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Case Studies (224)

IoT Central Digest, October 17, 2016

Ever wonder what people REALLY do with the Internet of Things and Big Data? Join us on November 3, 2016 to find out. I'm hosting a webinar with John Myers of Enterprise Management Associates and Dan Graham of Teradata where we look at real world implementations. Registration information is here.

This week's newsletter has new contributor B Jansen looking at IoT Programming languages. I also cover his very useful Interactive Map of IoT Organizations (people in business development this is for you!). Mark Niemann-Ross, also a new contributor, looks at why we're going to need sophisticated device management, Ajit Jaokar guest blogs about the AI layer for the enterprise and the role of IoT, Bill McCabe on the moves of IBM, and Sandeep Raut pens a piece on data science for predictive maintenance. I also include an industry call to action: government intervention is needed for the IoT.

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.

IoT Programming Languages

Posted by B Jansen

I began collecting information on various home automation hubs, industrial IoT Platforms, hardware solutions, software technologies, and variety of different “things”. All of the data I collated into what I am calling my “Thing of Things” (ToT) database. 

I currently have 8,821 data points across 541 organizations, 532 product lines, and 63 countries. A large number of the organizations have formed over the past 6 years. If you are interested in getting into IoT, this could help guide you on which language(s) to learn.

The Internet of Us

Posted by Mark Niemann-Ross

We are going to have devices using low-power, short-range networks to communicate with other devices. This type of communications will require adaptive and flexible methods. This is going to require sophisticated device management.

We Need to Save the Internet from the Internet of Things

Posted by David Oro 

Over on MotherBoard, noted cryptographer, computer security and privacy specialist, and writer, Bruce Schneier pens his thoughts on the recent gaping holes in security for Internet connected devices. When Bruce speaks, people listen. First, if you haven't been following the recent DDoS attacks using IoT devices, read this. In short, IoT devices have been comprised to attack networks. It's so bad that Bruce is calling out the IoT market for failing to secure their devices and machines that connect to the Internet and is asking for government intervention.

The AI layer for the Enterprise and the role of IoT

Guest blog post by Ajit Jaokar

According to Deloitte: by the “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”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

Interactive Map of IoT Organizations 

Posted by David Oro

Here's a map that shows the location of the headquarters of organizations around IoT including standards bodies, manufacturers of Things, IoT Platform companies, etc. On the map you can click on a category on the left to highlight the organizations in that category. Or zoom in to see the areas where IoT organizations are near you.

Big Blue/ On the way back And Still Crazy about IOT ??

Posted by Bill McCabe 

There have been some interesting developments for Big Blue in the IOT space recently. Last time we reported on them, we were monitoring analysts’ worries about the semiconductor business and other divestures late last year. This year, it seems clear IBM is poised to create even more profitable opportunities in our IOT space. Let’s check in and see where they are.

Using Data Science for Predictive Maintenance

Posted by Sandeep raut

Remember few years ago there were two recall announcements from National Highway Traffic Safety Administration for GM & Tesla – both related to problems that could cause fires. These caused tons of money to resolve. Aerospace, Rail industry, Equipment manufacturers and Auto makers often face this challenge of ensuring maximum availability of critical assembly line systems, keeping those assets in good working order, while simultaneously minimizing the cost of maintenance and time based or count based repairs. Identification of root causes of faults and failures must also happen without the need for a lab or testing. As more vehicles/industrial equipment and assembly robots begin to communicate their current status to a central server, detection of faults becomes more easy and practical.

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What People REALLY Do with the Internet of Things and Big Data

Join us for the latest IoTC Webinar on November 3rd, 2016
register-now
Space is limited.
Reserve your Webinar seat now
 
Are you developing a winning Internet of Things (IoT) strategy? Or are you being outflanked by the competition again? IoT is a huge market expansion that will hit $14 trillion by 2020. A lot of that is in your industry. The Internet of Things market expansion is a chance to get out in front of the competition. Sadly, some will take a wait and see approach on IoT until others take the lead. A robust IoT initiative can move your company from the sidelines to market leadership. And all this means big data is getting a lot bigger.

This IoTCentral Webinar digs deep into real world implementations. Experts will discuss the IoT research results from clients with hands-on implementations. It all starts with the business drivers that lead to actual projects. Later the focus shifts to technical drivers and the implications. Real implementations illustrate the value of analytics. Come find out what happens when big data meets the Internet of Things.

Attendees will learn: 
  • The business drivers of end-user organizations implementing IoT
  • Who are the champions driving IoT initiatives? Hint: It’s not IT
  • Popular devices being monitored with sensor data
  • Discover which analytics are applied to sensor data
  • Which analytical platforms are supporting IoT initiatives
  • How many organizations are already on their second IoT project

Speakers:
John L Myers, Managing Research Director of Analytics -- Enterprise Management Associates
Dan Graham, Director of Technical Marketing -- Teradata

Hosted by: David Oro, Editorial Director -- IoT Central
 
Title:  What People REALLY Do with the Internet of Things and Big Data
Date:  Thursday, November 3rd, 2016
Time:  8 AM - 9 AM PDT
 
Again, Space is limited so please register early:
Reserve your Webinar seat now
 
After registering you will receive a confirmation email containing information about joining the Webinar.
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Interactive Map of IoT Organizations

Here's a map that shows the location of the headquarters of organizations around IoT including standards bodies, manufacturers of Things, IoT Platform companies, etc.

On the map you can click on a category on the left to highlight the organizations in that category. Or zoom in to see the areas where IoT organizations are near you.

This was found over at the The Pointy Haired Manager and the author says he's tracked 246 organizations, 59% (144) of them are based in the U.S.A. and 26% of them are based in California (63). This graph shows the locations of IoT companies in the U.S.A with the exception of California.

Update: The creator updated his map which can be found here.

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Originally posted on Data Science Central

 Printed electronics are being vouched as the next best thing in Internet of Things (IoT), the technology that is rightly regarded as a boon of advancing technology. Silicon-based sensors are the first that have been associated with IoT technology. These sensors have numerous applications, such as track data from airplane, wind turbines, engines, and medical devices, amongst other internet connected devices.

However, these silicon-based are not suitable for several other applications. Bendable packaging and premium items are some of the application where embedded sensors do not work. For such applications, printed electronics befit the need. Using sensor technology, information is transferred on smart labels that can be attached to packages to be tracked in real time.

Some Applications of Printed Sensor Technology

Grocery Industry: While bar code is the standard technology used in the grocery sector, the technology has limitations pertaining to the data it can store. Also, for some products, product packaging can run up to 30-40% of the cost, for which printed sensor are best-suited to save packaging costs. For such needs, a printed sensor is the most apt solution for real-time information about a product’s temperature, moisture, location, movement, and much more. Companies can check these parameters to validate the freshness and prevent substantial spoilage. Smart labels are also used to validate the authenticity of products.

Click here to get report.

Healthcare: The use of smart labels enables manufacturers and logistics firms to track the usage and disposal of pharmaceuticals and to control inventory. The use of smart labels on patients’ clothing enables to check their body temperature, dampness of adult diapers, or bandages for assisted living scenarios.

Logistics: Radio frequency identification (RFID) was the standard tag used by logistics companies until recently to identify shipping crates that carried perishable products. RFID is increasingly being replaced by smart labels that enable tracking of individual items. This facilitates companies to track products at the item level rather than at the container shipping level.

Biosensors Lead Printed and Flexible Sensors Market

As per the research study, the global market for printed and flexible sensors is estimated to grow at a fast pace, due to which several investors are interested in pouring funds into the market. This is expected to create potential opportunities for commercialization and product innovation. In addition, several new players are also projected to participate in order to gain a competitive advantage in the market. In 2013, the global printed and flexible sensors market stood at US$6.28 bn and is projected to be worth US$7.51 bn by the end of 2020. The market is expected to register a healthy 2.50% CAGR between 2012 and 2020, as per the study.

The rapid growth in individual application segments and several benefits over the conventional sensors are some of the key factors driving the global market for printed and flexible sensors. In addition, the developing global market for Internet of Things is further anticipated to fuel the growth of the market in the next few years. On the flip side, several challenges in conductive ink printing are estimated to hamper the growth of the market for printed and flexible sensors in the near future.

Biosensors are most extensively used with the largest market share in the global market for printed and flexible sensors. Glucose strips incorporated with a biosensor are one of the most sought after ways to track and monitor glucose levels among diabetics. Thus, it accounts as a multi-billion dollar segment in the global market for printed and flexible sensors. To evaluate and monitor working of the heart, kidney diseases, and cancer are the other emerging applications where printed biosensors technology is being utilized.

