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Brontobytes, Yottabytes, Geopbytes, and Beyond

Guest blog post by Bill Vorhies

Now that everyone is thinking about IoT and the phenomenal amount of data that will stream past us and presumably need to be stored we need to break out a vocabulary well beyond our comfort zone of mere terabytes (about the size of a good hard drive on your desk).

In this article Beyond Just “Big” Data author Paul McFedries argues for nomenclature even beyond Geopbytes (and I'd never heard of that one).  There is a presumption though that all that IoT data actually needs to be stored which is misleading.  We may want to store some big chunks of it but increasingly our tools are allowing for 'in stream analytics' and for filtering the stream to identify only the packets we're interested in.  I don't know that we'll ever need to store Geopbytes but you'll enjoy his argument.  Use the link Beyond Just “Big” Data.

Here's the beginning of his thoughts:

Beyond Just “Big” Data

We need new words to describe the coming wave of machine-generated information

When Gartner released its annual Hype Cycle for Emerging Technologies for 2014, it was interesting to note that big data was now located on the downslope from the “Peak of Inflated Expectations,” while the Internet of Things (often shortened to IoT) was right at the peak, and data science was on the upslope. This felt intuitively right. First, although big data—those massive amounts of information that require special techniques to store, search, and analyze—remains a thriving and much-discussed area, it’s no longer the new kid on the data block. Second, everyone expects that the data sets generated by the Internet of Things will be even more impressive than today’s big-data collections. And third, collecting data is one significant challenge, but analyzing and extracting knowledge from it is quite another, and the purview of data science.

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Internet of Things? Maybe. Maybe Not.

Everything is connected, through the cloud all machine-generated data are collected and widely shared over the Internet. That’s how we imagine IoT – the Internet of Things.

 

Correction: That’s how THEY imagine IoT. What WE envision here is not just about the Internet of Things but also the Intelligence of Things. The idea is: When a device is equipped with connectivity and sensors, why not take another bold move to make the device intelligent? With an agile and affordable computing unit, every device has the power to analyze collected data and take fact-backed actions, thus making intelligence “in-place” a part of the Internet of Things, anywhere and at anytime. Intelligence, according to Jeff Hawkins*, is defined by predictions.

 

Computers, home appliances, vehicles – even the apparel and kitchenware – can be turned into a thinking unit.  They can help you act or react to the environment or your neighbours based on your behavioral routines and preferences. Your running shoes could control the friction of their soles according to your weight, the weather, and the kind of trail you choose. Your home theater system fine-tunes sound effects according to the movie genre and what time of day you are watching. There are plenty of exciting applications that come with the advent of intelligent things.

 

The question is, how does it work?

 

The data collected from sensors uploads to the cloud and is stored in (machine) learning systems, while streaming data input triggers an analytic engine to predict the best outcome and to react accordingly. Big data accumulates the background knowledge while small data evokes intelligence in-place.

 

In-Place Computing, fully utilizing the unbounded memory space of our existing 64-bit architecture, opens up the window for this sci-fi-like scenario. In-place computing utilizes virtual memory space, and thus avoids hardware lock-in and offers cross-platform computing power. As Qualcomm announced the introduction of 64-bit CPUs for handheld devices, now all mobile devices are entitled to serve complicated computing jobs at your fingertips. In-place Computing, can thus be the catalyst for a new era of “Intelligence of Things.”

 

*Check out this awesome video where Jeff Hawkins explains how brain science will change computing

Originally posted on Data Science Central

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How Secure are Home IOT Devices, Actually?

The Internet of Things (IoT) is a phenomenon that is currently experiencing huge year on year growth. One of the fastest growing areas within the industry is in the market of home IoT devices. These are devices designed to make life easier, such as connected garage door openers, smart switches, smoke alarms, and even IP surveillance cameras. There are almost 5 billion connected devices being used today, and according to Gartner Research, that number is expected to grow by 500% in the next 5 years.All of this shows a promising industry, but unfortunately the risks are never covered as much as the growth figures. IoT devices are often designed without a necessary focus on security or user privacy, and this is something that the industry needs to address.

Security Risks for IoT in the Consumer Space

Although IoT can be found in industries as diverse as medical and even manufacturing, it is the home markets that garner the headlines and consumer mindshare. People have come to expect that their security cannot always be maintained online. But the difference with IoT is that we’re not simply talking about passwords, emails, and social media accounts. Instead, we’re talking about access to the garage door, the front door, or even knowing whether or not somebody is home.

There are plenty of examples where common IoT devices have been found to be unsecure, or at least at risk of being compromised with relatively little effort.

