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Case Studies (238)

Guest blog post by Eduardo Siman

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

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

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

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

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

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

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

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

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Guest blog post by PG Madhavan

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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

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Figure:1 TPS-based aircon controller

TPS-based aircon controller

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

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

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

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

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

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

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

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

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

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

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

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

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

Further work

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

Figure:2 Aircon

Figure 2  Aircon

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Guest blog post by Raj Dalal

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

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

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

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

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

Questions that IoT can address

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

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

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

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

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

Some successful user case

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

The future

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

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

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

About the Author

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

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Two Hot Growth Areas for IoT

Guest blog post by Bill Vorhies

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

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

 Velocity = Stream Processing + IoT

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

 

Three Characteristics of Emerging IoT

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

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

 

The IoT Ecosystem

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

 

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

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

 

Sensors on Things

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

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

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

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

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

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

 

The Goldmine in Human Wearables

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

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

 

Enterprise Augmented Reality

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

 

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

 

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

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

 

Human Wearables that Change Behavior

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

 

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

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

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

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

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

 

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

[email protected]

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IoT Central Digest, November 16, 2016

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

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

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

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

Internet-of-Things Patents: Tough to Enforce?

Guest post by Kenie Ho and Charles Huang

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

But are IoT patents truly valuable?

IoT Standards / Organizations

The IoT communication protocols

Guest post by James Stansberry

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

Is it possible to democratize the Internet of Things?

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

7 things that are getting smarter in IoT era

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7 things that are getting smarter in IoT era

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

Home security systems

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

Bed

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

Energy use

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

All home appliances and systems

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

Self-storage monitoring

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

Construction sites

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

Emergency vehicles

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

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

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Guest post by Kenie Ho and Charles Huang

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

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

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

But are IoT patents truly valuable?

I Want to Sue You, But I Can’t

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

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

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

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

Divided Infringement

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

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

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

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

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

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

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

Territorial Scope

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

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

Good Patents, Big Consequences

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

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

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

Strategic Patenting

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

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

Authors’ Bio

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

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

 

Patent Photo Credit to Nick Normal via Flickr.

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IoT Central Digest, November 1, 2016

Well October was definitely a scary month for IoT. In this edition our newsletter revisits the security issues that hacked their way into IoT last month. If you haven't been paying attention, or are looking for different points of view, you'll want to read the pieces below from our members and contributors. Lets hope for a more secure and sane month of November.

Also, a reminder, this Thursday, November 3, 2016, join me, John Myers of Enterprise Management Associates and Dan Graham of Teradata where we look at what people REALLY do with the Internet of Things and Big Data? Registration information is here.

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.

The Internet of Evil Things

Guest post by Joe Barkai 

You may have heard me at a conference or read my response to questions concerning the security of the Internet of Things. When asked, I sometimes “refuse” to answer this question. This is not because I do not think that data security—and the closely-related data privacy—are not important; of course they are.  But I want to highlight the point that data security and privacy are foundational issues that are not unique to IoT devices. Every enterprise must ensure that all data—IoT generated or not—is secured and that data privacy and ownership are handled properly.

Do not stop asking for security in IoT

Posted by Francisco Maroto

Almost three years ago, I wrote in my IoT blog  the posts “Are you prepared to answer M2M/IoT security questions of your customers ?. and “There is no consensus how best to implement security in IoT” given the importance that Security has to fulfil the promise of the Internet of Things (IoT). And during this time I have been sharing my opinion about the key role of IoT Security with other international experts in articles “What is the danger of taking M2M communications to the Internet of Things?, and events (Cycon , IoT Global Innovation Forum 2016).

Hacking a Home Can Be Easier Using IoT - Is Your Smartphone Safe?

Posted by Mike Davidson  

Internet of Things has raised concerns over safety. Nowadays, it is possible to control your home using your Smartphone. In the coming years, mobile devices will work as a remote control to operate all the things in your house.   Some devices display one or several vulnerabilities that can be exploited by the hackers to infiltrate them and the whole network of the connected home.

