of the Internet of Things (IoT) technology to improve passenger experience, yet, there’s not been much progress there.

Yes, airlines and airports have started “experimenting” with IoT, but unlike say the health or medicine sector, progress has been comparatively slow. Areas where IoT can prove to be a great asset are baggage handling, equipment including avionics monitoring and passenger communication; in fact, the entire passenger experience from door to aircraft to door.

According to a 2015 Airline IT Trends Survey produced by SITA in association with Airline Business, only about 37% of global airlines had allotted some kind of budget for IoT implementation, while 58% were planning to invest resources in the IoT with “emphasis on pilot projects”, and about 16% were preparing for major deployment.

Where the airlines and IoT are concerned, the former needs to understand that deploying IoT based tech such as beacons, smart luggage trackers and fuel monitors will help cut down on time as well as financial mismanagement. A buck saved is a buck earned, after all.

On the passenger facing side, there’s enormous scope for IoT tech implementation. I’ve already mentioned baggage and passenger handling, but matters such as fuel efficiency, even the tracking of pets in transit, are areas where this tech will yield positive results.

Questions that IoT can address

Typically, as all of us who have flown know, an air traveller’s journey is in 4 parts:

  • Before the journey: Is flight on schedule, what about traffic conditions, etc
  • At the airport: How long is the check-in queue, where’s my gate, where’s the nearest food court, etc
  • In the aircraft: What’s on the menu, will I get the inter-connecting flight
  • After the journey: Hope my baggage is on the carousel, but where’s the carousel, where’s the lost luggage counter, and so on

The versatility of IoT tech is such that it can address problems in all four areas.

 One other reason for the IoT adoption time lag in the airline industry is the complex integration issues, given that the aviation industry’s other “business”, outside of ferrying passengers, is handling cutting edge technology. So CIOs or CTOs should be able to figure out how to assimilate IoT based tech in existing IT infrastructure.

To be fair, a majority of the airlines have acknowledged that IoT will generate benefits for them in the coming years. The technology retains the potential to alleviate passenger pain points, including luggage handling and inter-flight connections.

Some successful user case

  • London City Airport, for example, which initially implemented a pilot project about two years ago -  the first major airport in the world to test how IoT could help operations - has come to realise the potential of this game-changing technology.  
  • Miami airport in the United States, which handles approximately 20 million passengers a year, turned to web-connected sensors and apps to help improve a passenger’s travel experience. Last year, it debuted a mobile app called MIA Airport Official on iOS and Android devices. The app uses a network of beacons around the airport to provide detailed information to passengers based on their location and needs. Each of the beacons is about a stack of pennies and so are easy to install. The app provides directions throughout the huge airport and helps passengers to find restaurants and baggage carousels.
  • In 2014, US carrier JetBlue fully automated the check-in process who booked its ‘Even More Space’ seats on domestic flights. A day before departure, passengers receive a ready-to-print boarding pass via email, plus an option to download a pass via the JetBlue iOS or Android mobile apps.

The future

With international air traffic set to grow by about 7% annually in the short term, here’s what the aviation industry should be doing - using IoT, to rapidly improve passenger experience, both on the ground and in flight:

  1. Like other industries, digitally transform the business
  2. Use Big Data and analyse it into actionable information to become more agile
  3. To make airport and airline operations more responsive and real time
  4. To use existing IT resources to maximize operational efficiency and passenger experience

Perhaps, what stops airlines from “going the whole hog”, is the lack of finance, coupled with the “fear of grappling with new technology”, and then, the security aspect. Such lack of zeal on part of the airlines is understandable to an extent, but airports surely are no strangers to technology.

About the Author

Raj Dalal is Founder & Principal of data analytics research & advisory firm, BigInsights. BigInsights specialises in the application of Data Analytics & IoT technologies as a catalyst for business innovation. BigInsights helps craft Big Data analytics strategies for their clients which include major Australian enterprises, start-ups and Data Analytics vendors. 

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Two Hot Growth Areas for IoT

Guest blog post by Bill Vorhies

Summary:  If you want to capitalize on all the amazing advancements in data science take a look at these two hot growth areas for IoT.  It's likely that these will be where a lot of venture capital is invested over the next year or two.