The expanding automobile industry holds promise for piezoelectric type printed flexible sensors for performance testing during production. Due to these varied applications of printed and flexible sensors, the global market for printed and flexible sensors will expand at a slow but steady 2.5% CAGR in the next six years starting from 2012.

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Guest blog post by Ajit Jaokar

Introduction 

According to Deloitte: by the “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”. Gartner also predicts that 40 percent of the new investment made by enterprises will be in predictive analytics by 2020. AI is moving fast into the Enterprise and AI developments can create value for the Enterprise. This value can be captured/visualized by considering an ‘Enterprise AI layer’. This AI layer is focussed on solving relatively mundane problems which are domain specific.  While this is not as ‘sexy’ as the original vision of AI, it provides tangible benefits to companies.

 

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem. The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

 

Enterprise AI – an Intelligent Data Warehouse/ERP system?

AI enables computers to do some things better than humans especially when it comes to finding insights from large amounts of Unstructured or semi-structured data. Technologies like Machine learning , Natural language processing (NLP) , Speech recognition, and computer vision drive the AI layer. More specifically, AI applies to an algorithm which is learning on its own.

 

To understand this, we have to ask ourselves: How do we train a Big Data algorithm?  

There are two ways:

  • Start with the Rules and apply them to Data (Top down) OR
  • Start with the data and find the rules from the Data (Bottom up)

 

The Top-down approach involved writing enough rules for all possible circumstances.  But this approach is obviously limited by the number of rules and by its finite rules base. The Bottom-up approach applies for two cases. Firstly, when rules can be derived from instances of positive and negative examples(SPAM /NO SPAN). This is traditional machine learning when the Algorithm can  be trained.  But, the more extreme case is : Where there are no examples to train the algorithm.

 

What do we mean by ‘no examples’?

 

a)      There is no schema

b)      Linearity(sequence) and hierarchy is not known

c)      The  output is not known(non-deterministic)

d)     Problem domain is not finite

 

Hence, this is not an easy problem to solve. However, there is a payoff in the enterprise if AI algorithms can be created to learn and self-train manual, repetitive tasks – especially when the tasks involve both structured and unstructured data.

 

How can we visualize the AI layer?

One simple way is to think of it as an ‘Intelligent Data warehouse’ i.e. an extension to either the Data warehouse or the ERP system

 

For instance,  an organization would transcribe call centre agents’ interactions with customers create a more intelligent workflow, bot etc using Deep learning algorithms.

Enterprise AI layer – What it mean to the Enterprise

So, if we imagine such a conceptual AI layer for the enterprise, what does it mean in terms of new services that can be offered?  Here are some examples

  • Bots : Bots are a great example of the use of AI to automate repetitive tasks like scheduling meetings. Bots are often the starting point of engagement for AI especially in Retail and Financial services
  • Inferring from textual/voice narrative:  Security applications to detect suspicious behaviour, Algorithms that  can draw connections between how patients describe their symptoms etc
  • Detecting patterns from vast amounts of data: Using log files to predict future failures, predicting cyberseurity attacks etc
  • Creating a knowledge base from large datasets: for example an AI program that can read all of Wikipedia or Github.
  • Creating content on scale: Using Robots to replace Writers or even to compose Pop songs
  • Predicting future workflows: Using existing patterns to predict future workflows
  • Mass personalization:  in advertising
  • Video and image analytics: Collision Avoidance for Drones, Autonomous vehicles, Agricultural Crop Health Analysis etc

 

These  applications provide competitive advantage, Differentiation, Customer loyalty and  mass personalization. They have simple business models (such as deployed as premium features /new products /cost reduction )

 

The Enterprise AI layer and IoT

 

So, the final question is: What does the Enterprise layer mean for IoT?

 

IoT has tremendous potential but faces an inherent problem. Currently, IoT is implemented in verticals/ silos and these silos do not talk to each other. To realize the full potential of IoT, an over-arching layer above individual verticals could ‘connect the dots’. Coming from the Telco industry, these ideas are not new i.e. the winners of the mobile/Telco ecosystem were iPhone and Android – which succeeded in doing exactly that.

 

Firstly, the AI layer could help in deriving actionable insights from billions of data points which come from IoT devices across verticals. This is the obvious benefit as IoT data from various verticals can act as an input to the AI layer.  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 to 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 and is already happening, 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

 

We can extend the idea of ‘machines teaching other machines’ more generically within the Enterprise. 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.  We see early examples of this approach in Salesforce.com and Einstein. Longer term, Reinforcement learning is the key technology that drives IoT and AI layer for the Enterprise – but initially any technologies that implement self learning algorithms would help for this task

Conclusion

In this brief article, we proposed a logical concept called the AI layer for the Enterprise.  We could see such a layer as an extension to the Data Warehouse or the ERP system. This has tangible and practical benefits for the Enterprise with a clear business model. The AI layer could also incorporate the IoT datasets and unite the disparate ecosystem.  This will not be easy. But it is worth it because the payoffs for creating such an AI layer around the Enterprise are huge! The Enterprise AI layer theme is a key part of the Data Science for Internet of Things course. Only a last few places remain for this course!.

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IoT Central Digest, October 1, 2016

Happy last quarter of 2016 and welcome new members! If you haven't been paying attention, IoT is having its moment in security, and it's not good. Andrew Hickey of A10 Networks gets you up to speed on this still developing story. Also in this edition uber-IoT recruiting guru and regular contributor Bill McCabe has a five point plan for hiring in IoT, Ben Dickson is back with a look at greenfield vs. brownfield development in IoT, Phillip Tracy has the top five use cases for the Industrial IoT, and finally Ajit Jaokar gives us a look at GE's much-hyped IIoT platform Predix. 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.

IoT Devices Common Thread in Colossal DDoS Attacks

A pair of distributed denial-of-service (DDoS) attacks against high-profile targets last week rank among the largest DDoS attacks on record. And a common thread has emerged: these attacks are leveraging botnets comprising hundreds of thousands of unsecured Internet of Things (IoT) devices.

What is the difference between greenfield and brownfield IoT development?

By Ben Dickson 

The Internet of Things (IoT) is one of the most exciting phenomena of the tech industry these days. But there seems to be a lot of confusion surrounding it as well. Some think about IoT merely as creating new internet-connected devices, while others are more focused on creating value through adding connectivity and smarts to what already exists out there. I would argue that the former is an oversimplification of the IoT concept, though it accounts for the most common approach that startups take toward entering the industry. It’s what we call greenfield development, as opposed to the latter approach, which is called brownfield. Here’s what you need to know about greenfield and brownfield development, their differences, the challenges, and where the right balance stands.

The 5 Point Plan for IOT Recruitment

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Guest blog post by Mehul Nayak

Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages.

Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. Yes, that’s right! Companies will be successful in analyzing large complex data and providing meticulous insights without spending a huge amount on installing and maintaining machine learning systems.

The three newest machine learning trends that will make this possible are Data Flywheels, The Algorithm Economy, and Cloud Hosted Intelligence. In the coming years, every application built will be an intelligent app by incorporating open source algorithms and machine learning codes. Let’s see how these trends will reshape the cloud industry, data handling and everything that’s digital.

Data Flywheels- See the future’s ruler!

Data is anticipated to be the ruler of the digital world in the coming years. It is observed that the world’s data doubles every 18 months while the cost of cloud storage decreases at almost the same rate, which suggests that data will be available in abundance after a few years.

This availability of high amount of data will open the doors of better and extensive machine learning experiments as well as deployment. With the use of the improved machine learning services we will be able to get a hold on more refined data. Ultimately the users of these services will increase which will give us more data. This data flywheel will keep on rolling and expanding.

For instance, Tesla’s data flywheel is planning to release a self-driving car by 2018, and for that project they have collected a massive driving data of 780 million miles and are adding a new million within every tenth hour.

This extravagant capacity of data collection at a very low cost than before will enforce people to use cloud technology primarily. Machine learning algorithms will get an economic market to flourish in the coming years.

The Algorithm Economy- Every industry will be a smart industry!

If there is an abundance of data, but there is no way of manipulating it or generating insights from it, then what’s the use, right? With massive data generation using flywheels, there will be an economy created for algorithms, like a marketplace for algorithms. The engineers, data scientists, organizations, etc. will be sharing algorithms for using the data to extract required information set.

Business owners from different sectors will be able to receive insights in seconds by sending their data directly to the algorithm marketplace. You can also buy algorithms you need for your data research and deploy it to manipulate data and get insights. Making every app and each business smart is absolutely possible with this concept.