How insecurity is damaging the IoT industry

Guest post by Ben Dickson

The Internet of Things (IoT) is often hyped as the next industrial revolution—and it’s not an overstatement. Its use cases are still being discovered and it has the potential to change life and business as we know it today. But as much as IoT is disruptive, it can also be destructive, and never has this reality been felt as we’re feeling it today. On Friday, a huge DDoS attack against Dyn DNS servers led to the majority of internet users in the U.S. east coast being shut off from major websites such as Twitter, Amazon, Spotify, Netflix and PayPal.

IOT Security Trends// Is the Online World More Dangerous ??

Posted by Bill McCabe 

Security threats are the biggest concern among the main concerns on the Internet of Things. Due to its very nature, it is a target of interest for those who want to commit either industrial or national espionage. By hacking into these systems and putting them under a denial of service, or other attacks, an entire network of systems can be taken out. This has caused cyber criminals to become very interested in the IoT and the possibilities that surround its misuse.

Report: List of Top 10 Internet of Radios Vulnerabilities

Posted by David Oro

The IoT has a big security problem. We've discussed it herehere and here. Adding to these woes is a new report on the Top 10 Internet of Radios Vulnerabilities. Yes, radios...because IoT so much more than data, networking, software, analytics devices, platforms, etc. When you're not hardwired, radio is the only thing keeping you connected.

5 Steps to Creating a Secure Smart Home

Posted by Ryan Ayers 

First came smartphones, equipped with the ability to set alarms and calendar notifications, reminders, and other convenient apps and services to make our lives easier. Taking that a step further are “smart homes” or automated homes, which allow users to remotely control devices in the home such as lights, televisions, and even toilets and water pumps, using a smartphone or computer. Aside from remote control, however, smart systems in homes can also help make the home more adaptable. For example, Nest is a smart system that learns the home’s inhabitants’ schedules and preferences to heat or cool the house for maximum efficiency and comfort. Sounds great, right? Many people think so, which is why the industry is projected to keep growing quickly from 48 billion in 2012 to an estimated $115 billion by 2019

How the IoT industry will self-regulate its security

Guest post by Ben Dickson

Following last week’s DDoS attack against Dyn, which was carried out through a huge IoT botnet, there’s a general sense of worry about IoT security—or rather insecurity—destabilizing the internet or bringing it to a total collapse.

All sorts of apocalyptic and dystopian scenarios are being spinned out by different writers (including myself) about how IoT security is running out of hand and turning into an uncontrollable problem. There are fears that DDoS attacks will continue to rise in number and magnitude; large portions of internet-connected devices will fall within the control of APT and hacker groups, and they will censor what suits them and bring down sites that are against their interests. The internet will lose its fundamental value. We will recede to the dark ages of pre-internet.

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Industry 4.0 and Manufacturing Processes

Industry 4.0 or, as it is also known the fourth industrial revolution is the trend that is currently coming into play of automating the manufacturing processes and the use of IoT and other technologies to make industrial processes more readily accomplished. It is working hand in hand with things like the internet of things, cloud computing and cyber-physical computing. 

Using Industry 4.0, we create what are called smart processes and smart computing.

According to Wikipedia, "Within the modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real time, and via the Internet of Services, both internal and cross-organizational services are offered and used by participants of the value chain."

The term Industry 4.0 or fourth industrial revolution began in the German government with a project that they had created that was markedly high tech. It promoted computerized manufacturing and provided the reasons for that manufacturing to take place as well as how industry 4.0 would play out with other areas of manufacturing such as logistics and supply.

Industry 4.0 provides for changes in the way in which we work. It makes our work smarter and faster and in most cases will save a great deal of money for the factories and businesses which embrace it. For those that do not embrace the fourth industrial revolution, they will be hard pressed to keep up to those who have introduced smarter factories. Better manufacturing, better use of space and better safety results are just a few of the things that Industry 4.0 provides.

For those who embrace Industry 4.0 the results can be faster, better, more profitable results from their business. What's not to love about that.

