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Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. It will play a big part in the IoT. From our friends at R2D3 is a very interesting visual introduction to machine learning. Check it out here

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The Next Big Thing In Big Data: BDaaS

Guest blog post by Bernard Marr

We’ve had software as a service, platform as a service and data as a service. Now, by mixing them all together and massively upscaling the amount of data involved, we’ve arrived at Big Data as a Service (BDaaS).

It might not be a term you’re familiar with yet – but it suitably describes a fast-growing new market. In the last few years many businesses have sprung up offering cloud based Big Data services to help other companies and organizations solve their data dilemmas.

Source for illustration: click here

Some estimate that business IT spending on cloud-based, x-as-a-service activity will increase from about 15% today to 35% by 2021. Given that it is estimated that the global Big Data market will be worth $88 billion by that point, we can see that the forecast value of the BDaaS market could be $30 billion.

So, here I will attempt to give a brief(ish) overview of the concept, as well as examples of how it is being put into practice in real life businesses and organizations around the world.

What is BDaaS?

Big Data refers to the ever-growing amount of information we are creating and storing, and the analysis and use of this data. In a business sense, it particularly refers to applying insights gleaned from this analysis in order to drive business growth.

At the moment, BDaaS it is a somewhat nebulous term, which is often used to describe a wide variety of outsourcing of various Big Data functions to the cloud.

This can range from the supply of data, to the supply of analytical tools with which to interrogate the data (often through a web dashboard or control panel) to carrying out the actual analysis and providing reports. Some BDaaS providers also include consulting and advisory services within their BDaaS packages.

So, in many ways, BDaaS encompasses elements of what has become known as software as a service, platform as a service, data as a service, and so on – and applies them to solving Big Data problems.

Why is BDaaS useful?

There are several advantages to outsourcing or virtualizing your analytics activities involving large datasets.

The popularity of Hadoop has to some extent democratized Big Data – anyone can use cheap off-the-shelf hardware and open source software to analyze data, if they invest time learning how. But most commercial Big Data initiatives will still involve money being spent up front on components and infrastructure. When a large company launches a major initiative, this is likely to be substantial.

On top of upfront costs, storing and managing large quantities of information requires an ongoing investment of time and resources. When you use BDaaS, all of the techy “nuts and bolts” are, in theory, out of sight and out of mind, leaving you free to concentrate on business issues.

BDaaS providers generally take this on for the customer – they have everything set up and ready to go – and you simply rent the use of their cloud-based storage and analytics engines and pay either for the time you use them or the amount of data crunched.

Additionally BDaaS providers often take on the cost of compliance and data protection. When the data is stored on their servers, they are (generally) responsible for it.

Who provides and uses BDaaS?

A good example is IBM’s Analytics for Twitter service, which provides businesses with access to data and analytics on Twitter’s 500 million tweets per day and 280 million monthly active users.

As well as the “firehose” of tweets it provides analytics tools and applications for making sense of that messy, unstructured data and has trained 4,000 consultants to help businesses put plans into action to profit from them.

Another is agricultural manufacturers John Deere, which fits all of its tractors with sensors that stream data about the machinery as well as soil and crop conditions to the MyJohnDeere.com and Farmsight services. Farmers can subscribe to access analytical intelligence on everything from when to order spare parts to where to plant crops.

The arrival of Apple’s Watch – perhaps the device that will bring consumer wearables into the mainstream – will doubtlessly bring with it a tsunami of new BDaaS apps. They will soak up the data from the presumed millions of people who will soon be using it for everything from monitoring their heart rate to arranging their social calendar to remote controlling their home entertainment. Then they will find innovative ways to package it and sell it back to us. Apple and IBM have just announced their collaboration on a big data health platform.

In sales and marketing, BDaaS is increasingly playing its part, too. Many companies now offer customer profiling services, including Acxiom – the world’s biggest seller of direct marketing data. By applying analytics to the massive amount of personal data they collect, they can more effectively profile us as consumers and hand their own customers potential leads.

Amazon’s AWS as well as Google’s AdSense and AdWords are better known services that would also fall under the banner. They are all used by thousands of small to medium-sized businesses to host data infrastructure, and target their marketing at relevant niches where potential customers could be lurking.

The future of BDaaS?

The term may be rather unwieldy and inelegant (I’ve written before that I’m not even particularly a fan of the term Big Data, so BDaaS is a further step into the ridiculous) but the concept is rock solid.

As more and more companies realize the worth of implementing Big Data strategies, more services will emerge to support them. Data analysis can and generally does bring positive change to any organization that takes it seriously, and this includes smaller scale operations which won’t have the expertise (or budget to develop that expertise) to do it themselves.

With the growth in popularity of software as a service, we are increasingly used to working in a virtualized environment via a web interface, and integrating analytics into this process is a natural next step. We can already see that it is making Big Data projects viable for many businesses that previously would have considered them out of reach – and I think it is something we will see and hear a lot more about in the near future.

AboutBernard Marr is a globally recognized expert in big data, analytics and enterprise performance. He helps companies improve decision-making and performance using data. His new book is Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve PerformanceYou can read a free sample chapter here.

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The ‘connected’ car, not to be confused with the self-driving, autonomous car, is defined as any vehicle equipped with Internet access that allows data to be sent to and from the vehicle.

Since the automobiles were invented, car makers have been trying to add features which may reduce driver error. Today’s car has the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour.

Digital technology is also changing how we use and interact with our cars, and in more ways than you probably realize.

The market for smart vehicles is certainly set for takeoff and many analysts predict they could revolutionize the world of automobiles in much the same way smartphones have changed the face of telecommunications.

Is your car connected to the Internet? Millions of vehicles around the world had embedded Internet access, offering their drivers a multitude of smart options and benefits. These include better engine controls, automatic crash notifications and safety alerts, to name just a few. Owners can also interact with their connected vehicles through apps from any distance.

Vehicle-to-vehicle communications, for example, could help automobiles detect one another's presence and location to avoid accidents. That could be especially useful when it comes to driver-less cars - another advance already very much in development. Similar technology could help ensure that cars and their drivers slow down for school zones or stop at red lights.

Connected vehicle technologies provide the tools to make transformational improvements in safety, to significantly reduce the number of lives lost each year through connected vehicle crash prevention applications.

The Connected Car will be optimized to track and report its own diagnostics, which is part of its appeal for safety conscious drivers.

Connected cars give superior Infotainment services like navigation, traffic, weather, mobile apps, emails and also entertainment.

Auto insurers also have much to gain from the connected car revolution, as personalized, behavior based premiums are already becoming new industry standard.

OEMS and dealers must embrace the  Big Data revolution now, so they’re ready to harness the plethora of data that will become available as more and more connected cars hit the roads.

Cloud computing powers much of the audio streaming capabilities and dashboard app functions that are becoming more commonplace in autos.