Cloud Hosted Intelligence- Intelligence on rent!

Imagine the troubles you may have to go through to create an AI for your own business. Can’t even imagine the hard work and intelligence required, can you? There are obvious options such as approaching Machine Learning service providers for your needs, but the future will be rather different. You will be able to use the cloud hosted AI such as deep learning, Google’s machine learning venture. This is the coolest machine learning trend followed currently.

This advancement will be an efficient cost cutting method as the companies will not need to deploy AI for their business. Analytics and data science will be easier than it ever was. Getting accurate results, faster mining and generation of new models will be possible with the help of cloud-based intelligence.

The smarter tomorrow of manufacturing industry using AI

IoT has already delivered business value to the manufacturing industry through various use cases such as Remote Asset Monitoring, Logistics and Supply Chain, Predictive Maintenance, etc. However, the future is far more interesting than the current scenario of AI deployment in manufacturing.

The most promising factor of AI in the manufacturing industry is automotive production using robots. We are pacing towards a highly robotic industry where assembling of products and packaging of shipments will be handled by Artificial Intelligence. Currently, most of the AI technologies need human support and supervision, which is expected to change in the coming future.

The tomorrow of healthcare industry is in safe hands of AI

Healthcare is the most influenced enterprise by artificial intelligence. IT companies have already started developing AI applications that can track the health of employees or monitor senior citizens’ health remotely from quite some time. However, the future of AI in healthcare is unbelievably hopeful.

IBM Watson has been deployed by a number of medical organizations to help doctors provide intense care to their patients. However this is the current scenario of cognitive computing, the future has a bigger picture. The coming age of artificial intelligence will include mining of medical records to provide better and faster health services.

The most promising example of it is Google’s DeepMind Health Project. The AI research branch of Google has developed this project to collect the medical data, normalize it, and trace its lineage. Being in its initial phase, the DeepMind project is helping the Moorfields Eye Hospital to improve eye treatment.

Following IBM’s Watson and Google’s DeepMind, Microsoft, Dell and Hewlett-Packard are setting their mark in the healthcare industry and analysts predict that 30% of the providers will run cognitive analysis on patient data by 2018.

The future of construction business is secure with Artificial Intelligence

Artificial intelligence has changed the world and will continue to do so being an integral part of Industry 4.0. The construction industry will be affected positively by the deployment of automation. AI can help in saving a lot of money if there is a smarter option available in determining the expenditures on materials, choosing the perfect engineering companies and so on.

Autonomous TMA truck is a fascinating development of artificial intelligence in construction. This truck can function efficiently without the presence of a driver which suggests that for his safety, the driver could remove himself from the truck if any dangerous situation comes up. ATMA trick is equipped with the electro-mechanical system and the fully integrated sensor suite that enables choosing the leader or follower truck. This truck is being used in road construction and will change the scenario of the construction industry in the coming years.

The old talk of 3-D and 4-D will soon be or rather is replaced by the next generation 5-D building information modeling. This is a five dimensional representation of functional as well as physical characteristics of a construction project. All the important aspects such as geometry specifications, aesthetics, thermal and acoustic properties are taken into account to generate its cost and schedule.

However, this model has already been adopted by many companies. The future of this use case is its integration with augmented reality technology using wearable devices. The combination of 5-D BIM and augmented-reality devices will transform the entire construction industry. This technology will enable you to see through a holographic display and allow you to pin holograms to physical objects. You will be able to interact with data using gestures and voice commands.

The future of AI in retail will be a reality, not virtual reality!

Virtual reality is one of the most emerging uses of Artificial Intelligence in the retail industry currently. You may be able to see the virtual reality headsets in stores with the help of which you can actually see how the product looks. It helps the shoppers in selecting products more easily.

However, the future of AI in retail is the inclusion of chatbots in the retail industry. E-commerce is the most flourishing retail platform which is soon going to transform into conversational commerce with the introduction of chatbots. Utilization of chatbots in the retail sector will enable business owners to provide a personalized shopping experience to their customers. This will definitely help in building a strong customer base. Development of chatbots has already begun, and it’s time for every online retail business to adopt this technology.

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As the Internet of Things becomes more important for companies of all sizes, Information Technology professionals are beginning to seek out roles related to this growing niche. The Internet of Things is built on many of the technologies that professionals are already familiar with. Internet Protocol (IP) experts, hardware engineers, and even GUI designers could find themselves working on IoT projects in companies ranging from startups, to the technology giants that are driving the industry.

If one were to ask; “what kind of field do I need to be in to land a job in IoT?”, the answer would not be simple. IoT works on many layers. Software plays a key role in usability and functionality. Network layers are key to infrastructure, and hardware layers define the capabilities and development opportunities involved in any IoT system. Perhaps a better way to find out what is required of IoT professionals, would be to take information from some of the opportunities that are available in the job market right now.

Take Amazon as an example. Amazon AWS is the online retail giant’s cloud services arm. Cloud systems like Amazon S3 power some of the most widely adopted cloud computing systems in use today. To be considered for a role on a team working within AWS, the qualifications are no different to most IT development roles. A Bachelor’s Degree in Computer Science, professional experience (4+ years is a must), fundamentals in object design, and programming proficiency in a contemporary programming language will at least ensure a candidate’s resume is looked at.

But this doesn’t paint the full picture. Businesses who engage in IoT technologies are businesses who are invested in the future. This means that they’re seeking forward thinking professionals. Meeting the requirements where it comes to academic achievement is only part of what it takes to make it in IoT.

Last year, Forbes published a number of articles on what it would take to make it in the growing IoT industry. According to Forbes, the necessary qualities go beyond academia, and incorporate more soft skills and innovative thought.

High on the list was associative thinking. Collaborators who can integrate varying strategies and concepts were also tipped to be in demand. Finally, professionals who can communicate complex ideas easily through speech, written word, and abstract methods were considered more likely to be successful in the IoT niche than those who were only proficient in their technical field.

Take a look at the job market on any given day, and you will find dozens of IoT related jobs advertised by high profile tech companies. The second quarter of 2015 has seen positions opening at Dell and IBM (Software Development), Verizon (IoT Product Management), and Accenture (IoT Delivery Consultants), to name just a few.

The reason these companies are hiring in IoT is simple; it is the next big thing. Technology firms like Dell and IBM have a vested interest. Their core products and services are built around delivering and facilitating IoT. With companies like Verizon and Accenture, it is more about preparing for the future. IoT will allow Verizon to better deliver the services that they already have. Customer billing and customer experience can be improved by incorporating IoT into the ways that customers can interact with the company, but there’s also the fact that Verizon is a cellular network leader. Their consumer and business devices (i.e. smartphones) are key to incorporating IoT into daily consumer life. Wireless payments, mobile banking, home automation, and sensor interaction can be achieved through smart devices from Verizon. The talent that these companies recruit will be actively involved in designing, maintaining, and delivering IoT in the immediate future.

Although IoT hasn’t completely changed the face of Information Technology, it has created new opportunities for jobseekers in the market. Existing professionals with transferable skills will find new challenges and progression opportunities within the Iot Job Market, and also in smaller companies that are incorporating IoT concepts into manufacturing, packing, logistics, and even medical.

International Data Corporation has predicted that IoT will be a $7 trillion industry by 2020. With growth as fast as it currently is, IoT job market is the perfect platform from where jobseekers can showcase their skills, and where companies can form relationships with the talented professionals who will take them into the future.

For more information please check out our website at

www.internetofthingsrecruiting.com or contact me directly at 303-337-7871

delivering IoT forward thinking professionals IoT Delivery Consultants IoT jobseekers IoT Product Management IoT professionals IoT related jobs

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When considering any new or emerging technology, it can be easy to immediately think of the potential implementation in developed markets. After all, these are the markets where consumers have high purchasing power, and businesses and governments have strong credit lines and funding options. Well, wouldn’t it be a surprise to learn that the developing world will likely be responsible for almost half of all revenue generated by IoT? This is exactly what a 2015 report from the International Telecommunication Union stated, and if you look at trends and innovation around the world, there is evidence that supports the prediction.

Industry Leaders Recognize the Value of IoT in Developing Markets

Take India as an example. Although it is one of the largest countries by area, and the second most populous in the world, it is still considered to be a developing country by leading economists. Even so, there are some areas where India is a leader in IoT. In 2015, IBM selected the Indian city of Vizag as a winner in their Smarter Cities Challenge. This city wants to improve its disaster preparedness and response programs through the use of IoT technologies, and with the help of IBM, the government will work towards implementing a sensor based utility grid, improve citywide electronic communications, and develop an emergency command center that uses historical data and machine sensors to better predict and respond to natural disasters.