This is the second in a series. To see # 1 in the series please use this link https://www.linkedin.com/today/author/0_1gNYYer-mY9IO8KGV50j_c?trk=prof-sm

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Guest blog post by Ajit Jaokar

 

Background

 

Dresner advisory services has published  a report on IoT business models. This report covers IoT, Big Data and Analytics. I have been focussing on this subject in my teaching at Oxford University and the Data Science for IoT course . So, it’s nice to see the insights. Forbes has written a good analysis of this report’s findings.  Based on this analysis, I find that the report has some areas I agree but also some surprising omissions. I suspect that the report was based on from survey results – and hence innovation is missed.  For example, the inclusion of Map reduce for IoT is surprising (and I suspect arises from familiarity of survey respondents). For the same reasons, ‘Relational Database support’ is seen to be very important, whereas ‘Real time’ is much less so according to the survey. This is similar to asking a group of Telecom Operators in 2005: ‘Will Skype succeed?’ All would say no .. but the reality does not reflect survey findings. Having said that, there are many other findings and trends that I agree with.  

 

Focus on the Enterprise is correct – but is only part of the story

 

Emphasis on the Enterprise

The emphasis on the Enterprise is accurate. For IoT, consumer gets lot of traction – but the value is in the Enterprise.

However, the word ‘Enterprise’ also encompasses many areas – and each of these verticals have their unique intricacies. If IoT analytics (data) is the main value-add for IoT, then the question is:  How will IoT data will be leveraged in an Enterprise considering IoT itself comprises of multiple silos? In a recent article, I advocated an Enterprise AI layer which will incorporate IoT datasets.

Such a layer is likely to be the best way to integrate the currently small and diverse IoT data into the Enterprise and also cope with the very large data volumes in the near future. So, the emphasis on the Enterprise is only part of the story

 

Data As a Service

The report says: “Sales and strategic planning see IoT as the most valuable today. Strategic planning’s prioritization of IoT is also driven by a long-term focus on how to capitalize on the technology’s inherent strengths in providing greater contextual intelligence, insight, and potential data-as-a-service business models.”

 

As a service data model for IoT is valid. As per a blog from Gartner - IoT creating Data as a Service DaaS opportunities  : “The moral of the story is that organizations should seize the opportunity to grab all the gold (data) and create the rules (algorithms and analytics) they can, both to benefit businesses and scenarios they are already focused upon but also to potentially create new data brokerage businesses. These new offerings may be adjuncts to their core competencies, enabling them to reap the benefits that the IoT revolution is bringing. There are packaged providers, there are evolving marketplaces, there are crowdsourced collections, and there are many basic building blocks an erstwhile organization can implement to capture, manage, slide, dice and provide data brokerage offerings.”

This advice is valid – but will companies spend money speculatively on getting hold of all the IoT data they can if it does not have immediate business payoff? I doubt it. This again leads to the idea of the Enterprise layer for IoT which has a much more tangible payoff  

Data warehousing

Finally, the report emphasises Data Warehousing – but does not say how and why existing Data warehouses will work with IoT. The report says Data warehouse optimization is considered critical or very important to 50% of respondents, making this use case the most dominant in the study“ Again, in the Enterprise AI article, I advocated that the Enterprise AI layer could be seen as an intelligent Data Warehouse

To conclude – Enterprise relevant for IoT – but only with AI  

 

‘Enterprise IoT’ encompasses many areas – each of these verticals have their unique intricacies. If IoT analytics (data) is the main value-add for IoT, then the question is:  How will IoT data will be leveraged in an Enterprise considering IoT itself comprises of multiple silos? IoT is also a complex domain and there are  many differences between traditional Data Science and Data Science for IoT.To actually implement Data Science for IoT at an Enterprise level, you would need to consider Enterprise AI layer which will incorporate IoT datasets.

 

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IoT as a Metaphor

Originally posted on Data Science Central

What exactly is “IoT”? Internet of Things, yes; but what does that mean?

Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?