A lot of well deserved attention is being directed at speech, image, and text processing.  The tools in this area are the CNNs and RNNs we've reviewed in recent articles.  We'll continue to exploit and refine these capabilities probably for several more years but if you want to get out in front you really need to be looking for the next wave.  We think we've spotted two areas of emerging opportunities where there's not yet a lot of competition but soon will be.

 Velocity = Stream Processing + IoT

In terms of the three original Big Data characteristics, today’s frontier action is all about velocity.  Not necessarily in absolute speed, though there is some of that, but in the volume of data in motion from sensors and our ability to make use of it through streaming platforms like Spark and specialty analytics, particularly those with a renewed focus on time series.  (see Recurrent Neural Nets.)

 

Three Characteristics of Emerging IoT

There are three emerging technologies that are enabling cutting edge IoT.  It’s opportunities that combine these three that we think present the best current place to dig.

  1. Streaming Platforms – focus on bi-directional.  So far the field has focused mostly on one-direction platforms that receive and process the data and then alert actions via data viz or message.  Focus on bi-directional platforms that send commands directly back to the sensor source in real time without human intervention required to filter or interpret the action.
  2. Very Smart Sensors.  This is where our cutting edge developments in CNNs and RNNs come into play.  Although these NNs are difficult to train, once trained they can be migrated directly onto chip-enabled sensors so that the processing and potentially the response can take place without the need to even send the data back to the platform.  Not only can these actions be extremely complex, the platform can be relegated to reporting what the sensor has already done and for future refresh and development of more models.  This will be especially true as we move forward with Spiking Neural Nets with their very low power consumption and extraordinarily high processing capability in tiny form function chips.
  3. Smart Feedback Mechanisms. By smart we mean making decisions that are faster or more consistently correct than a human could make.  Some of this will be mechanical responses like detecting a machine problem and either shutting it down or modifying its behavior.  It’s the human side of this however where your focus should be.

 

The IoT Ecosystem

Enough folks in data science, business, and in the investment community have explained the universe of IoT that we now have pretty good agreement on what that looks like.  Specifically the one graphic you’ll see over and over is this one from Goldman Sachs.

 

It’s not clear why they represented these as off center concentric circles or why ‘people’ is the smallest circle but it will serve as a starting point.

When I see these opportunities however, I see a bigger and much more fundamental split, between IoT applied to things, and IoT applied to people which brings us back to my third point above about focusing on human feedback mechanisms

 

Sensors on Things

Five of the six rings in the Goldman Sachs diagram relate to sensors on things, be they airplanes, streets, machinery, or cars.  IoT came into being on machinery long before Hadoop. 

Through the 80s and 90s the biggest industrials, utilities, and oil and gas companies implemented SCADA systems (supervisory control and data acquisition) that connected machines and produced data on which early optimization and preventive maintenance models were built.

It should come as no surprise then that the large industrial enterprises (GE, Exxon, Caterpillar, and the automotive manufacturers among others) were locked and loaded when Hadoop and stream processing took the binders off these early systems.

My personal observation is that it will be very difficult for venture funded companies to compete in the ‘sensors on things’ world.  First this is true because companies like GE have reframed themselves as data and analytic companies specifically to attack these opportunities with massive resources. 

Second, because even where venture-sized companies have achieved some success, and I’m thinking NEST and some of the home security and video surveillance startups, they have quickly become acquisitions for the majors. 

Perhaps being quickly acquired isn’t too bad a deal for entrepreneurs, but it’s likely those founders are now on long-term earn out contracts as full time employees of large corporations.  That may not be exactly what those entrepreneurs had envisioned.

 

The Goldmine in Human Wearables

The greatest area of opportunity, diversity, investment, and technological advance is in the application of sensors on humans and the manner in which we provide our IoT feedback to the human user.  It’s the resulting action, the feedback, after all that is the actual value in IoT.

Where human wearables are concerned our current feedback mechanisms are pretty archaic.  For the most part they are visual displays of numbers or diagrams or simple text messages.  Here are two specific directions where big opportunities lie by incorporating vastly smarter and improved feedback devices.