This program has the potential to attract foreign investment, create jobs, and save lives.

Markets That are Ideal for IoT Investment

One reason why developing nations are prime for IoT investment is because many of them can make immediate use of IoT technologies for critical applications. In the gridlocked Philippine region of Metro Manila, government agencies are using connected machines to monitor traffic in real time and provide public alerts. The metropolitan area is served by a number of CCTV systems and sensors that can be accessed through APIs, allowing for news stations and privately developed smartphone apps to provide instant updates to the general public.

Safety is also an issue in many developing countries, and again, we can use Metro Manila as an example. The region’s widely utilized MRT rail lines are often overcrowded and sometimes dangerous. With connected technology, members of the public can already access the MRT security CCTV feeds from smartphones and web browsers, allowing them to view real time platform video to help plan their daily commutes.

Perhaps one of the biggest advantages that developing countries have is that they are lacking in some areas of infrastructure. A developing city that now has the funds to invest in widespread water metering will have more incentive to use accurate and efficient machine driven meters. By contrast, a long developed city would have to weigh up the cost savings of an IoT based system, compared to the efficiency of their current metering system.

IoT Infrastructure Can Be Built on Existing Cellular Networks

Despite lack of infrastructure in some areas, LTE penetration is high in a number of developing economies, meaning that there is increased opportunity for bringing IoT services to corporations and the general public. India has LTE penetration throughout more than 50% of the population, which means that there is potential to connect more than half a billion people to the Internet of Things. China, which could be considered still developing in some provinces and cities, boasts LTE coverage across 76% of the mainland. That’s only two points behind the United States, and China has more than four times the population, allowing for massive opportunity in the consumer and public service IoT sectors.

While the developed world is no doubt leading in IoT innovation, developing countries will contribute significantly to revenue, adoption, and investment. With more than $6 trillion in worldwide IoT investment expected by 2020, developers and innovators cannot afford to ignore the world’s developing economies.

For more information please review our new website www.internetofthingsrecruiting.com

iot internet of things

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The Business Bandwagon You Should Never Miss

Digital Transformation is here and that means everyone—IT and non-IT alike—must embrace the disruptions. Automation and modernization is a bandwagon and just letting it pass by is missing a great business opportunity.

Digital Transformation means a wave of technology disruptions taking over vertical and horizontal industries. Disruptive technologies are considered to challenge the status quo and beat the conventional all for the sake of better business efficiency, credibility, sustainability and most importantly, higher chances at succeeding in an ever progressing era where information technology has become Midas—everything it touches turns into gold.

Digital Transformation for the most part promises to make data driven business decisions more accurate, predictive, and extremely reliable compared to traditional tools and processes. This phenomenon in the IT landscape pushes business processes to deliver results at an impressive speed and become more efficient and unified. With the right tools and solutions, and with the proper migration, design, and implementation, Digital Transformation can lead an enterprise towards success.

It is no wonder that most organizations, startups, and high-performing enterprises are taking firmer steps in treading the path towards this phenomenon in high technology. And who wouldn’t take this leap? Apart from providing more informed decisions that aim to get valuable and productive outcomes, Digital Transformation also enables sustainability and agility in most business aspects. It positively affects key areas including customer engagement, finance, unified communications and collaboration, networking, and many others.

The disruptions in IT is not an unknown domain to a good number of people. In the recent Accenture Technology Vision 2016 Survey, it was revealed that there are 58% who say that the pace of technology will change in their industry rapidly. This says a lot about proving that the Digital Transformation is not anymore a mere setting for sci-fi or IT fiction films but is the present reality.

Digital Transformation calls for everyone to beef up IT know how

As back office processes gradually but surely begin to become automated, other roles in an organization such as recruiters, finance officers, and human resources managers are highly encouraged (if not compelled) to add in their skillset some IT know-how. Apparently, in this age of automation, setting up and doing some minor software troubleshooting is no longer the sole responsibility of an IT officer. Though it may not be required for a non-IT professional to have some IT skills among their competencies, it surely is a great advantage to be knowledgeable and capable in IT.

A great example is the demand on expanding the role of a chief finance officer. In an article titled Great Expectations: How the CFO’s Role is Growing, authored by the General Manager for Enterprise Resource Planning (ERP) of Oracle ANZ Thomas Fikentscher, it was revealed that there has become a need for the chief financial officer’s (CFO’s) role to expand and this particularly means that they need to gain some IT capabilities. This is due to the uptake of data analytics in making the processes of finance more efficient and reliable by enhancing it with improved forecasting and decision making.

Meanwhile, the emergence of HCM software and tools also proves that there is a demand from non-ITs to gain skills on automated processes and data analytics. In an annual study titled Sierra-Cedar 2014–2015 HR Systems Survey White Paper, 17th Annual Edition, it was revealed that the adoption of cloud-based SaaS Human Capital Management (HCM) is expected to rise to 58%.

IT Demands

As most non-IT members of an enterprise are encouraged to become adept in various areas of IT concerns, IT professionals become even more vital in many key areas in a company and must always sharpen their skillset themselves. These individuals are not only responsible in making sure that IT tools are working and rolled out. Most importantly, IT decision-makers and leaders are expected to spark knowledge on the latest business software advancements and guide the teams in embracing the disruptions in technology.

Accenture Technology Vision 2016 also confirms such trend when it revealed that 37% of the business and IT executives surveyed reported that “the need to train workforce is significantly more important today compared to three years ago.”

The exceptional talent and brains of IT professionals are much sought-after now than ever as their role becomes challenging in this day and age where office mobility, online banking, business process management tools, and the Internet of Things are further becoming everyday essentials. Due to automation and massive connectivity, much focus and attention are placed upon IT security, applications development, servers, and data center housekeeping (virtually or physically).

The reality that must be embraced now, however, is that IT knowledge and skills even to non-IT pros are highly beneficial in a thriving and progressing enterprise. This will be true as long as companies are becoming more open to modernizing their offices and are willing to cope with the impressive disruptions in information technology.

As long as Digital Transformation is dominating in vertical and horizontal industries, non-IT roles in a company will also have to add some IT professional skills in their competencies.

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Guest blog post by ajit jaokar

Background and Approach

This blog is based on my talk in London at the Re.work Connected City Summit on Deep Learning Applications for Smart cities. The talk is based on a forthcoming paper created with the help of my students atUPM/citysciences on the same theme. Please email me at  ajit.jaokar at futuretext.com  or follow me  @ajitjaokar  for more details.

Here are some notes on our approach:

  • When we speak of Machines – the media dramatizes the issue.  Yet,  city officials and planners plan for ten to twenty years in the future. They will have to consider many of these issues in a pragmatic way.
  • Deep Learning / Artificial Intelligence will impact many aspects of Smart cities. We decided to approach the subject in a pragmatic manner and to explore the impact of Deep Learning/AI technology on the lives of future citizens.

How could self-learning machines affect humanity in cities?

Initially, we started off with the usual Smart City approach i.e. domains such as Security – Transport – Health – Governance – Environment etc

Then, we were inspired by a statement “Man becomes the sex organs of the machine world – the bee of the plant world – enabling machines to evolve ever new forms” – Marshall McLuhan

It indicates that disruptive innovations like Deep Learning and AI cannot be viewed in silos. Instead, we decided to reframe the problem in a more disruptive way by asking the questions;

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens ?

    What are the threats?

    How will the lives of future citizens be impacted through self learning machines?

 

The shortest introduction to Deep learning:

Here is a brief introdcution to Deep Learning.  I have spoken of the Evolution of Deep Learning models and An introduction to Deep Learning and it’s role for future cities

Deep Learning can be seen more as a specific form of Machine Learning that leads to creating Self Learning Machines.  The whole objective of Deep Learning is to solve ‘intuitive’ problems i.e. problems characterized by High dimensionality and no rules.  With Deep learning, Computers can learn from experience but also can understand the world in terms of a hierarchy of concepts – where each concept is defined in terms of simpler concepts. The hierarchy of concepts is built ‘bottom up’ without predefined rules . This is similar to the way a child learns ‘what a dog is’ i.e. by understanding the sub-components of a concept ex  the behavior(barking), shape of the head, the tail, the fur etc and then putting these concepts in one bigger idea i.e. the Dog itself.