In the next 5 years it seems that non-connected cars will become a thing of the past.  Here are some good examples of connected cars:

  • Mercedes-Benz models introduced this year can link directly to Nest, the Internet of Things powered smart home system, to remotely activate a home’s temperature controls prior to arrival.
  • Audi has developed a 12.3 inch, 3d graphics fully digital dashboard in partnership with NVIDIA.
  • Telematics Company OnStar can shut down your stolen car remotely helping police solve the case.
  • ParkMe covers real time dynamic parking information and guide drivers to open parking lots and meters. It if further integrating with mobile payments.

The next wave is driver-less, fully equipped and connected car, where there will be no steering wheels, brakes, gas pedals and other major devices. You just have to sit back, relax and enjoy the ride!!

This article originally appeared here.
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What options do you have for remotely monitoring Water and Fluids with Industrial IoT sensor telemetry?

IIoT or Industrial IoT (Internet of Things) is everywhere. It’s across all industries, from high tech transport, to natural resources and governments. IIoT software and hardware is deployed for numerous, varying applications, and it’s critical to understand just what the customer needs. Especially since the customer can’t always articulate exactly what the remote monitoring and sensor telemetry should do. According to a study performed by Verizon: the worldwide Internet of Things market spend will grow from $591.7 billion in 2014 to $1.3 trillion in 2019. That’s tremendous.

One of the areas that we’ve seen recent growth is water and fluid monitoring. Water comes to us as a life sustaining asset and also as a force of destruction. The utility of water needs to be measured and monitored in order to effectively and efficiently use our greatest natural resource. Similarly, monitoring the destructive force of water can be just as important. Let’s talk about the different ways that you can measure and monitor water!

 

Flow Meters

Flow meters calculate the amount of water that flows through them. Flow meters are everywhere from your house to your office, to anywhere and everywhere water is used. Measuring water flow is a need recognized across industries, from agriculture to commercial, pharmaceuticals, and oil and gas. Flow meters in an IIoT solution provide not only a total flow amount, but allow you to utilize real time data to predict and adjust consumption. Further still, real time analysis allows immediate recognition of catastrophic events such as a burst pipe. The analysis will be drawn out further to establish predictive failure behavior and potentially prevent massive water loss issues like the ones that happened in Los Angeles and Hollywood Hills.

 

Water Detection

Almost certainly this one is all about protecting assets. There are essentially four ways that we have used to detect presence, quantity, volume, and levels of water. Each of these fits quite well for a particular purpose. They also compliment each other nicely!

 

Presence of Water: The Rope Sensor

Rope sensors are great and they come in a variety of lengths. A rope sensor will tell you if you have water present at any point along the sensor. Imagine a large trailer with rope sensors running along the bottom of the trailer. If you have a spill in that trailer, truck, or vehicle and any fluid reaches the rope sensor, then you’ll receive an alert and immediately know there’s a problem.

Rope sensors are also great for flood detection. Because you can purchase these sensors in practically any length, you can lay them across a flood channel. If any portion of that rope sensor gets wet then you know you have water present. However, in terms of flood detection rope sensors will tell you if there is water, but they won’t tell you how much.

 

Presence of Water: Yes or No

If your rope sensor went off on a flood channel you might want to know how much water is flowing through. Depending on the lay of the land there are a number of different applications that we use to provide this information.

 

Ultrasonic, Ultrasound, Pulse, and Radar Sensors

If you have a fixed structure next to or going over a flood channel then a great solution is an ultrasonic sensor. Essentially, once the sensor is fixed in place it will continuously ping the ground. When the reading between the sensor and the ground becomes more compact, you can calculate that distance and in turn determine how much water is flowing through the channel and the flood level. Also note that radar and ultrasonic fluid level sensors are quite useful for remotely monitoring levels and volumes of liquid products in assets like tanks!

 

Pressure Transducers

Another way that we have measured quantity of water is by using a pressure transducer. A sensor with a membrane sits at the bottom of a water well, lake, or a reservoir, or a flood channel. As the water increases above the sensor so does the pressure on the sensor’s membrane. The higher the pressure the more water you have moving through!

 

Making things Digital

Water metering and water detection are now all IIoT solutions. All of these meters / sensors connect to sensor hub connector hardware that sends data out into the internets and into a cloud data analysis solution. Whether you’re monitoring agriculture / viticulture, oil / gas / mining, municipal water treatment facilities or other water plants, nowadays you can obtain a cost-effective, rapidly deployable monitoring solution.

 

 

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Bi-Weekly Digest, April 15th

Starting this week, we're going to compile the best of IoT Central membership content in our new Bi-Weekly Digest. 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.

Featured Articles

How Will Big Data and IoT Shape the Future of Apps Market? 

By Marcus Jensen

Technological advancements and the surge of mobile platforms have announced the new era of global economy, and the booming app market is expanding with lightning speed. Big Data has a powerful influence on business operation on a global scale, although the rates of adoption are not that convincing. What is more, the advent of the Internet of Things (IoT) means that it will not be long before all household items are equipped with wireless capacity. For an app market, which relies heavily on knowledge and data, particularly user feedback, this has strong implications. It is time to think big in terms of data.

IoT needs automated, hardware-based, localized machine learning for wider deployment and usage

By Asim Roy

As we move towards widespread deployment of sensor-based technologies, three issues come to the fore: (1) many of the these applications will need machine learning to be localized and personalized, (2) machine learning needs to be simplified and automated, and (3) machine learning needs to be hardware-based.

IoT Guidelines Need to Ask Less of Device Manufacturers

By John Berard

The Online Trust Alliance (OTA) has been at the forefront of helping build consumer confidence in the technology products that have helped remake our day. So, it was no surprise it moved to create a set of guidelines around the products and services that are part of the Internet of Things (IoT).

How the IoT will Impact Businesses in 2016

By TechJB

The Internet of Things has been labeled as the next ‘Industrial Revolution,’ by many experts that have predicted how it will be able to change the way various industries, businesses, consumers, and even governments, interact with the physical world in the future. Here are other ways on how the IoT will impact, challenge, and change business in 2016.

10 Case Studies for the Industrial Internet of Things

By David Oro

It’s still early days for the IoT but everyday a little part of its burgeoning ecosystem becomes a factor in our lives, whether we know it or not. From industrial tools to farming to cities to grocery aisles and everything in between, the IoT is there. Here are 10 IoT case studies that show just where some of these technologies and applications are being applied.

There will be EXACTLY 18.995 billion connected #IoT devices by 2020

Guest blog post by Eduardo Siman

If you follow news about the Internet of Things, you will have read quite a few articles that attempt to predict the number of connected devices by the year 2020. Eduardo breaks it down with this chart. 

Additional Links
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As we move towards widespread deployment of sensor-based technologies, three issues come to the fore: (1) many of the these applications will need machine learning to be localized and personalized, (2) machine learning needs to be simplified and automated, and (3) machine learning needs to be hardware-based. 