 

Enterprise Augmented Reality

Although human wearables immediately brings to mind the consumer market, there is substantial opportunity in enterprise applications.  The first of many ‘smart glasses’ are already in use.  Some will look like Google Glass; some more like this DAQRI Smart Helmet.  In this scenario the heads up display is assisting this worker in guiding a robotic welder.  The operator is being coached by a remote expert in exactly where and how to make the weld using cameras and heads up displays.

 

Another current application that is truly augmented reality is the overlay of schematics or instructions via the heads up display onto the actual piece of tech equipment that is being serviced.  This is the HP MyRoom Virtual Assistant used with Google Glass.

 

What should jump out at you is that neither of these human feedback mechanisms use real artificial intelligence, the first using a remote human expert and the second using a static database of visual overlays and instructions.

It should be a small step for some smart company to wed the image/video and text/speech processing capabilities of CNNs and RNNs (transferred onto a chip within the heads up display) plus a decision tree of diagnostic and corrective actions to autonomously guide the technician in real time as he first makes an action that is evaluated by the IoT system, then guided to the next most appropriate action based on the results of the first.

 

Human Wearables that Change Behavior

On the consumer side, IoT systems shouldn’t just report facts for us to interpret.  They should actually be acting to change our behavior.  In August we wrote in ‘These IoT Sensors Want to Know How You Feel – And Maybe Even Change Your Mood’ about a driving game that uses sensors in the controller plus your performance in the game to decide if you are comfortable with the degree of difficulty the game is presenting.  The goal is to keep us playing the game.  If it’s too easy or too hard we’re likely to quit.  So if the algorithms decide we’re having a hard time it will adjust the game settings to make it a little easier, and conversely a little harder if it seems too easy.

 

Another example; it’s possible to use the rate, steadiness, and frequency of key strokes on your keyboard to determine something about your mood, and a small step to use that feedback to present you with information or an environment that that would calm or excite you.

And still another.  My car already has a function where it tries to determine if I am getting drowsy by some combination steering inputs and other factors.  If it decides I am too drowsy it will alert me and may not let me reengage the cruise control unless I take specific actions.

In retail and ecommerce it’s widely known that mood influences buying behavior and our openness to new experiences or products.  Sensors that could detect mood could be human wearable or could use non-contact sensors like cameras and microphones.

IoT systems combined with wearable sensors and the natural contact objects in our environment like keyboards and automobile controls can take direct action to change or at least directly influence our behavior.

If you’re looking for your next venture idea, try one of these two.

 

About the author:  Bill Vorhies is Editorial Director for Data Science Central.and has practiced as a data scientist and commercial predictive modeler since 2001.  He can be reached at:

[email protected]

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IoT Central Digest, November 16, 2016

Very few people predicted the results of last week's U.S. Elections. The surprise, and for many shock, has put the technology industry on notice as President Elect Trump's administration has outlined a different approach for issues surrounding the technology industry, i.e. trade, hiring and Net Neutrality.

I'm not going to make predictions of what happens next in Washington, but I am going to extend an invitation to all our members and friends to send your predictions for what happens in IoT in 2017. For inspiration grab a crystal ball, or review our 50 Predictions for the Internet of Things in 2016. Send your predictions directly to me with a message here. I will compile the best predictions and publish next month. 

In the meantime, please enjoy this edition of IoT Central Digest. Our contributors and guests cover patent law (great read by the way) and provide super useful lists of IoT organizations, standards and protocols. 

If you're interested in being featured, we always welcome your contributions on all things IoT Infrastructure, IoT Application Development, IoT Data and IoT Security, and more. All members can post on IoT Central. Consider contributing today. Our guidelines are here.

Internet-of-Things Patents: Tough to Enforce?

Guest post by Kenie Ho and Charles Huang

Companies, like IBM, Intel, and Qualcomm, recognize patents as potent business tools because they can use them to keep competitors out of a market or obtain lucrative licensing royalties by allowing the competitors to practice the inventions. In 2015 alone, these companies each applied for and obtained several thousand new U.S. patents, many on IoT-related inventions. Experts estimate that more than 20,000 patents and patent applications covering IoT technologies exist world-wide.