More specifically, a form of Deep Learning called Reinforcement Learning is making a huge impact in areas such as AlphaGo. Reinforcement Learning (RL) is based on a system of rewards. RL is a form of unsupervised learning – An RL agent learns by receiving a reward or reinforcement from its environment, without any form of supervision other than its own decision making policy.

In machine learning, the environment is typically formulated as a Markov decision process (MDP) as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). (adapted from wikipedia)

Analysis

Here are the trends we note from the themes noted above. Link sources from Home of AI info and the web

What are machines learning from Data and Observations?

  • New computer program first to recognize sketches more accurately than a human
  • Deep Learning Algorithm ‘Paints’ in the Style of Any Artist it Copies
  • New big data system developed at MIT is more intuitive than humans
  • Artificial intelligence breakthrough as intuition algorithm beats humans in data test
  • MIT Develops Device That Can See People Through Walls
  • Lie-detecting algorithm spots fibbing faces better than humans
  • Machines That Can See Depression on a Person’s Face
  • An algorithm aims to be able to replace human intuition
  • ‘Psychic Robot’ System Guesses Intentions From Your Movements
  • MIT’s intelligent drone can avoid crashes and fly at 30 MPH
  • Facebook working on AI that can tell what’s in photos
  • Computer Algorithms Could Aid Schizophrenia Diagnose
  • Machines That Can See Depression on a Person’s Face
  • Robot Radiologists Will Soon Analyze Your X-Rays
  • Predicting change in the Alzheimer’s brain
  • A new computer program that can diagnose cancer in just two days!
  • Machine learning to help predict online gambling addiction
  • Predicting people’s daily activities with deep learning
  • MIT Scientists Create An AI System That Can Determine How Memorable Your Face Is
  • This Algorithm Is Better At Predicting Human Behaviour Than Humans Are
  • New Artificial Intelligence: Russia Endows Robots With Collective Mind
  • Scientist Develop New Machine Which Can Calculate Pattern Recognition with Near Human speed
  • Machine Vision Algorithm Learns to Recognize Hidden Facial Expressions
  • Artificial Intelligence: Scientists Developed a Handwriting Algorithm
  • Computer With Built-In Algorithm Beats Man In A Turing Test
  • Machine learning to differentiate between positive and negative emotions using pupil diameter

 

Self learning for Robots(from observation)

  • Giving robots a more nimble grasp
  • Why it is hard to teach robots to choose wisely
  • Machine learning plays vital role in the evolution of Man
  • Designing Robots That Learn as Effortlessly as Babies
  • How Robots Can Quickly Teach Each Other to Grasp New Objects
  • Why IBM just bought billions of medical images for Watson to look at
  • Read my lips: truly empathic robots will be a long time coming

 

Learning Culture, Humanity, emotions and ethics

  • Smart Programs Read Shakespeare
  • Artificial intelligence learns how to put together interactive stories just as good as a human
  • How do you teach a machine to be moral?
  • ‘Psychic Robot’ System Guesses Intentions From Your Movements
  • Lie detection software learns from real court cases
  • Why Helping Humanity Should Be Core to Learning
  • Could Artificial Morals and Emotions Make Robots Safer?
  • AI: In search of the sarcasm algorithm
  • Microsoft Teaches Computers To Be Funny
  • Microsoft’s Project Oxford Can Now Detect Emotions from Photos
  • Robots are learning to disobey humans: Watch as machine says ‘no’ to voice commands
  • Robots could be converted to religion someday: Scientists
  • Intimacy & Falling In Love With A Robot Could Happen In 50 Years Because Of Artificial …
  • Health
  • If We Want Humane AI, It Has to Understand All Humans
  • Humai Is Working On A Way To Bring Your Loved Ones Back From The Dead
  • Mum Robot Goes Darwinian on Her Kids

How does that (self) learning affect services and our lives in future cities

  • Artificial intelligence comes to toys
  • Beyond the Pill: Data Is the New Drug – Google Life Sciences Rebrands As Verily, Uses Big Data To Figure Out Why We Get Sick
  • Nvidia Aims To Power Flying Vehicles with Jetson TX1 Board
  • Motorcycle-riding robot may take on world champion racer
  • Meet Mercedes-Benz’s Vision Tokyo, a self-driving car for the megacity
  • How artificial intelligence could lead to self-healing airplanes
  • Trains with brains: how Artificial Intelligence is transforming the railway industry
  • A self-driving sailboat to patrol the oceans and monitor the environment
  • Malaysia testing ‘artificial intelligence’ for prisons
  • Real-Time Seizure Detection Possible with Learning Algorithm
  • Facebook Is Helping People With Blindness “See” the Photos on Their Walls
  • Mitsubishi Electric uses machine-learning tech to detect distracted drivers
  • Tinder matches made easy with new intelligent algorithm
  • Deep Learning Algorithm Successfully Identifies Potential Intracranial Haemorrhaging
  • An artificial intelligence based third Umpire
  • When children talk to toys, some are talking back
  • Predicting change in the Alzheimer’s brain
  • Robotic Automation Meets Agriculture
  • Food delivered by drones, driverless cabs and cyber PAs to organise your party: A revolution in …
  • AI will soon be forecasting the weather
  • How Artificial Intelligence Can Fight Air Pollution in China
  • Starfish-killing robot to protect Great Barrier Reef
  • Self-Driving Car Tech Allows Vehicle To ‘See’ Environment In Real Time
  • US Company On Plan To Bring People Back From Dead Using Artificial Intelligence
  • A trillion tiny robots in the cloud: The future of AI in an algorithm world
  • Teforia Is A Tea Brewing Robot That Uses Algorithms To Pour The Perfect Cup
  • Japanese artificial intelligence passes university exams (but still can’t quite get into the country’s …
  • Facebook AI built to help visually impaired people
  • Problem of Climate Change and Global Conflicts Can Be Solved Using Human and Computer …

 

Risks to humanity and cities

  • ‘Only movies build bad robots‘ – famous last words?
  • Why human-in-the-loop computing is the future of machine learning
  • As Robots Steal Millennials’ Jobs, Young Workers Focus On Skills, Not Careers
  • Millions of jobs at risk from artificial intelligence
  • Davos report projects 5 million jobs will be lost to new technologies by 2020
  • Can Humanity Rein In The Rise Of The Machines?
  • Christian leader warns of ‘Frankenstein monsters’ due transhumanism
  • The rise of the killer robots — and why we need to stop them
  • Producer of Russia’s Armata T-14 plans to create army of AI robots
  • Inside the Pentagon’s Effort to Build a Killer Robot
  • How Technology Could Prevent Another Paris-Like Attack
  • Kaspersky deepens security offering through machine learning
  • Robots will declare war on humans within 25 years, claims artificial intelligence expert
  • Law firm bosses envision Watson-type computers replacing young lawyers
  • Hitachi Hires First ‘Artificial Intelligence’ Boss To Manage Workers

Conclusion and Evolution

We reframed the problem of Deep Learning and Smart cities by asking the Question:

How could self-learning machines affect humanity in cities?

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens

    What are the threats?

Please contact me at ajit.jaokar at futuretext.com to know more updates – especially if you are a city official. We are also planning to explore the implementation of these ideas by working with companies like Nvidia.

I would also like to thank the students who helped me with this project.

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IoT Central Digest, September 1, 2016

Thank you to all the new members and contributors of IoT Central. Our membership is growing quickly and we all should be excited about the community we are building. In this issue Bill McCabe looks at IoT Services, Ben Dickson explains why the ransomware threat is more serious than you think, and Sandeep Raut explores the good, the bad and the ugly of IoT. 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.

Why Companies Should Care About IOT Services

Posted by Bill McCabe 

As with any new technology, businesses will need to find quantifiable benefits in the Internet of Things before the concept is embraced and implemented. It could be argued that connected devices are already being adopted on a wide scale: companies like Microsoft, Amazon, Qualcomm, IBM, and others already see IoT as a core part of their businesses. Even so, there are still some, especially small to medium sized businesses, that are weighing up the costs and benefits of ultra-connectivity in the world of the Internet of Things.

The potential of cellular technologies for the great world of IoT

By Rick Blaisdell

In the next five years Internet of Things communications will see unprecedented growth, and cellular connectivity will become even more valuable. Wireless cellular technologies have found enormous potential as key enablers for IoT, and the continuously increasing technology enhancements and innovations in cellular technologies are promising to be the major primary access methodologies to enable a great number of IoT applications.