Beginning of the era of personalization of machine learning

Imagine a complex plant or machinery being equipped with all kinds of sensors to monitor and control its performance and to predict potential points of failure. Such plants can range from an oil rig out in the ocean to an automated production line. Or such complex plants can be human beings, perhaps millions of them, who are being monitored with a variety of devices in a hospital or at home. Although we can use some standard models to monitor and compare performance of these physical systems, it would make more sense to either rebuild these models from scratch or adjust them to individual situations. This would be similar to what we do in economics. Although we might have some standard models to predict GDP and other economic variables, we would need to adjust each one of them to individual countries or regions to take into account their individual differences. The same principle of adjustment to individual situations would apply to physical systems that are sensor-based. And, similar to adjusting or rebuilding models of various economic phenomena, the millions of sensor-based models of our physical systems would have to be adjusted or rebuilt to account for differences in plant behavior. We are, therefore, entering an era of personalization of machine learning at a scale that we have never imagined before. The scenario is scary because we wouldn’t have the resources to pay attention to these millions of individual models. Cisco projects 50 billion devices to be connected by 2020 and the global IoT market size to be over $14 trillion by 2022 [1, 2].

 

The need for simplification and automation of machine learning technologies 

If this scenario of widespread deployment of personalized machine learning is to play out, we absolutely need automation of machine learning to the extent that requires less expert assistance. Machine learning cannot continue to depend on high levels of professional expertise.  It has to be simplified to be similar to automobiles and spreadsheets where some basic training at a high school can certify one to use these tools. Once we simplify the usage of machine learning tools, it would lead to widespread deployment and usage of sensor-based technologies that also use machine learning and would create plenty of new jobs worldwide. Thus, simplification and automation of machine learning technologies is critical to the economics of deployment and usage of sensor-based systems. It should also open the door to many new kinds of devices and technologies.

 

The need for hardware-based localized machine learning for "anytime, anywhere" deployment and usage 

Although we talk about the Internet of Things, it would simply be too expensive to transmit all of the sensor-based data to a cloud-based platform for analysis and interpretation. It would make sense to process most of the data locally. Many experts predict that, in the future, about 60% of the data would be processed at the local level, in local networks - most of it may simply be discarded after processing and only some stored locally. There is a name for this kind of local processing – it’s called “edge computing” [3].

The main characteristics of data generated by these sensor-based systems are: high-velocity, high volume, high-dimensional and streaming. There are not many machine learning technologies that can learn in such an environment other than hardware-based neural network learning systems. The advantages of neural network systems are: (1) learning involves simple computations, (2) learning can take advantage of massively parallel brain-like computations, (3) they can learn from all of the data instead of samples of data, (4) scalability issues are non-existent, and (4) implementations on massively parallel hardware can provide real-time predictions in micro seconds. Thus, massively parallel neural network hardware can be particularly useful with high velocity streaming data in these sensor-based systems. Researchers at Arizona State University, in particular, are working on such a technology and it is available for licensing [4].

 

Conclusions

Hardware-based localized learning and monitoring will not only reduce the volume of Internet traffic and its cost, it will also reduce (or even eliminate) the dependence on a single control center, such as the cloud, for decision-making and control. Localized learning and monitoring will allow for distributed decision-making and control of machinery and equipment in IoT.

We are gradually moving to an era where machine learning can be deployed on an “anytime, anywhere” basis even when there is no access to a network and/or a cloud facility.

 

References

  1. Gartner (2013). "Forecast: The Internet of Things, Worldwide, 2013."

         https://www.gartner.com/doc/2625419/forecast-internet-things-worldwide-

     2. 10 Predictions for the Future of the Internet of Things

     3. Edge Computing

     4. Neural Networks for Large Scale Machine Learning

 

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It’s still early days for the IoT but everyday a little part of its burgeoning ecosystem becomes a factor in our lives, whether we know it or not. From industrial tools to farming to cities to grocery aisles and everything in between, the IoT is there. Here are 10 IoT case studies that show just where some of these technologies and applications are being applied.

1) Bytes and Bushels - Farming on an Industrial Scale

Farming and IoT seem to be the leading implementations on an industrial scale. I wrote on this last year, but the two New York Times pieces on Tom Farms, a multi-generation, family owned farm in North Indiana, is still one of the most comprehensive, and personal, IoT case studies I’ve seen to date. And it’s not just words, be sure to watch the multimedia video. Stories are here and here.

2) The Tesla IoT Car: Case Study

teslamodelsinterorio.jpg

MITCNC, the MIT Club of Northern California, is the regional alumni club of Massachusetts Institute of Technology in Northern California. They have a blog at https://blogmitcnc.org/ where they post on emerging trends and discoveries in science and technology. Displaying their best Car & Driver reviewer, while keeping their propeller hats on to look at IoT, data, privacy and security, this is a unique look at the most talked about car this century. Story here.

3) GE’s Big Bet on Data and Analytics

geimageengine.jpgHere’s a timely new case study from MIT Sloan Management Review that looks at how GE is seeking opportunities in the Internet of Things with industrial analytics. GE is leading the development of a new breed of operational technology (OT) that literally sits on top of industrial machinery. Long known as the technology that controls and monitors machines, OT now goes beyond these functions by connecting machines via the cloud and using data analytics to help predict breakdowns and assess the machines’ overall health. I’m really glad to see someone dive into this as I think GE’s big swing is still not yet fully appreciated. It soon will be. Case study here.

4) Can a Cow be an IoT Platform

One of my favorite stories on the IoT is penned by Bill Vorhies, President & Chief Data Scientist at Data-Magnum. It’s been on IoT Central for a while now, but I thought it important to include in this collection. Bill’s report recaps Microsoft’s Joseph Sirosh for a surprising conversation about a farmer’s dilemma, a professor’s ingenuity and how cloud, data and devices came together to fundamentally re-imagine an age old way of doing business. You can read Bill’s post here or watch the entertaining video below.

5) Global Smart Cities

In 2013, the UK government’s Department for Business, Innovation and Skills commissioned a study that looked at six global cities that are paving the way in smart city investment. It looked at how Chicago, Rio De Janeiro, Stockholm, Boston, Barcelona and Hong Kong tackled particular challenges when responding to the opportunities that a ‘smart city’ and private sector innovators might bring. Worth a read. Case study is here.

tvlightbvsmartcity.jpg

Photo courtesy of TVILIGHT BV

6) PTC Thingworx - All Traffic Solutions

Thingworx, a PTC company, has an IoT platform designed to build and run IoT applications, and enable customers to transform their products and services, innovate and unlock new business models. They have a plethora of case studies, but one that caught my eye was on All Traffic Solutions. The company has been at the forefront of the wireless market for over a decade but now sells its traffic safety products throughout the United States and 20 countries globally. That reach has provided a good deal of field-based insight that, over the last five years, All Traffic Solutions has channeled into developing innovative new web-based and IoT-connected signs that are incredibly smart, yet simple to use, adding significant value to the company’s hardware for its customers. Case study here.