But are IoT patents truly valuable?

IoT Standards / Organizations

The IoT communication protocols

Guest post by James Stansberry

A fascinating article from Philip N. Howard at George Washington University asserts that based on multiple sources, the number of connected devices surpassed the number of people on the planet in 2014. Further, it estimates that by 2020 we will be approaching 50 billion devices on the Internet of Things (IoT). In other words, while humans will continue to connect their devices to the web in greater numbers, a bigger explosion will come from “things” connecting to the web that weren’t before, or which didn’t exist, or which now use their connection as more of a core feature.  The question is, how will these billions of things communicate between the end node, the cloud, and the service provider?

Is it possible to democratize the Internet of Things?

Possibly be the US technology companies the most commonly use the word “democratization” as a marketing and sales argument. Influenced perhaps by the famous quote of President Abraham Lincoln "Democracy is the Government of the people, by the people, for the people”, US Tech companies have been abusing of the term to sell more. I wondering if their intentions are closest to the no less famous Oscar Wilde´s sentence “Democracy means simply the bludgeoning of the people by the people for the people.”

7 things that are getting smarter in IoT era

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7 things that are getting smarter in IoT era

Internet of Things is surrounded with a lot of buzz, which is there for a reason. It is one of the most revolutionary technologies and it is the closest we’ve come to predicting our future. Of course, the IoT is not based on spells and witchcraft (it’s way scarier than that), but on machine-to-machine communication, cloud computing and networks of small sensors, which collect and analyze data. In this article we’ll share some of things and processes that will change in the IoT Era.

Home security systems

Today you can monitor home security cameras from your smartphone screen. More advanced home security systems go even further. They come with different types of sensors that control air quality, motion, sound, vibration and temperature. These systems use machine learning to determine the normal activity in your home and they send alerts to your smartphone, when something out of the ordinary occurs. Because of their smart machine learning approach, home security systems that are based on IoT concept drastically reduce the incidence of false alarms.

Bed

Even our beds will become smart. At the moment you can buy several types of sleep trackers from the ones that come in the form of bracelet and measure your heart rate and blood pressure to smart mattresses that can connect to home automation systems, prepare your bed temperature, track your heart and breathing rate and wake you up in the morning. These special mattresses also collect information about your sleep and give you recommendations for improving your bed rest.

Energy use

Recently several companies released Wi-Fi enabled sensors that can connect to the home electrical panel and control and track your energy use. These small sensors recognize all appliances and gadgets by their “power signatures” and can monitor the energy use and brake it down to every single device. They will allow you to have a deep look into your monthly energy use, to recognize and deal with critical points and to save money on utility bills. Same as many other home security and home automation systems, these sensors learn to interpret the activity of your home devices and send warnings when incidents happen.

All home appliances and systems

All-in-one smart home automation systems can control several home appliances at once. People can use them to turn their porch lights on and off when they are on vacation and to preheat their home or their oven before they arrive home from work. These systems also control various conditions in your home and use smart sensors and machine learning to create the perfect comfort. Some home automation systems also come with a Bluetooth speaker and a microphone and they can work as voice assistants.

Self-storage monitoring

Self-storage monitoring protects stored goods from climate changes, theft and other unforeseen incidents. New storage monitoring systems based on the IoT concept control storage lighting, air-conditioning and security. They also use sensors to track variables that are critical for perishable goods like temperature and humidity. You can find these smart storages in many different cities around the world. 

Construction sites

Construction site managers can use IoT solutions to monitor the work of heavy machinery and the movement of construction employees. This basically means that they don’t need to leave their trailer office. Sensors track the movement of supply and dumping trucks through geo-location technology and insure that everything works as scheduled. If there’re any irregularities in the work of heavy machinery, supply trucks or employees, the site manager will be instantly notified by smartphone push-notification.  