The Fortify Security Software Unit at HP released case studies last year where they compared ten of the most popular devices used in home IoT. They found that seven out of ten devices had significant security issues. An average revealed 25 security risks in each individual product. The most prevalent problem was that IoT data was unencrypted as it was transferred through wireless networks. Worryingly, six of the devices didn’t even download firmware from encrypted sources. This leaves a possible risk where malicious firmware could be directed to home devices, providing external access for malicious parties.

HP isn’t the only company to have taken an interest in IoT security. Veracode recently published a report that was based on a similar survey of consumer devices. While the HP survey focused on devices like thermostats and lawn sprinklers, the Veracode study included critical devices, such as the Chamberlain MyQ Garage door opener, and the Wink Relay wall control unit. Veracode’s study looked more at risk than actual vulnerabilities, but the results were still significant.

The Wink Relay, if compromised, could allow external audio surveillance inside a user’s home. Information could be used for blackmail, to aid identity theft, or even for industrial espionage in relation to the resident’s employer. The Chamberlain garage door opener, if compromised, could mean that a third party could tell whether a garage door was open or not, allowing opportunities for easy, unauthorized entry.

Even if these devices connect to a relatively secure cloud platform, there’s always a risk that a home network could be compromised, and the fact is, few consumers are even aware of the dangers.

As we move forward, it is clear that security needs to be a top priority within the Internet of Things marketplace. Which means that stakeholders need to:

  • Understand the security risks involved with connecting home control devices to the cloud
  • Provide necessary security on their platforms
  • Educate consumers about security risks, and how they can protect themselves
  • Focus on building a talent pool of network security professionals to complement their core IoT development teams

Internet of Things represents an exciting time in the evolution of consumer, corporate, service based, and industrial technologies. It is important that key developers and manufacturers don’t lose sight of security during times of rapid innovation. With the right talent, and the right approach, the industry can build highly secure infrastructure and devices. This will ensure trust and desirability remains high, with the potential to drive adoption and overall market growth.

 

How does your team ensure practical security with its connected products?

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Internet of Things: Job Killer or Job Creator?

Is the IOT a Terminator or a Transformer? Where to look to get the most value out of the Internet of Things revolution.

The rebooted Terminator movie came out earlier this summer. Its blasted, futuristic landscape of robot killers and gun-toting, warrior humans probably started with enhanced computer technology similar to what we are experiencing today with The Internet of Things. I’ll be in the theater with my popcorn wondering: Will all this connectivity ultimately enhance our human experience or will we end up like the people on-screen, fighting to keep our place in this new world?

Of course, the Terminator movie is science fiction. But let’s look at the connected devices trends that will either displace or generate new opportunities for those of us in the trenches:

 

Healthcare

In a recent Goldman Sachs report (June 29, 2015), analysts predict that the healthcare arena is slated to experience extremely high levels of change based on the IOT:

"The service side of health care (hospitals, managed care) stands the most to gain from the adoption of digital health and IoT. Better patient management, streamlining the care continuum, reducing costly (and in some cases unnecessary) admissions all have the potential to improve the future economics for health care services," said the Goldman Sachs report. "The first wave of health care IoT technologies that prove successful will be those that drive specific action to improve patient care and correspondingly reduce waste and cost.”

I believe that this scenario provides more creative opportunities for connected Internet of Things developers in the healthcare space. Where can we take wearables in the digital age? We can reduce waste on the primary care side of things while creating opportunity for patients to gain unprecedented control of their health. And our IT geniuses can come up with new apps to connect it all.

 

Manufacturing

In a June 30, 2015 article in the Wall Street Journal, Ernst and Young’s acquisition of  “the systems consulting arm of manufacturing intelligence firm Entegreat Inc.” is just the latest in the mergers and acquisition free-for-all in the IOT (more accurately, the Industrial Internet of Things) space. The opportunities for eliminating waste in the industry are almost as plentiful as the thousands of connections that result when every node in a supply chain—from suppliers to customers and back—is integrated.  The production floor in an IOT-enabled factory will look quite different—yes, and probably will have fewer humans involved. However, the opportunity for job creation is endless—think about developers working to integrate old-school systems of record like MRP and ERP into new, cloud-based, mobile solutions. What about app developers—shop floor personnel might one day work from home—how can you translate inventory data streams, customer orders and work-in-process data to a tablet or mobile phone? These are the questions that new and emerging IT talents can sink their teeth into.