The IoT ransomware threat is more serious than you think

By Ben Dickson

At the recent Def Con hacking conference in Las Vegas, two researchers from cybersecurity firm Pen Test Partners showed that they could inflict your smart thermostat with ransomware from hundreds of miles away, and force you to fork over cash (usually bitcoins) before you could regain control of the appliance. Ransomware has been around for a while. It’s a breed of malware that locks down access to your files by encrypting them and sells you the decryption key that will give you back access to the files. IoT ransomware is relatively new. However, this isn’t the first time that the topic of IoT ransomware has been brought up by cybersecurity experts

Start Building an IOT Solution

By Ashish Modi

To build an IOT application we required following things.

  1. A problem where we required IOT solution. 
  2. Identify and design IOT based solution (Hardware + software + connection).

A problem where we required IOT solution

Nowadays everything is connected to the internet.  We need to move our existing system into IOT based solution.

Hitchhiker's Guide to IoT Standards and Protocols

By Preston Tesvich

In this article, we focus on a framework of how you can think about this problem of standards, protocols, and radios.  The framework of course depends on if your deployment is going to be internal, such as in a factory, or external, such as a consumer product. In this conversation we’ll focus on products that are launching externally to a wider audience of customers, and for that we have a lot to consider.  Let’s look at the state of the IoT right now— bottom line, there’s not a standard that’s so prolific or significant that you’re making a mistake by not using it. What we want to do, then, is pick the thing that solves the problem that we have as closely as possible and has acceptable costs to implement and scale, and not worry too much about fortune telling the future popularity of that standard.

The Good, The Bad & The Ugly of Internet of Things

The greatest advantage we have today is our ability to communicate with one another. The  Internet of Things, also known as IoT, allows machines, computers, mobile or other smart devices to communicate with each other. Thanks to tags and sensors which collect data, which can be used to our advantage in numerous ways. IoT has really stormed the  Digital Transformation. It is estimated that 50 billion devices connected to the Internet worldwide by 2020.

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As with any new technology, businesses will need to find quantifiable benefits in the Internet of Things before the concept is embraced and implemented. It could be argued that connected devices are already being adopted on a wide scale: companies like Microsoft, Amazon, Qualcomm, IBM, and others already see IoT as a core part of their businesses. Even so, there are still some, especially small to medium sized businesses, that are weighing up the costs and benefits of ultra-connectivity in the world of the Internet of Things.

You do not have to dig deep to see why IoT is important. Business Insider's research division, BI Intelligence, has predicted that IoT will become the largest device market in the world over the next five years. Most analysts predict market value will reach in to the trillions, with possibly $7 trillion of total value by 2020. Any way you slice the pie, billions of dollars are on the table. These figures are promising for businesses directly involved in the manufacture and design of device services and hardware, but what about the companies that will purchase these technologies to incorporate them into operations?

Perhaps the single largest benefit will be in how Internet of Things devices can lower costs. The manufacturing sector provides an ideal case scenario. Machine to Machine (M2M) systems will allow for machinery to become more efficient, and more autonomous. Take a production line that was previously labor intensive. Sensors relying on IoT can receive orders, initiate fabrication, sign off work orders, and even package products using IoT, and with little human interaction. Even non-automated manufacturing will benefit. Orders can be taken from anywhere in the world, transferred through the cloud, and delivered to remote manufacturing facilities. These systems can collect valuable analytics that can benefit accounting, inventory management, and even resource procurement.

While this type of IoT will directly benefit businesses in manufacturing, it will also create new opportunities for project managers, engineers, and IT professionals who will be necessary in designing, implementing, and supporting these systems. It even creates the role of Chief Internet of Things Officer, the CIOTO, tasked with managing a network of connected systems, and connecting their efforts back to business goals.

Because IoT provides immediate data collection, businesses in all industries will benefit from improved decision making. Being able to analyze and distribute intelligence faster means that tedious data collection will be a thing of the past. Decisions can be made faster, and in some cases can be automated. What this spells for enterprise is, in essence, better decisions based on better data.

Hong Kong International Airport, and other mega-airports around the world, already rely on RFID technology to track luggage and freight throughout their sites. This enables luggage to be delivered by machine to the correct gate, the correct passenger carousel, or to the correct airliner, train, or delivery vehicle. Items are tracked via computer, and managed from a central control point. This reduces hands on management and labor costs. HKIA spent $50 million to develop the initial infrastructure, but widespread adoption of this IoT based technology could save the industry $760 million per year, according to the International Air Transport Association.

Imagine how a similar system could benefit a SMB. Goods delivery could be RFID or barcode tracked on handheld scanners. This tracking information could be uploaded to a cloud solution, from where dispatchers, couriers, and clients could track the location and progress of a delivery. These are the kind of innovations that are driving IoT, and making it a necessary technology in a market where cost and efficiency is key, and where end users and consumers demand constant, easily accessible information.

The opportunities are there for businesses who adopt IoT today. The benefits exist whether they seek to improve manufacturing efficiency, streamline logistics processes, or even provide new ways for customers to interact and receive information. In the growing world of IoT, the question is not why should we care, but is rather, can you afford not to?

Please give us your feedback or share how the Internet of Things has touched your business below. 

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Guest blog post by Bill Vorhies

Summary:  Sensors that know how you feel?  Sensors that want to change the way you feel?  When did that happen and better yet how?

 

We’re getting used to sensors finding out what we’re doing.  Apparently they are now sufficiently sophisticated that they can even tell if I’m sitting up straight (yes Mom – BTW using a camera is almost cheating, you should be able to do this with just an accelerometer and a gyro). 

But what if I told you that those same IoT sensors can tell how you feel?  And now they’re even being programmed to change the way you feel!  A little creepy?  Feeling manipulated?  Hang on to your hat because it’s about to get worse or better depending on your point of view.

 

Mood Science

First of all I didn’t even realize that ‘mood science’ is a real thing.  Turns out it’s been going on a long time in design circles where designers and architects in particular have been making informed guesses at what chills us out.  Blue rooms relax.  Red rooms stimulate and arouse.  Pink rooms are most soothing. 

Interesting note: For years many prisons have been painting their walls bright pink based on early findings that prison inmates’ tempers were soothed when placed in pink-walled cells.  For what it’s worth these generalizations about room color have all now been overturned by the new practitioners of ‘mood science’.

I’ve been tracking the uses of IoT sensors particularly those with human interaction (think Fitbit) but I didn’t see the big picture until I came across this article “Design for Mood: Twenty Activity-Based Opportunities to Design for Mood Regulation” by Pieter M. A. Desmet, a member of the Faculty of Industrial Design Engineering, Delft University of Technology.  This is one of those articles you know you should trust because it contains a reference bibliography of 169 learned articles.

For the most part it seems that in academic circles the desire to determine how to ‘regulate mood’ is pretty benign and generally couched in terms like improving subjective well-being.  After all who doesn’t want an extra helping of well-being?

Then I found it.  About three pages in, buried in the text:

  • Mood influences consumer behavior. Research has demonstrated that consumer mood influences buying behavior, product preference, and purchase decisions.
  • When evaluating new products, people do so more favorably when in a good mood than when in a bad mood.
  • Mood influences user behavior. For example, when using new products, individuals in a bad mood tend to explore fewer interaction possibilities than those who are in a good mood.
  • A good mood increases one’s willingness and motivation to adopt and use new technologies.

OK, now it’s clear.  Just sending me a coupon when I’m standing next to the new flat screens isn’t nearly enough.  “They” want to know how I’m feeling, and better yet to make me feel in a way that positively disposes me to buy.

One more piece of foundational information before we move on to how this works.  Turns out that monitoring and manipulating mood (feelings) through just four quadrants and eight basic mood types is enough to make this happen.

When it comes time to model, one of these eight states will be our dependent target variable.

 

How It Works

How would sensors go about detecting mood?  It’s all about cleverly combining and interpreting the signals.  This is a fairly new field studying how to fuse sensor data to make it context aware.  Take for example heart rate as measured by a wearable sensor.

Dr. José Fernández Villaseñor is a medical doctor and electrical engineer studying the field of emotion analysis using sensors.  His research shows the rate at which heart rate increases can differentiate between exercise and increases due to adrenalin from excitation based on the slope of the increase.  Turns out that Heart Rate Variability (HRV) is one of the prime tells that can be used to differentiate one mood from another.

Here’s a simple example of how your Xbox or PS4 can not only tell how you’re feeling but manipulate those feelings. 

image source: mouser.com

You are playing a driving game.  Your game controller may contain sensors that can detect:

  • Muscle relaxation (MR)—via a pressure sensor.
  • Heart rate variability (HRV)—via a two-electrode ECG on a chip.
  • Sweat (S)—via a capacitive sensor.
  • Attitude (A)—via an accelerometer monitoring a person’s state of relaxation (jerky movements vs. steady hands).
  • Muscle contraction (MC)—via a pressure sensor.