Emergency vehicles

In many cities the only connection between emergency vehicles and their headquarters is established through old-fashion radios. This offers a limited control in emergency situations. Advanced telematics already appeared in many emergency vehicles around the world. This technology allows lone drivers to receive updates in real time from the environment they are entering, including: over speeding, harsh events or the incidents of other team members. Employees at the headquarters also receive the information about emergency vehicle’s hours of service, speed, siren state and location. This way, they can easily schedule vehicle’s regular maintenance and minimize its downtime.

Internet of Things is the biggest tech trend that is happening at the moment. It will completely rock our world and bring a lot of positive disruption to every segment of our lives. Soon, we’ll be able to control all of our possessions through one smart app, which will leave us more time to focus on ourselves and our friends and family.

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Internet-of-Things Patents: Tough to Enforce?

Guest post by Kenie Ho and Charles Huang

You might be riding to work in a driverless car without ever having to look up from your text messages. Or you might rely on weather forecasts derived from micro-weather patterns using the barometric sensor of every iPad inside a local area. These kinds of IoT miracles will use dozens or even thousands of IoT devices. That creates challenges for a company trying to protect its IoT innovations, or for a company trying to avoid infringing someone else’s protected technology.

Businesses typically protect their R&D through patents. A patent allows an inventor to exclude others from making, using, selling, or importing a patented invention.

Companies, like IBM, Intel, and Qualcomm, recognize patents as potent business tools because they can use them to keep competitors out of a market or obtain lucrative licensing royalties by allowing the competitors to practice the inventions. In 2015 alone, these companies each applied for and obtained several thousand new U.S. patents, many on IoT-related inventions. Experts estimate that more than 20,000 patents and patent applications covering IoT technologies exist world-wide.

But are IoT patents truly valuable?

I Want to Sue You, But I Can’t

At its core, IoT is basically a massively distributed network. IoT devices across the world work together to implement creative IoT solutions. Because of that, it can be challenging to obtain patents that are useful against competitors in the IoT space.

To prove infringement in the United States, a patent owner must show a single entity infringes the invention claimed in the patent. In an IoT ecosystem where many devices and actors must interact to implement a use case, it can be difficult to meet that requirement. Often times, no single entity implements or uses the entire claimed invention.

For example, in the autonomous-driving scenario, smart cars might have IoT-enabled sensors reporting on the vehicle’s position, nearby obstacles, speed, and vehicle status. IoT sensors embedded on a smart highway, in a smart city’s traffic-control system, and around a smart parking lot might provide additional information for routing and safety. These devices might communicate with each other to pick up commuters, drive them to work, drop them off, and then park their cars—all without them lifting a finger.

The problem is that different entities own or manufacture each component in this scenario. And if a patent owner has a patent covering this situation, who can it sue for infringement? The smart-car manufacturer? The county maintaining the smart highway? The city with its IoT-enabled traffic system? The owners of the smart parking lot? Depending on how the patent was prepared, the patent owner might be able to sue some, none, or all of them.

Divided Infringement

If you have never read a patent, consider yourself lucky. It is an arcane combination of technical writing and legalese that will put all but the most stalwart patent attorney to sleep. And the most arcane section of the patent—called the “claims”—happens to be the most important because it describes what the inventor is actually claiming as the invention.

Under U.S. law, an entity infringes a patent only if it practices or uses the invention described in the claims. Without getting into all of the legal details and numerous exceptions, if an entity practices or uses only a portion of the invention described in the claims, it is typically not liable for infringement.

In the autonomous-driving case, if a patent claims the combination of using a smart car, a smart highway, a smart traffic-control system, and a smart parking lot, then an entity that practices or uses all of them in combination is liable for infringement. But if there are multiple entities acting in concert, and each practices or uses only a part of the claimed combination, then a “divided infringement” situation exists and the patent owner might not be able to sue any of them for infringement.

For these and other reasons, patent attorneys consider it a best practice to procure patents with claims targeting the actions of individual entities. A well-designed portfolio of patents might include (1) a patent directed to the smart car made by the manufacturer, (2) a patent on the smart highway maintained by the county, (3) a patent on the smart traffic-control system owned by the city, and (4) a patent on the smart parking lot run by the parking company. The patent owner would then have a portfolio of patents to choose from when deciding whom to sue (e.g., the car manufacturer, county government, city, or parking company, respectively)—preferably the entity with the deepest pockets.