Being able to connect things together is “table stakes” at the intelligence augmentation game. What you are able to do with this network is the real story.

 

IoT is an enabler of THREE high-level objectives:

(1)    DO MORE: Whether machines producing more (high throughput) because of less breakdowns or a weekend athlete burning more calories because she is able to keep her heart rate in the fat-burning zone, we are accomplishing more.

(2)    HIGHER QUALITY: By monitoring environmental pollution, cities restrict automobile access into city center for better health outcomes over time.

(3)    BETTER USER EXPERIENCE: In the near future, mass customization will allow me to find eyeglasses with perfect fit at low cost based on video inputs at connected additive manufacturing facilities.

 

All three objectives can be met because Network of Sensors & Devices exist and will grow. But Networking of Sensors & Devices does NOT capture what has to happen so that we can DO MORE at HIGHER QUALITY & BETTER UX! The information that rattles around the network has to be consumed properly, insights generated and decisions made.

A functional description of IoT is a network that “processes” information in the network towards our three objectives. One can see that “processing” is enabled by a multitude of technologies: IP networking, wireless, chipsets, protocols, security, cloud computing, database technologies, analysis software, visualization and so on. Subsuming this under “IT or Information Technology” and keeping it aside for the moment, the “higher-level” processing involved is Applied Data Science!

Applied Data Science is a tautology. Data Science IS the applied aspects of many pure sciences (see “What exactly is Data Science?” for details). Beyond the network of sensors & devices and base IT technologies partially listed in the last paragraph, what is unique and new in IOT is Data Science applications –Data Science applied with the focus on information extraction, insights generation and prescriptive decisions. There is no identifying name for this *applied* aspect of Data Science but I have been referring to it as “Engineering” Data Science. We use “engineering” in the sense of the applied aspect of any science (Engineering is the applied aspect of Physics, for example).

 

IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science)

 

Each component is critically important to IoT; major advances in all three in recent years have made IoT and its promise real.  Engineering Data Science (EDS) is the youngest and the least mature of the three. Immediate next steps in EDS evolution seem clear to me  (more in Next Stage in IoT revolution – “Continuous Learning”).

When IoT is defined as “(Network of Sensors & Devices) + IT + (Engineering Data Science)”, it seems to pervade ALL industries from my vantage point! What do I mean by that?

 

Let us look at the largest 5 (excluding IT) sectors of S&P 500: Consumer Discretionary, Energy, Financials, Health Care & Industrials.

I have mentioned only a few instances in the right hand column but you can add many more. They all require some data generating mechanism, IT connectivity and decision making software. What this shows me is that IoT has already pervaded and will totally engulf all the businesses! As such, I tend to look at IoT as a technology framework that underpins ALL businesses and industries of the 21st century.

Now, does the exact same IoT serve all these sectors or are there nuanced variations? As I mentioned, EDS is the youngest and fastest evolving portion of IoT today – let us focus where the changes are most rapid.

I have partitioned applied Data Science into three: Industry, Business & Social Data Science. 

As you can see, each application area calls for refinements and adaptations to its verticals. Specialization for each vertical notwithstanding, the three “types” of Data Science are best seen as a unified whole, which we are calling “Engineering Data Science or EDS”. Rapid progress in Engineering Data Science is required to achieve our three goals of “DO MORE at HIGHER QUALITY & BETTER UX” with IoT.

 

IoT is not JUST what GE, Siemens, ABB or Hitachi do! It is a technology framework for all business and industrial technologies going forward. IoT is just a metaphor for this powerful, all-encompassing technology framework.

 

SUMMARY:

  • IoT = DO MORE at HIGHER QUALITY & BETTER UX.
  • IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science).
  • IoT = Technology framework that underpins ALL businesses and industries of the 21st century.

 

 

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

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

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In 2016, many companies are using Industry 4.0 as a buzzword. This doesn’t mean that the old industry has been revolutionized into a new version. On the contrary, this is an extension of what has currently existed, with the dawn of the modern variation arriving about 2010 in Germany.