Suppose the combination of increased pressure on the controller, sweat, and the jerkiness of your motions (from the accelerometer) could be correlated (modeled) against your performance in the game.

Pressure and sweat increase.  Jerkiness increases.  Your game platform infers that you are both excited and stressed.  Your score is just OK.  To encourage you to play more, the system adjusts the difficulty of controlling the steering, braking, and the behavior of the other cars to reduce difficulty.

Your performance and score improves.  Pressure and sweat decrease and your hand movements become smoother.  The platform interprets that you are more relaxed and are mastering the game at this level.  To keep you involved it increases excitement by making input controls and the behavior of competing cars more difficult.

You’ve just been gamed in the new world of IoT mood manipulation.

 

It’s Not Just About Wearables

In a sense, if you’re worried about the intrusiveness of this technology you would think that wearables offer their own defense – just don’t wear them (leave the Fitbit at home).  Problem is it’s not just wearables.  There are at least four categories of things that supply data about ourselves, many of which you may not have thought of in this way.

Wearables:

Wearables is a big one.  It’s not just where you are and how fast you got there (GPS, accelerometers, altimeters, thermistors, gyros) it’s also sensors that measure physiological signals such as heart rate, skin conductance and temperature, and respiratory rate.  These already include finger rings, ear rings, wristwatches, wrist and arm bands, and gloves.  Soon to come, sensorized garments including shirts, shoes, and underwear.

Take a look at the W/Me wearable wellness monitor introduced in 2013 that claims to measure the four basic mood states: passive, excitable, pessimistic, and anxious.

Natural-Contact Sensors

These are sensors that are integrated into the devices and particularly the surfaces of objects we regularly come in contact with.  Likely you are interacting with these objects, not just brushing up against them. How about the steering wheel of your car that could easily have these sensors embedded and also transmit information about the smoothness or jerkiness of your movements.

It could be a chair that infers your stress or relaxation or a pen, cell phone, or mouse that can detect moods like stress, nervousness, and excitement based on hand movement.  Even your keyboard can give you away by interpreting the strength and cadence of your keystrokes or how many times you use the backspace key.

Non-Contact Sensors

Anything with a camera or a microphone: computer, phone, TV, or game console that could use visual signal processing (deep learning) to record facial and voice expression, body posture, pupil diameter, and eyelid closure patterns.  Law enforcement is hard at work adopting facial and emotion detecting software.

Self Expression

Sometimes we just tell machines how we feel.  I frequently tell my alarm clock how I feel when I give it a rough slap (there could be a sensor in there warning my family I’m in a bad mood when I come out for breakfast).  About 8 years ago Philips developed a ‘mood pad’ for hotel rooms that let you pick a mood (romantic, restful, let me sleep) that controlled ambient lighting.  And if you look in your app store, I’m sure you can find an app for creating a mood journal or for evaluating how you feel right now.  Who’s receiving that signal?

 

What Could Possibly Go Wrong?

This question is almost too rhetorical to even ask.  If you want to make me more likely to buy something, OK, maybe I can live with that.  And if it makes my game play more interesting that might go on the good list.  If your car tells you you’re suddenly suffering from road rage that could be helpful.  And certainly there are applications in healthcare, mental care, and elder care that we can easily applaud.

But when it comes to manipulating me I really want to know who’s doing it and with what motive.  What could the government or the IRS be learning about me or trying to make me do?  I don’t want to seem alarmist.  Sometimes the best thing we can do is just make ourselves aware that this is happening.  Maybe there will be an on-package or on-screen disclaimer (probably buried deep in the EULA). 

This is one of those technological advances that delights me as a data scientist and disturbs me as a citizen and human being.  Like all technological advances this one’s out of the bottle and trying to get it back in would make the loaves and fishes look like child’s play.  As much as anything, I just want to know if I’m getting some quid for my quo.

 

 

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 and Assisted Living

IOT and Assisted Living

It is most likely the you have heard the term “internet of things” or IOT in regards to everyday things such as our televisions and phones. That is not however where this new innovation is going to end. There has been a lot of talk about the IOT stepping into the healthcare industry with things like connected healthcare.

Another area where we can expect to see the IOT playing a large role is in assisted living. It is no secret that people are living longer than we ever have before. It has even been said that the first person to see the age 150 has already been born. It should come as no surprise then that nursing homes and senior assisted living facilities are full to bursting with elderly people whom are healthy but incapable or afraid to live on their own. The IOT could help with this.

We are all familiar with products such as Life Alert that have been used to give seniors a sense of security in their own home. These types of things allowed seniors to remain in their homes longer than before. They are not perfect though. The fact is that the technology behind these types of monitoring devices is out dated. It relies on a live person being available 24/7 to respond to the individuals call for help. What happens when the person in question does not have the capability of triggering the monitoring device though? This is where the IOT can step in.

Recently engineers have developed sensors that can be placed discreetly throughout the home. These sensors then monitor the resident’s movements and activities throughout the day. These sensors rely not on a live person monitoring them, but on algorithms and programming that over time learn the normal habits of the person living in the home. They monitor things such as…

  • location of the resident within the home
  • light sources being used
  • bed time and awakening time
  • television watching
  • cooking
  • bathroom usage
  • leaving the home and returning
  • heating or air conditioning temperature and adjustments

Then in the case of an emergency or variations to that pattern that do not fit the normal activity within the home can notify family members or medical professionals.

Another development is something similar to that of Life Alert but more sophisticated. Wireless vital sign monitors. These devices can notify first responders of medical emergencies such as stroke, heart attack and a loss of consciousness without the person suffering having to do anything at all. Further they could notify patients of an issue well before it actually happens, such as notifying a heart patients doctor that their heartrate has been erratic over a period of time, thus indicating that further investigation may be needed. It is not hard to see that very soon we could see the IOT playing a large role in the lives of our seniors, or anyone that needs some form of assistance.  

 For more information about IOT and Healthcare please check out our new website  www.internetofthingsrecruiting.com 

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Guest blog post by Jason Li

Connected devices, Smart City, home automation, e-health, Big Data ... In recent years, the concepts of communicating objects have multiplied. In reality, they are all one facet of the same upheaval - the Internet of Things.

Cars can be driven without a driver, TVs are going online, and heating systems are activated automatically to the arrival of the residents. The Internet is making many processes in daily life easier. The Internet of Things, or IoT, which enable devices to communicate with people or machines, is actually working in many places in our daily life already.

Further reading:

For People: Web-Enabled Electronics 

If phone and computer without Internet access, they become unthinkable devices. Meanwhile, this is also valid for televisions and audio devices or cameras. New electronic products will not come without Internet in the future. For example, wearables are the typical types of Web-enabled electronic devices which are worn directly on the body for health monitoring, have become the next big trend in healthcare.

Further reading:

For Home: Smart Home 

Manufacturers not only produce Internet-enabled home appliances, but also care about apps and software portals. For example, Miele combines a range hood with the stove so that the fan motor is automatically adapted to the cooking process.

The smart home concept has created many great ideas - shutters can be controlled by smartphone, and lights and heats can be turned before you returning home from vacation. Germany’s digital association Bitkom assumed that there will be one million fully networked households by 2020.

Further reading:

For Transportation: Connected cars 

In a survey, the respondent indicated that the smartphone connection in the car was more important than a higher horsepower. In the world of connected cars, drivers, cars and infrastructures are all connected with each other, and are able to communicate among objects in the system in real-time to optimize routes and avoid accidents.

The concept of autonomous cars has taken the step further towards making self-driving cars. Major car manufacturers promised to produce at least one business model within the next five years.

Further reading:

For Community: Smart City 

In 2050, our planet will be different from today – There will be nine billion people live on it, 70% of them live in cities. This growing trend has not only significantly increased demands in cities, but also created great opportunities to improve efficiency of energy, material and human resources.

The Smart City concept was created to exploit these opportunities with aims to integrate information and communication in various technical systems of a city to promote innovative solutions for mobility, management and public safety in the city – in particular, electricity, water, gas, and goods.

Further reading:

For Agriculture: Digital Agriculture 

“According to the United Nations’ Food and Agriculture Organization, food production must increase with 60% to be able to feed the growing population expected to hit 9 billion in 2050. John Deere uses big data to step into the future of farming to help farmers achieve this ambitious target.”(Datafloq)

The networked agriculture can benefit in many places of databases and real-time monitoring – The balance of weather data with the plant growth data, and the complex structure of the forecasted demand and the current market.