7) Stanley Black and Decker

Managing a complex manufacturing facility is a challenge and this case study from Cisco showcases how Stanley Black & Decker operates one of its largest tool manufacturing plants in Reynosa, Mexico, which serves the North American market. Opened in 2005, the Reynosa plant primarily manufactures dozens of products, such as jigsaws, planers, cordless drills, floodlights, and screwdrivers for the DeWALT brand and lawnmowers for the Black & Decker brand. With 40 multiproduct manufacturing lines and thousands of employees, the plant produces millions of power tools each year. This case study shows how IoT technologies help with production visibility and flexibility. Case study here. Great video below.

8) SLAC National Accelerator Laboratory

sllabs.jpg

Since its opening in 1962, SLAC National Accelerator Laboratory has been helping create the future. Six scientists have been awarded Nobel prizes for work done at SLAC, and more than 1,000 scientific papers are published each year based on research at the Palo Alto-based lab. The team is now working on a future plan to take data from all intelligent sensors that monitor the vast systems at SLAC and feed the data into the cloud where it can be processed, analyzed, and delivered back to control engineers. Case study here.

9) The Supermarket of the Future: Designing for humans

It’s not just about technology, but applying technology to improve the human experience. This case study on Italy’s biggest grocery cooperative shows how it might be done...and I like it. Coop Italia’s “supermarket of the future,” designed by Carlo Ratti, has won rave reviews, thanks to a digital design that created a more human shopping experience using a range of off-the-shelf technology. Read more about it here.

10) IoT for Electronic Medical Records

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The need to cut cost, improve medical care, and adopt electronic medical records (EMR) is driving hospitals to implement information technology solutions that streamline procedures such as billing, medical imaging, and electronic medical records processing. In this case study from Intel, it shows how their partner NEXCOM developed a medical informatics solution based on technologies from the Internet of Things to help overcome communication barriers between medical devices and IT networks. The solution turns medical device data into electronic medical records and sends them to the hospital’s private cloud, where data analytics can be performed to better evaluate a patient’s condition. Read more about it here.

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Matt Turck, a venture capitalist at FirstMark, has mapped out the Internet of Things Landscape for 2016.

Matt notes "The IoT today is largely at this inflection point where “the future is already here but it is not evenly distributed”. From ingestibles, wearables, AR/VR headsets to connected homes and factories, drones, autonomous cars and smart cities, a whole new world (and computing paradigm) is emerging in front of us. But as of right now, it just feels a little patchy, and it doesn’t always look good, or work great – yet."

The chart above is great, but it's his thoughtful and detailed blog post that's definitely worth your time. He covers the booming investment, the seemingly glacial pace for the end user, jockeying by large corporations, and what it all means for start-ups. 

 

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The Online Trust Alliance (OTA) has been at the forefront of helping build consumer confidence in the technology products that have helped remake our day. So, it was no surprise it moved to create a set of guidelines around the products and services that are part of the Internet of Things (IoT).

That framework took another step forward this month when the OTA released its Trust Framework of 30 recommendations for consumer-facing IoT companies seeking to build out this network of connected devices.

At first blush, the list of recommendations seems complete, but a longer look suggest is may be both too long and not long enough.

Too long because any list of 30 “must-haves” becomes more a barrier to entry than a glide path to market share. Too short because the biggest danger to consumer privacy, security and trust is a product no longer supported by a company that has moved on or shut down.

Too long? Rather than seek to create a granular set of prescriptive recommendations, it would be better to focus on a shorter and more effective set of requirements. I count five: encryption, authentication, fault tolerance, security and user control to review, change or delete. The ability to easily integrate and interoperate might be a sixth, but the market for consumer IoT is not so mature as to make that necessary – just yet. 

Too short? The biggest dangers to consumers are IoT products no longer supported, either because it didn’t gain traction or the company has ceased to operate. It does not take long for a technology product to develop security holes if upgrades are not made. These holes are the source of the greatest vulnerability for the growth of the IoT market.

The OTA framework doesn’t answer all the questions raised by the expansion of the IoT, but it ought to be a real conversation starter – both for consumers and industry.

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Can we use HTTP for IoT

Irrespective of how people and companies view IoT, everybody agrees that it will dwarf the internet we see today in very near future. Industry estimates that there will around 50 billion connected devices by 2020. With so many connected devices talking among themselves we need very robust protocols which will work in the real world

 

HTTP is the workhorse of the world wide web. Its the common standard according to which all the browsers communicate with all the servers. Can devices use HTTP for communication?

 

Usually HTTP runs on the top of TCP and has a big header

A bare minimum GET request for HTTP 1.1 is

 

GET / HTTP/1.1

Host: www.example.com

 

The above request fetches the resource at ‘/’. Each new line character is 2 bytes long (CRLF) and the last line should be a new line character, so there is an overhead of 25 characters to fetch a single resource

 

The minimal reply is also similarly long, its

 

HTTP/1.1 200 OK

Content-Length: 1

Content-Type: text/plain

 

a

 

thats for replaying with a single character ‘a’. That is an overhead on 64 characters.

 

Each extra byte that needs to be transmitted incurs a cost on the battery life which is a very precious commodity for embedded devices

 

We have to keep in mind that all the data is passed as clear text without any encryption across the channel, HTTPS is used is used to overcome the problem of security but this adds another overhead of the SSL/TLS channel, handshake and certificate examination.

 

In the real world scenario where the communication channels and often unreliable and bandwidths limited, this much overhead is too much of a baggage.

 

Apart from that, HTTP essentially works under request response model, where clients can only push data to a server and there is no way for the server to connect back to the client unless the client also implements the server. This is an excellent way to get data from many and not the best when you want one to many communication moreover it would be impossible for a remote sensor to be aware of the events in real time.

So HTTP clearly cannot be used used and we need a protocol which is more suited for IoT.

 

Constrained Application Protocol (CoAP) is one such protocol which is designed for the constrained devices. The protocol extensively uses bit fields and mappings from strings to integers to reduce the number of bytes, moreover packets are easy to generate and can be parsed easily. It lets the clients get the updates in realtime by extending the HTTP request model and adding the ability to observe a resource.

 

CoRE, the group which designed the protocol has also defined mapping of CoAP with HTTP, this makes it easier to build proxies which will give access to CoAP resources via HTTP.

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This is an interesting resource for data scientists, especially for those contemplating a career move to IoT (Internet of things). Many of these modern, sensor-based data sets collected via Internet protocols and various apps and devices, are related to energy, urban planning, healthcare, engineering, weather, and transportation sectors. 

Sensor data sets repositories

Originally posted on Data Science Central

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RSA Conference 2016, the world’s leading information security conferences and expositions, kicked off its annual show today at San Francisco’s Moscone Center. In its 25th year, the conference brings together the top information security professionals and business leaders to discuss emerging cybersecurity trends and formulate best strategies for tackling current and future threats.