But what happens if the novelty in the invention comes from the combination of all the “smart” elements, and the patent office will issue only a patent claiming the combination? Enforcing this kind of patent in a divided-infringement situation is much harder, but still possible.

In 2015, the U.S. Court of Appeals for the Federal Circuit—the highest court overseeing U.S. patent cases besides the U.S. Supreme Court—explained that an entity can still be held liable for patent infringement if it controls or directs multiple entities to jointly use a patented invention. That is, an entity would be liable for divided infringement if the acts of the other entities can be attributed to the first entity.

For example, if a smart-car manufacturer has a contractual relationship obligating other entities to embed and use IoT sensors on the highway, in the traffic-control system, and around the parking lot to implement the autonomous-driving use case, then the smart-car manufacturer could be found liable for infringing a patent claiming the combination. But unless the patent owner can show this type of control or joint enterprise, it will likely not be able to prove infringement for that combination patent.

Territorial Scope

Besides divided infringement, another obstacle facing IoT patents is territorial scope. A U.S. patent grants rights in the United States. Thus, a U.S. patent presumptively does not confer any protection to infringing acts outside of the United States. This poses a problem for IoT patents because many IoT use cases employ devices located outside of the United States.

For instance, in the autonomous-driving scenario, sensor data from a smart car might be routed to a server located in Canada—because it might be cheaper there—for routing and map updates before being sent back to the car. A U.S. patent claiming a “process” for autonomous driving that includes routing and updating maps would generally not be enforceable here because those routing and updating steps take place outside of the United States. But due to patent policy set by the government and U.S. courts, a U.S. patent claiming an autonomous driving “system” might be enforceable if the Canadian server was being controlled in the United States. The differences between the policy reasons for the two are beyond the scope of this article. The point is that territorial scope of a patent matters, particularly for IoT applications.

Good Patents, Big Consequences

A good patent that avoids the above problems and covers a competitor’s IoT products provides a big competitive advantage, especially if the competitor cannot design around the patent. Further, if the competitor had full knowledge of its infringing activities and had no reason to doubt the patent’s validity, but nonetheless continued with its infringing activity, it may be liable for willfully infringing the patent, an act that can triple the amount of actual damages.

The U.S. Supreme Court recently changed the law to make willful infringement easier to prove. Before the change, a patent owner needed to show, by clear and convincing evidence, that the accused infringer was reckless in infringing the patent and knew or should have known its infringing actions were reckless. Now, the patent owner needs to show by a preponderance of evidence—a lower standard—only that the infringement was “egregious” and not just simply a “garden-variety” infringement case.

In the past, if a company became concerned about a patent, it would seek a patent attorney’s opinion on the matter to avoid liability based on willful infringement. That practice went out of favor in the mid-2000s after the courts raised the standard for proving willful infringement. Now, with the lowering of the standard, that practice has enjoyed a revival if only to show that the company took due care in investigating the matter to reduce the likelihood of willful infringement and treble damages.

Strategic Patenting

Despite the divided-infringement and territorial-scope issues, thousands of patents on IoT-related technologies are being issued each year. The key is to make sure to get patents that are well thought out to avoid divided-infringement and territorial-scope issues in the first place.

On average, it takes over 2 years to obtain a patent and most patents have a term of 20 years. It might be 10 or 15 years before the patent owner asserts the patent. How the market uses the invention can change significantly during that time. Thus, a patent applicant must carefully predict and anticipate likely infringement scenarios when protecting its IoT technology.

Authors’ Bio

Kenie Ho has litigated over 50 patents in U.S. courts on electrical and consumer-electronics technology. He is a thought leader on intellectual-property issues for IoT and leads the IoT Legal Group at Finnegan, Henderson, Farabow, Garrett & Dunner, LLP.

Charles Huang prepares patent applications for IoT patents. His practice includes litigation, client-counseling, patent portfolio management, and patent analysis.

 

Patent Photo Credit to Nick Normal via Flickr.

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