While the first reference to Industry 4.0 would not occur until 2011, the German Federal Ministry of Education and research began to explore the various trends that were taking place. They wanted to identify things in high level technology that could help to improve the world and boost technology. This would allow those seeking future employment in the industrial sector to have a simplified work experience while allowing us to do more in a fraction of the time.

By 2012, the Germans had collected a great deal of research and they used this information to hold the first presentation. As part of this presentation, they took the smart factory setting and began to showcase some of the potential that was there. This allowed potential customers and industry professionals to gain a deeper understanding of what all was possible. Now machines could almost think and react to real life situations in order to boost effectiveness and help to make the industry more incredible than ever before. The German government was thrilled with the results and they began to boost funding to the research in the hopes it would advance their country and help them to become a frontrunner during the Industrial Revolution.

Once the research was determined and there was an understanding that the internet was far more powerful than originally believed, the incorporation of information relay over the internet helped to further propel the internet of things, which was already gaining significant prominence in other countries at this time. Funding was not at a new high through Germany’s manufacturing industry and the invention of the process was solidifying. It was at this time that the Platform of Industry 4.0 was introduced. But it was still a ways from where we find Industry 4.0 today.

In 2014, companies outside of Germany began to step in. There was more virtulization and input from neighboring countries, so that effective work solutions could be created. Decentralization became a key component for the process, and ensuring that digital manufacturing would ultimately benefit from the new processing the most. This is the point where the internet of things became perfectly aligned with the industrial revolution and a sweet harmonious union was formed.

Further evolution occurred as new things began to appear thanks to the research and development that has taken place during the fourth industrial revolution. This includes advanced medical technology, effective cost saving mechanics for production plants and so much more. This is an exciting time in our world to be alive and witness the incredible changes that are taking place.

This is the 1st in a Series - be on the lookout for additional articles on this topic.

For more information about us check out www.internetofthingsrecruiting.com

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The IOT / Big Data and Data Scientists

In recent years, two of the biggest topics of discussion when it comes to technology have been Big Data and the IOT (Internet of Things).On the off chance that all the hype has passed you by, the IOT is a rapidly expanding network of sensors that are internet connected and attached to a vast range of “things.” The internet connection can be either wireless or wired and the potential measurements that could be taken by the sensors are nearly endless. The “things” involved can be virtually any object, whether living or inanimate, to with a sensor can be attached or embedded. Anyone with a smartphone can, in effect, become a living IOT sensor and enables many routine, daily activities to be tracked and analyzed.

The Internet of Things and Big Data clearly have a very intimate connection because these billions of “things” that are internet connected will generate data in unimaginable amounts. The generation of this data alone however, won’t bring about industrial revolution, alter day to day living, or create earth saving technology. Big Data is characterized by what are known as the “four V’s”. They are: volume, variety, velocity, veracity. Putting it simply, the structured and unstructured data (variety) arrives in vast amounts (volume) at high speeds (velocity) and is of uncertain value (veracity). Data processing systems such as Apache’s Hadoop are helpful, but in some cases the human touch is needed. That is where data scientists enter the picture.

It has been speculated that the introduction of artificial intelligence would mean the end of the relatively new profession of data scientist, but that is far from being the case. Machines are being programmed to learn, but the applications of artificial intelligence are limited for the present. One example of the need for data scientists in the IOT, Big Data equation are autonomous vehicles. These vehicles are loaded with sensors that continuously transmit data that allows the vehicle to respond to its surroundings. Analyzing that data and programming the vehicle’s response to certain conditions requires a human with real driving experience.

With the sheer number of “things” that could potentially join the IOT, there will be much less actionable data involved than you might imagine. With data analysts, data scientists, and processing systems such as Hadoop however, the internet of things and Big Data have the potential to change society as we know it.

For More information check out our new website at www.internetofthingsrecruiting.com - or to schedule at call using our schedule link.. https://app.acuityscheduling.com/schedule.php? owner=11427493&appointmentType=468451

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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
 
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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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