According to Britta Glade of the RSA Conference, during the course of this year’s review process they collectively looked at the "forest" of the submissions together and found that the Internet of Things was the #1 trend that stood out.

Last year they saw a huge uptick in IoT submissions, but this year it moved front and center.  She noted “While last year’s submissions tended to be "observational," this year we seem to have moved into the "solutioning" phase of the maturity curve, evidenced by a slew of new submitting companies—organizations that directly service end consumers and haven’t traditionally participated in our call-for-speaker process.”

Building on top of IoT, conference organizers also saw increased submissions on Industrial Control Systems and the Industrial Internet of Things. In the past, the sessions focused on this just didn’t gain attention. But one year makes a difference as many of the "things" coming alive and online, such as robots, sensors, building automation, are still based on old security protocols and approaches, and breaches here have the very real potential to trigger large-scale disasters.

Late last year when I posted 50 Predictions for the Internet of Things in 2016, security dominated. With the RSA Conference starting today, here’s a recap of some of those IoT security predictions.

Nathaniel Borenstein, inventor of the MIME email protocol and chief scientist Mimecast

“The maturation of the IoT will cause entirely new business models to emerge, just as the Internet did. We will see people turning to connected devices to sell things, including items that are currently "too small" to sell, thus creating a renewed interest in micropayments and alternate currencies. Street performers, for example, might find they are more successful if a passerby had the convenience of waving a key fob at their "donate here" sign. The IoT will complicate all aspects of security and privacy, causing even more organizations to outsource those functions to professional providers of security and privacy services.”

Mark Coderre, National Practice Director, OpenSky

“Attacks on connected cars, connected medical devices, and connected critical infrastructure have all hit the headlines in the recent past; and this is just the tip of the iceberg. The Internet of Things is proving to be a treasure trove for hackers. When developing networked devices, manufacturers are still placing more value on features than on security. "Security by design" must become an integral factor in development so that innovations win over increasingly security-conscious users. Additionally, the relevance of Cyber Threat Intelligence (CTI), as a part of a proactive information security program, will become essential for information security. In response to increasingly dynamic threat situations, it is critical for organizations to be able to identify evolving methods and emerging technology trends used by the cybercriminal, and then to continually assess their capability in this regard. Because many organizations don´t have access to internal specialists, they will need to turn to external experts from the CTI sector. Effective cyber security will require knowledge and understanding of the capabilities and intent of threat actors. Who are they? What do they want? What can they do? Organizations will define threat more specifically (i.e. less reliance on vague terms like "vulnerabilities"). We will see an emphasis on threat actors with means, motive, and opportunity being tracked. Understanding motive will become crucial for prioritizing resources.

Laurent Philonenko, CTO, Avaya

“Surge in connected devices will flood the network – the increasing volume of data and need for bandwidth for a growing number of IoT connected devices such as healthcare devices, security systems and appliances will drive traditional networks to the breaking point. Mesh topologies and Fabric-based technologies will quickly become adopted as cost-effective solutions that can accommodate the need for constant changes in network traffic.”

Lila Kee, Chief Product Officer and Vice President, Business Development, GlobalSign

“Prediction: PKI becomes ubiquitous security technology within the Internet of Things (IoT) market. It's hard to think of a consumer device that isn't connected to the Internet these days - from our baby monitors to our refrigerators to our fitness devices. With the increase of connected devices of course comes risk of exposing privacy and consumer data. But, what happens when industrial devices and critical infrastructure connect to the Internet and get hacked? The results can be catastrophic. Security and safety are real concerns for the Internet of Things (IoT) and especially in the Industrial Internet of Things (IIoT). Regarding security, the industrial world has been a bit of a laggard, but now equipment manufacturers are looking to build security in right at the design and development stages. Unless the security challenges of IIoT can be managed, the exciting progress that has been made in this area of connected devices will slow down dramatically. PKI has been identified as a key security technology in the IIoT space by the analyst community and organizations supporting the IIoT security standards. In 2016, we expect that PKI will become ubiquitous security technology within the IoT market. There will be an increased interest in PKI, how it plays in the IoT market and how it needs to advance and scale to meet the demands of billions of devices managed in the field.”

Lasse Andresen, CTO, ForgeRock

“Chip to cloud (or device to cloud) security protection will be the new normal As business technology advances, the security data chain continues to grow, presenting an increasing number of opportunities for hackers to break in. With most data chains now spanning the full spectrum of chip, device, network and cloud (plus all stages in between), many organizations are starting to realize a piecemeal approach to protection simply isn't effective. This realization is spurring the adoption of more 'chip to cloud' security strategies, starting at the silicon level and running right through to cloud security. In this model, all objects with online capabilities are secured the moment they come online, meaning their identity is authenticated immediately. In doing so, it eliminates any window hackers have to hijack the identity of unsecured objects, thus compromising the entire data chain via a single entry point.”

Thorsten Held, Co-Founder and Managing Partner, whiteCryption Corp.

“Ransomware, a means whereby a hacker takes over a device and demands a ransom to remove the restrictions, will creep into biomedical devices in 2016. To thwart life-threatening consequences, medical device manufacturers will be looking for diverse ways to address these types of security flaws using more stringent, agile security solutions against the malware threats.”

Sam Rehman, Chief Technology Officer, Arxan Technologies

“Security regulation will make a meaningful impact for medical and other IoT devices: Regulatory requirements have generally been viewed as helping to drive organizations to meet minimum security standards. However, the overall security effectiveness or impact of regulatory requirements has been nominal. We can expect to see a much more meaningful advancement in the rigor of security requirements laid down by the regulators in 2016. This is partly due to accelerated advancements in public-private threat intel-sharing, and the regulators' acknowledgement of the need to seek out cutting-edge threat data and security best practices from the organizations that are on the front lines of defending against them. For example, in IoT, the FDA is making significant improvements in beefing up minimum security requirements for medical devices, which could otherwise pose grave safety risks to people, care providers, and medical device manufacturers that depend on their trusted operation. Since the vertical markets are so intimately interconnected, we will also see more teeth behind enforcement of security requirements.

Marty P. Kamden, CMO, NordVPN.com

“While facing the major transformation of our daily lives because of IoT, we are not completely ready to face related security issues. Since IoT networks will significantly grow in 2016, privacy and security issues related to web-enabled devices will mirror this change. For example, in August of 2015 hackers remotely seized control of over a million Chrysler automobiles, showing ability of having the full control of the cars – activating the windshield wipers, turning the radio and air conditioning on or disengaging the car’s transmission. To start tackling increasing online security threats, there are simple security measures that every Internet user should learn about, one of them being VPN (Virtual Private Network). VPNs will be increasingly popular in 2016 as security and privacy issues online will become more prominent, encouraging people to start encrypting their devices' online data, securing transfer of sensitive data, etc. NordVPN, one of the most advanced VPN service providers on the market, 256-bit AES encryption, is available on 6 devices on one account and has zero log policy.”