Further reading: 

For Manufacturing: Industry 4.0 

Industry 4.0 represents the manufacturing future with IoT. High demands are made on the production of the future – you must be intelligent, changeable, efficient and sustainable. Industry 4.0 stands for the intelligent networking of product development, production, logistics and customer.

The Industry 4.0 Working Group define Industry 4.0 as “a network of autonomous, controlled situational itself, configure itself, knowledge-based, sensor-based and spatially distributed production resources (production machines, robots, conveyor and storage systems, resources), including their planning and control systems”.

Further reading:

Digital Industry 4.0 – It is All about the Manufacturing Future with IoT

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Big data in ranching and animal husbandry

Guest blog post by Brian Rowe

Another big part of the food supply comes from ranches and farms that raise and slaughter various livestock. While ranching is sometimes bundled with agriculture, I discussed farming in Big Data in Agriculture, so we’ll focus on ranching this time around. Somewhat surprising is that big data usage in ranching appears more limited than in farming. That said, there are a number of novel uses of technology and data in animal husbandry.

Credit: Emilio A. Laca

Land Use Optimization

At a high level, the goals of ranching and farming are the same as any business: increase yields and lower costs. Production maximization has long played a role in large operations. A twist to the optimization problem is land use optimization and how that can affect yields. According to NASA, “Australia’s rangelands provide an opportunity to sustainably produce meat without contributing to deforestation” if properly managed. This sort of optimization is made possible by big data coming from satellites. The same article cites how some West African nations use satellite data “to identify areas with agricultural potential and to estimate the amount of food available.” Growing up in rural Colorado, the most advanced tech I saw at ranches were solar powered fences and artificial insemination. Clearly a lot has changed. From a supply chain perspective, these trends also demonstrate how just-in-time manufacturing can be extended to resource allocation.

From a technical perspective, crop and livestock rotation will become outputs of a multi-objective optimization problem. I imagine that the challenge will be less about the optimization and more about the inelasticity of “bioprocesses”. Aside from slaughter or transfer to somewhere else, there aren’t too many options for reducing “inventory”. Presumably these issues already exist, so any solution is bound to be an improvement. Ultimately, there is a race to avoid the outcome that the U.N. foresees: the majority of humans eating insects as a primary source of protein. Even if that future is unavoidable (not necessarily bad), presumably similar techniques can be used to maximize insect yields.

Sensors and IoT

Technology advancements are driving parralel trends in agriculture and ranching. While satellite imagery offers a big picture overview, sensors provide a micro view of individual plants and animals. RFID tags are a first step enabling real-time tracing of an animal. Equally important is the assignment of a unique identifier to facilitate storing electronic records that can be merged into a centralized dataset. RFID is fundamentally passive, whereas sensors are active. This is where biosensors and Precision Livestock Farming (PLF) come into play. PLF is a comprehensive approach to livestock management and animal welfare. The goal is “continuous, fully automatic monitoring and improvement of animal health and welfare, product yields and environmental impacts” Some of the sensors developed to achieve this are surprisingly simple and surprisingly clever, such as sensors that monitor the vocalizations of livestock to determine stress, illness, etc. These advances can also “raise milk yields, while also increasing cows’ life expectancy and reducing their methane emissions by up to 30%” (CEMA). The Biosensors in Agriculture workshop held in the UK presents even more exciting examples.

Other notable research around PLF include image analysis to monitor animal welfare and
classifying the behavior of cattle and fowl based on GPS movements. According to one paper, a decision tree was used to classify four behaviors: ruminating, foraging, standing, and walking. The features were based on distances and turning angles from the GPS data. Not surprisingly, the confusion matrix was pretty poor in terms of distinguishing between ruminating, foraging, and standing. So there’s lots of opportunities to whip out R and randomForest or party to conduct your own analysis (assuming you have access to the data).

Data and Accessibility

Big data is often synonomous with cloud computing and for ranching it’s no different. As with agriculture there are trends to centralize data to “help ranch managers track livestock, view production statistics, plan grazing rotations and generate reports that can offer insight into the health of a livestock operation.” Unlike in agriculture, it doesn’t appear that the machinery manufacturers are taking a role, although it wouldn’t surprise me if some PLF suppliers have cloud platforms for their customers. GrowSafe Systems is creating their own cloud-based dataset based on their customer data. Their system collects and forecasts “complex animal traits such as efficiency, growth, health, stress and adaptation.”

Europe has taken a different approach focusing on defining a comprehensive classification scheme for agricultural systems. Clearly the goal is data interoperability, so data can be widely shared and applied across farms and ranches. This goal is reflected in the three-level system that encompasses environmental factors and GIS data to site-specific measurements of individual animals that affect yields and animal welfare. Landcover data appears to be the most extensive, while biosensing is likely where the most immediate opportunities are to be found.

As data becomes more focused on individual sites and animals, scarcity is the word that comes to mind. In the USA public datasets don’t come anywhere near the level of detail to make a useful analysis. See data.gov for an example of a disappointing dataset. Of course it isn’t clear whether transparency of this sort is even possible. One rancher believes they have a right to privacy and shouldn’t be compelled to open their books to external scrutiny. This is understandable, but does this belief extend to data? Data privacy is a thorny issue, particularly balancing privacy, ownership, and the need for transparency vis a vis food security/safety. Eventually I think economics will force a change of heart if yields and margins increase significantly with the help of open data. However, this may take the shape of data cartels as opposed to truly open data. As big data and centralized data stores become more wide spread, this debate over data ownership will continue to be visited.

Know of some public datasets available for ranching and animal husbandry? Post links in the comments!

This post first appeared on cartesianfaith.com. Brian Lee Yung Rowe is Founder and Chief Pez Head of Pez.AI // Zato Novo, a conversational AI platform for guided data analysis and Q&A. Learn more at Pez.AI.

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By Abjijeet Banode. This article originally appeared here.

Fleet Management System (FMS) is one of the essential parts of businesses which directly or indirectly deal with automobiles. Precise fleet management minimizes various operational risks and increases cost efficiency. With proper utilization of analytics, alerts, and diagnostics, fleet management converts businesses to be more reliable and sustainable. Like any other business, predicting risks and working towards mitigation is essential for fleet businesses. Ample use of data analytics for early detection of faults and predictive mechanism helps business to reduce maintenance cost and downtime.

Typical modular fleet management unit consist of OBD-II (On-Board Diagnostics – Standard revision – II) module which connects to Controller Area Network (CAN) bus. Microcontroller, sensors, and various devices from vehicles use this bus (communication channel) to communicate with each other. OBD-II module captures diagnostic information from the CAN bus example, data engine control unit, and transmission. 

Figure 1: Typical architecture of Fleet Management System (FMS)

GNSS (Global Navigation Satellite System) receiver unit like GPS, GLONASS, assist to capture geographic coordinates. Synchronous capture of GNSS data and diagnostic data can help to immediately identify exact location of a vehicle breakdown or other events. Mapping it against reference data from the department of transportation can be utilized to analyse driver behaviour and their adherence to traffic regulations.

Every business has its unique requirement and objective behind Fleet Management System.  Organizations need to upgrade FMS module based their particular use case.  Trailer transporting food needs additional sensors to monitor temperature of on-vehicle refrigerators whereas a trailer carrying hazardous liquids has its own sensor requirements.

Cellular module is essential for real-time monitoring of a vehicle’s essential parameters, theft detection, driver safety, and to report breakdown. This data needs to be sent to cloud (or physical storage) so that fleet operators can analyse required parameters in real-time, perform predictive analysis, and identify mitigation requirements for smooth operation of fleet.

Effective use of data analytics and visualization tools – dashboards is the brain of intelligent fleet management system. Visualization parameters varies with respect to business needs but a few basic conclusions like driver behaviour, fuel efficiency, fleet health, maintenance requirements and breakdown will be always there on dashboards. 

Figure 2: Benefits of effective fleet management

The cost of E2E fleet management system has been reduced due to efficient analytics platform based on quality open source solutions (e.g. MongoDB, Hadoop), reduction in cost of electronic assemblies (chipset cost, antenna cost), economical cellular data connectivity (eUICC, dedicated data plans for M2M, IoT), reduction and flexibility in cloud storage cost due to competition, and most importantly cross domain interest of companies from various vertical (e.g. Cellular operators), electronic product manufacturers, and IT services are exploring additional revenue streams in automotive domain.

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