Ian Worrall, CEO, Encrypted Labs, Inc.

"The Blockchain has the ability to transform business similar to the Internet. With IoT, a major issue inhibiting its growth is how to manage the vast amount of data that will be stored around it. I think the answer to this is by leveraging distributed system technologies such as permissioned-server networks (Private Blockchains) or maybe even utilizing the Bitcoin Blockchain. A key aspect of this is inter-corporate collaboration between the networks of big data companies. This is crucial because the larger a single datacenter (one company) becomes, the harder it is to manage & secure. To do so efficiently it would involve (in some cases) competitors working together. This not only facilitates the management of this data, but secures it more effectively through distributed storage encryption. The companies willing to collaborate will succeed, while those overly competitive to control the space will inevitably fail long-term and short-term are impeding industry growth.”

Trevor Daughney, EVP, INSIDE Secure

"IoT device makers are realizing that they need to secure IoT devices to protect their reputations and customers. In 2016, IoT device manufacturers will pivot from asking 'why is security needed' to asking 'how do I implement security.' They will look to control data access and protect data at-rest, in-motion and in-process using a combination of software and hardware security measures."

More thoughts on IoT security can be found in our post here.

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Last week Gartner identified the Top 10 Internet of Things Technologies for 2017 and 2018.

Nick Jones, vice president and distinguished analyst at Gartner, said, "The IoT demands an extensive range of new technologies and skills that many organizations have yet to master. A recurring theme in the IoT space is the immaturity of technologies and services and of the vendors providing them. Architecting for this immaturity and managing the risk it creates will be a key challenge for organizations exploiting the IoT. In many technology areas, lack of skills will also pose significant challenges."

 

 

Here are the top 10 IoT technologies for 2017 and 2018 according to Gartner:

IoT Security

The IoT introduces a wide range of new security risks and challenges to the IoT devices themselves, their platforms and operating systems, their communications, and even the systems to which they're connected. Security technologies will be required to protect IoT devices and platforms from both information attacks and physical tampering, to encrypt their communications, and to address new challenges such as impersonating "things" or denial-of-sleep attacks that drain batteries. IoT security will be complicated by the fact that many "things" use simple processors and operating systems that may not support sophisticated security approaches.

"Experienced IoT security specialists are scarce, and security solutions are currently fragmented and involve multiple vendors," said Mr. Jones. "New threats will emerge through 2021 as hackers find new ways to attack IoT devices and protocols, so long-lived "things" may need updatable hardware and software to adapt during their life span."

IoT Analytics

IoT business models will exploit the information collected by "things" in many ways — for example, to understand customer behavior, to deliver services, to improve products, and to identify and intercept business moments. However, IoT demands new analytic approaches. New analytic tools and algorithms are needed now, but as data volumes increase through 2021, the needs of the IoT may diverge further from traditional analytics.

IoT Device (Thing) Management

Long-lived nontrivial "things" will require management and monitoring. This includes device monitoring, firmware and software updates, diagnostics, crash analysis and reporting, physical management, and security management. The IoT also brings new problems of scale to the management task. Tools must be capable of managing and monitoring thousands and perhaps even millions of devices.

Low-Power, Short-Range IoT Networks

Selecting a wireless network for an IoT device involves balancing many conflicting requirements, such as range, battery life, bandwidth, density, endpoint cost and operational cost. Low-power, short-range networks will dominate wireless IoT connectivity through 2025, far outnumbering connections using wide-area IoT networks. However, commercial and technical trade-offs mean that many solutions will coexist, with no single dominant winner and clusters emerging around certain technologies, applications and vendor ecosystems.

Low-Power, Wide-Area Networks

Traditional cellular networks don't deliver a good combination of technical features and operational cost for those IoT applications that need wide-area coverage combined with relatively low bandwidth, good battery life, low hardware and operating cost, and high connection density. The long-term goal of a wide-area IoT network is to deliver data rates from hundreds of bits per second (bps) to tens of kilobits per second (kbps) with nationwide coverage, a battery life of up to 10 years, an endpoint hardware cost of around $5, and support for hundreds of thousands of devices connected to a base station or its equivalent. The first low-power wide-area networks (LPWANs) were based on proprietary technologies, but in the long term emerging standards such as Narrowband IoT (NB-IoT) will likely dominate this space.

IoT Processors

The processors and architectures used by IoT devices define many of their capabilities, such as whether they are capable of strong security and encryption, power consumption, whether they are sophisticated enough to support an operating system, updatable firmware, and embedded device management agents. As with all hardware design, there are complex trade-offs between features, hardware cost, software cost, software upgradability and so on. As a result, understanding the implications of processor choices will demand deep technical skills.

IoT Operating Systems

Traditional operating systems (OSs) such as Windows and iOS were not designed for IoT applications. They consume too much power, need fast processors, and in some cases, lack features such as guaranteed real-time response. They also have too large a memory footprint for small devices and may not support the chips that IoT developers use. Consequently, a wide range of IoT-specific operating systems has been developed to suit many different hardware footprints and feature needs.

Event Stream Processing

Some IoT applications will generate extremely high data rates that must be analyzed in real time. Systems creating tens of thousands of events per second are common, and millions of events per second can occur in some telecom and telemetry situations. To address such requirements, distributed stream computing platforms (DSCPs) have emerged. They typically use parallel architectures to process very high-rate data streams to perform tasks such as real-time analytics and pattern identification.

IoT Platforms

IoT platforms bundle many of the infrastructure components of an IoT system into a single product. The services provided by such platforms fall into three main categories: (1) low-level device control and operations such as communications, device monitoring and management, security, and firmware updates; (2) IoT data acquisition, transformation and management; and (3) IoT application development, including event-driven logic, application programming, visualization, analytics and adapters to connect to enterprise systems.

IoT Standards and Ecosystems

Although ecosystems and standards aren't precisely technologies, most eventually materialize as application programming interfaces (APIs). Standards and their associated APIs will be essential because IoT devices will need to interoperate and communicate, and many IoT business models will rely on sharing data between multiple devices and organizations.

Many IoT ecosystems will emerge, and commercial and technical battles between these ecosystems will dominate areas such as the smart home, the smart city and healthcare. Organizations creating products may have to develop variants to support multiple standards or ecosystems and be prepared to update products during their life span as the standards evolve and new standards and related APIs emerge.

If you’re a Gartner client, you can dive deeper into the topic here.



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The Internet of Things at Mobile World Congress

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Photo Credit: GSMA

And so it begins, Mobile World Congress 2016, the annual confab in Barcelona where the telecommunications industry goes to beach themselves for a week.

As with any big event, day one kicks off with a flurry of announcements. Announced today are a bevy of new smartphones, 5G networks as the new frontier, and Facebook’s Mark Zuckerberg making a surprise appearance to talk about virtual reality at Samsung’s event where they showed off Samsung Gear 360 which allows you to make your own virtual reality video with a 360 degree camera.

But what of IoT?  Here’s a rundown:

  • In a sign of the future and the importance of the subject, the organizers of MWC introduced for the first time this year the Internet of Things (IoT) Pavilion.

  • The organizing body of the show, GSMA, is introducing the Mobile IoT Initiative which has the first live demonstrations of Low Power Wide Area (LPWA) solutions in licensed spectrum. Visitors will experience a diverse range of solutions underpinned by three complementary technology standards known as Narrow Band IoT (NB-IoT), Extended Coverage EGPRS (EC-EGPRS) and LTE Machine Type Communication (Cat-M). The Mobile IoT Initiative is supported by 30 of the world’s leading mobile operators, OEMs, chipset, module and infrastructure companies.

  • The organizing body will also showcase several IOT innovations including a “Mobile IoT Vineyard” that shows how mobile technology is helping to keep grapevines and wine stocks at optimum levels.

  • Global M2M Association (GMA) - The GMA will showcase a live demonstration of its Multi-Domestic Service, an innovative global M2M connectivity management solution that significantly simplifies the global deployment, management and operations of M2M and IoT services for large enterprises.

  • Continuing it’s march on leading IoT, Samsung moved forward on advancing their Open IoT Ecosystem with a new partner program and commercial availability of the SAMSUNG ARTIK™ Platform. Samsung’s new Certified ARTIK Partner Program is designed to help developers and companies jump start their development and take their ideas to market faster by working with carefully selected professional service providers and design houses.

  • GE also made an announcement to grow its partner base and expand its Predix platform footprint with the GE Digital Alliance Program. The company says that this is the first ever program dedicated to growing the digital industrial ecosystem. This new alliance program is designed to connect systems integrators, telecommunications service providers, independent software vendors, technology providers and resellers with the technology and digital industrial expertise of GE. GE alliance members will be able to train and certify their developers and begin building industrial apps with Predix, GE’s cloud platform for the Industrial Internet.

  • Visa Inc. announced that it is expanding its Visa Ready program to include Internet of Things (IoT) companies, such as manufacturers of wearables, automobiles, appliances, public transportation services, clothing and almost any other connected device. The Visa Ready Program for IoT will allow emerging IoT companies to join with mobile device manufacturers to evaluate, develop and potentially adopt new payment methods that are already approved by Visa, and can help financial institutions and merchants drive growth by expanding the use and acceptance of electronic payments globally.

  • AT&T will showcase its capabilities as a global integrated provider helping millions around the world connect with leading entertainment, mobile, high-speed Internet and Internet of Things (IoT) solutions. Inside the GSMA Innovation City, visitors will be guided around AT&T solutions that connect consumers and businesses, including AT&T IoT solutions such as connected car, smart cities and real-life industrial IoT use cases.

  • Recently acquired Jasper will be demonstrating how leading enterprises are using its global IoT service platform to drive real-time business transformation, add value for customers and secure their share of the multi-billion dollar IoT market.

  • Sierra Wireless, with partners Axis, Parkeon and Valeo, will showcase its latest innovative solutions in scenarios including how connected parking meters can become multi-service kiosks and enable services such as couponing, city news updates and payment of parking fines.

If you’re at #MWC2016, leave us a comment and let us know what we missed in the IoT buzz.

 

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Photo Credit: GSMA

 

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We are in the midst of the fourth industrial revolution within the Industrial Manufacturing sector. Accenture & General Electric recently published a paper on the Industrial Internet that places spending at $500 billion by 2020 and forecasts growth to a whopping $15 trillion of global GDP by 2030. The Industrial Internet is disrupting almost all industries today with a revolution of possibilities and opportunities across the value chain. The third Industrial revolution ushered in the digital era, but Industry 4.0 has the vision to connect the digital to the analog world in a truly seamless fashion.

Overcoming the challenges of systems architectures of the past

As industrial systems grow larger, their architecture frequently needs to be updated to match the technology capabilities of today. Most industrial systems have been closed systems built on proprietary technology stacks, both the hardware and software. It was impossible to communicate with them: communication worked only within the systems’ own ecosystems. Interoperability has also been challenging, and at times nonexistent, due to incompatible interfaces even among parts of the same family of systems. These systems could analyze real time data and use historical data to make good decisions, but many were built with rigid interfaces that lacked the ability to exchange data with others outside their ecosystem.

Where will we go with Industry 4.0?

As physical and digital worlds come together via exponential growth in analog and digital integrations, those who implement manufacturing control systems will need to understand the complexities of Industry 4.0 and lead the way to simplify them. Most industrial solutions now have embedded control systems that are constantly monitoring and computing based on feedback, so managers can optimize the performance of manufacturing processes. The next step in the evolution is to converge physical and digital processes and data to create a more holistic view not only of the manufacturing process, but also the context in which it operates. Both heavily impact realized outcomes.

The ‘things’ are absolutely vital for the Industrial Internet of Things, but what makes them highly valuable and ‘smart’ is when they provide unique insights and context and inform action by communicating, cooperating and collaborating with each other. Each ‘thing’ works not on its own, but rather in a mesh with others to attain a singular purpose. This also makes ‘things’ more resilient to faults and outages. They can be tuned to use little communication overhead and work in a congested and constrained network. However, an infrastructure to support communication among ‘things’ and addressability over networks and channels must be established via an Integrator or a Gateway.

A key component of Industry 4.0 is the Smart Factory, which should be context aware to help people and machines understand the execution context of the task at hand. This is different from the feedback loop of control systems. Rather, in a Smart Factory, the machine knows the state of operation not just by the position, but also from data that is provided to it from other information sources. This helps workers on the factory floor focus on higher priority tasks within context. For example, if in a warehouse, the sorting system needs to be calibrated after every couple of days, the system will automatically initiate an auto-calibration when it knows that it has no pending activities. The calibration data could be derived from the manufacturer or a local system that maintains it.

Use Case: Applying Predictive Maintenance on Heavy-Duty Gas Turbines

The energy sector offers a good use case for Industrial IoT, and particularly with gas turbines. The primary purpose of a turbine is to generate power. The turbine itself is made up of rotors, blades, exhausts, inlets, brushes, shafts and a variety of control systems that manage fuel injection, power generation, etc. Each of these turbine elements has to be maintained from time to time, usually on a scheduled maintenance cycle. Any scheduled downtime of the turbine has to be managed; otherwise, it is not generating power. By combining data from the array of ‘things’ that can monitor the various parts of the turbine, the frequency of vibration of the blades and rotors, tensile and/or radial stress, and leakage control at the various seals, turbine operators gain insight into the overall turbine health and not just selected aspects. In addition, adding data on environmental conditions, such as temperature fluctuations, humidity, air quality, geography, and fuel quality, provides valuable context on the running condition of a turbine. For example, data on air quality can be used to predict when air filters need to be cleaned or changed. 

Turbine operators can collect and store data from each of the fleet’s turbines in an Asset Performance System. By applying statistical models and looking for patterns in the data, turbine operators can optimize maintenance scheduling and identify common fault areas in order to take corrective and/or preventive actions before an issue occurs. This results in lower maintenance costs and less turbine downtime, so turbine companies can generate more revenue, increase profitability, and deliver a better customer experience.

The impact of the Industrial Internet will be far greater and widespread than other industrial revolutions before it. Companies are beginning to realize the financial benefits and early mover advantage from implementing Industrial IoT, and we have only just started to scratch the surface of possibilities.

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Notable IoT Announcements at CES 2016

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170,000 attendees from across the globe and 3,600 vendors gathered amongst 2.4 million net square feet of exhibit space debuting the latest products and services across the entire consumer tech ecosystem just concluded CES 2016.

It’s come a long way since spinning out of the Chicago Music show in 1967. Products that have debuted at CES include the videocassette recorder, the compact disc player, HDTV, Microsoft Xbox and smart appliances.

Each year there seems to be a new category in consumer electronics added to the mix. In 2015 the big buzzword was the Internet of Things and it’s weight carried over to 2016 with more than 1000 exhibitors unveiling IoT technologies. For a community like ours focused on the industrial side of the IoT, what does a consumer electronics show have to do with our world?

A lot actually.  

Here are the notable announcements from CES 2016:

 

WiFi HaLow

For industrial IoT heads this is probably the most notable announcement to come out of the show. The Wi-Fi Alliance® introduced a low power, long range standard dubbed Wi-Fi HaLow™ .

In the IoT space with billions of sensors to be placed everywhere, the industry is in need of a low power Wi-Fi solution. Wi-Fi HaLow will be a designation for products incorporating IEEE 802.11ah technology. Wi-Fi HaLow operates in frequency bands below one gigahertz, offering longer range, lower power connectivity to Wi-Fi certified products.

Edgar Figueroa, President and CEO of Wi-Fi Alliance said, “Wi-Fi HaLow is well suited to meet the unique needs of the Smart Home, Smart City, and industrial markets because of its ability to operate using very low power, penetrate through walls, and operate at significantly longer ranges than Wi-Fi today. Wi-Fi HaLow expands the unmatched versatility of Wi-Fi to enable applications from small, battery-operated wearable devices to large-scale industrial facility deployments – and everything in between.”

Many devices that support Wi-Fi HaLow are expected to operate in 2.4 and 5 GHz as well as 900 MHz, allowing devices to connect with Wi-Fi’s ecosystem of more than 6.8 billion installed devices. Like all Wi-Fi devices, HaLow devices will support IP-based connectivity to natively connect to the cloud, which will become increasingly important in reaching the full potential of the Internet of Things. Dense device deployments will also benefit from Wi-Fi HaLow’s ability to connect thousands of devices to a single access point.

The bad news? The Wi-Fi Alliance isn't planning on rolling out HaLow certifications until sometime in 2018, and even if it gets here, it might not be the de-facto standard. There are others vying for the crown.

 

AT&T

AT&T held a developer summit at the Palms Resort which was all about emerging technologies, products and services. A year ago, AT&T launched the M2X Data Service, a cloud-based data storage service for enterprise IoT developers. At CES they announced the commercial launch of Flow Designer, a cloud-based tool developed at the AT&T Foundry that lets IoT developers quickly build new applications. They also said that they are on track to have 50% of their software built on open source. They are working with OpenDaylight, OPNFV, ON.Lab, the Linux Foundation, OpenStack and others. Rachel King of ZDNet has an interview with AT&T President and & CEO Ralph de la Vega here.

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Ericsson

Ericsson and Verizon announced joint activities to further the development and deployment of cellular low-power wide-area (LPWA) networking for a diverse range of IoT applications. Ericsson introduced three IoT solutions for smart homes and cities:

  • Smart Metering as a Service puts consumers in control and enables utility companies to offer "smart" services to consumers in the future.

  • User & IoT Data Analytics enables controlled access and exposure of data from cellular and non-cellular devices and creates value through cross-industry offerings.

  • Networks Software 17A Diversifies Cellular for Massive IoT, supporting millions of IoT devices in one cell site, 90 percent reduced module cost, 10+ years battery life and 7-time cell coverage improvement.

 

IBM Watson

Last year, IBM announced a USD 3 Billion investment in Internet of Things, and in October, they announced plans to acquire The Weather Company, accelerating IBM's efforts in the IoT market that is expected to reach USD 1.7 trillion by 2020.

They furthered their commitment with five related IoT announcements at CES: Softbank, Whirpool, Under Armour, Pathway Genomics and Ford. What IBM does with Watson in the consumer space will carry over to the industrial space and vice versa. With the tremendous volumes of data from IoT, Watson’s advanced power of cognitive computing will be one way to exploit this new resource. Fortune’s Stacey Higginbotham has more here.

 

Intel

Lady GaGa aside, Intel made one announcement at CES which I think got through a lot clearer than Qualcomm’s 14 announcements! Rather than focus on technical aspects, Intel announced innovative technologies and collaborations aimed at delivering amazing experiences throughout daily life - which we often forget to do as we get enamored by the 1’s and 0’s. From unmanned aerial vehicles and wearables to new PCs and tablets, Intel made sure their chip was in it. On the industrial front was the DAQRI Smart Helmet, an augmented reality helmet for the industrial worker, powered by an Intel® Core™ M processor.

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Qualcomm

Qualcomm made a mind-boggling 14 announcements in the CES time frame. Probably the most interesting was the Qualcomm® Snapdragon™ X5 LTE modem (9x07). Qualcomm said the chip has multimode capability and supports LTE Category 4 download speeds up to 150 Mbps. It’s designed to be used in a range of mobile broadband applications and in IoT use cases that demand higher data rates.

 

Samsung

The President and CEO of Samsung Electronics, BK Yoon, delivered the opening keynote speech CES, calling for greater openness and collaboration across industries to unlock the infinite possibilities of the Internet of Things. Mr. Yoon announced a timetable for making Samsung technology IoT-enabled. By 2017, all Samsung televisions will be IoT devices, and in five years all Samsung hardware will be IoT-ready. He also emphasized the importance of developers in building IoT and announced that Samsung will invest more than USD 100 million in its developer community in 2015.

 

ZigBee Alliance

The ZigBee Alliance, a non-profit association of companies creating open, global standards that define the Internet of Things for use in consumer, commercial and industrial applications, announced that it is working with the Thread Group on an end-to-end solution for IP-based IoT networks. The solution will become part of the ZigBee Alliance’s comprehensive set of product development specifications, technologies, and branding and certification programs.

 

I’m sure there were many more industrial Internet of Things announcements. Let me know what I missed in the comments section below.




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