 

Everywhere Else

If you want to unlock the job creation potential of the Internet of Things, look no further than the latest McKinsey report. They’ve identified nine areas of growth to reach the $4 trillion to $11 trillion of value inherent in the IOT’s potential. I’m taking liberties here in placing the remaining seven (we’ve already talked about what McKinsey characterizes as the “human” (healthcare) and “factories” (manufacturing) categories) together in an overarching category of “everything else” with a few characteristics in common: Business Model and Modality Disruptions.

McKinsey talks about Business Model opportunities where the Internet of Things will create brand new ways of doing business. Its focus on “everything as a service” disrupting the traditional back-and-forth of business transactions is spot-on. However, the most opportunities for job creation (aside from the fact that these new business models might very well need a brand new breed of MBA) are what I call “modality disruptions.” These are the “how I will live my life” changes that provide the most value. For developers and IT professionals, this means that their discipline’s value will experience a sea change in the eyes of their leaders. With all of the changes in Cities; Homes; Vehicles, and among all of the categories of emerging value in IOT, the modality disruption of how we do business will ensure IT is not only an enabler; never again a not-so-benign cost center; but a true game changer whose capabilities will guarantee a company’s future—or its demise.

Give us your take on the Job Killer or Job Creator debate -  where do you stand, and what do you think will be the outcome?

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When you read or hear about the Internet of Things (IoT), do you imagine that we’re not quite in an age where such a concept is able to be fully realized? Have you ever pointed towards the fragmentation in the market regarding devices and services, or even the complexity of IoT, and questioned how concepts like the connected home could be adopted on a widespread scale?

If you’re still questioning IoT at this point, then it’s possible that you’re simply not looking closely enough. Many of the products and services that you’re using are already a part of IoT.

Microsoft’s Office suite is a connected service on IoT, Apple’s ecosystem is IoT to the core, and even your late model vehicle is likely connected to IoT in some way. In the consumer world, IoT is simply the reality of all your devices being connected; from your game console, to your cellular phone, the computer in your office and on your coffee table, and even your automated home lighting, air conditioning, and garage door.

IoT as a concept was first described over 20 years ago by researchers at MIT. They spoke of a future where devices and sensors would collect and share data. There’s a reason why it is a buzzword today. Data capabilities, the decreasing cost of hardware, and the widespread adoption of the internet have made IoT possible for consumers, businesses, and large organizations across the world.

As a consumer, you’re probably already using IoT today. Your smartphone can connect to your home PC and control it remotely. You can set schedules for you Cable PVR and arrive home to your favorite programs already recorded and ready to play. You can even strap a smart device to your wrist while you jog, while also collecting data on your heart rate, the calories you’ve burnt, and even map a GLONASS or GPS tracked route of where you went.

You can then upload that data to the cloud and retrieve it later. You can share it with other people. You could even send the information to your personal trainer who can observe and advise around your exercise regime. This is what the Internet of Things is all about. For consumers, it’s all about the power of information.

IoT makes life easier. Progression has been gradual, and in many ways low key. This may be why many haven’t noticed it happening. When you used to collect your mail, there was one place where you could do it; your mailbox. Today, your mailbox is anywhere that you go, as long as you have a connected device. We used to bank inside buildings. ATM’s came later, and they increased the convenience. Today you can bank from a smartwatch. You can make payments with an NFC chip without swiping plastic. You can transfer your money from account to account from a Smartphone or PC.

The Internet of Things has provided countless advantages to society. From smarter automated manufacturing, to biometric implants in critical care patients, IoT does more than the average person knows. Perhaps the fact that we already use IoT without even knowing it, is testament to how important, influential, and firmly embedded IoT is in our lives today.

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Will Javascript be the Language of IoT?

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JavaScript has proven itself worthy for web applications, both client and server side, but does it have potential to be the de-facto language of IoT?  

This is a topic I posed to Patrick Catanzariti, founder of DevDiner.com, a site for developers looking to get involved in emerging tech. Patrick is a regular contributor and curator of developer news and opinion pieces on new technology such as the Internet of Things, virtual/augmented reality and wearables. He is a SitePoint contributing editor, an instructor at SitePoint Premium and O'Reilly, a Meta Pioneer and freelance web developer who loves every opportunity to tinker with something new in a tech demo.

Why does IoT require a de facto language any more than any other system? Wouldn't that stifle future language evolution?

Honestly, I think it's a bit too much to ask for every single IoT device out there to run on JavaScript or any one de facto language. That's unbelievably tough to manage. Getting the entire world of developers to agree on anything is pretty difficult. Whatever solution the world of competing tech giants and startups come to (which is likely to be a rather fragmented one if current trends are anything to go by), the most important thing is that these devices need to be able to communicate effectively with each other and with as little barriers as possible. They need to work together. It's the "Internet of Things". The entire benefit of connecting anything to the Internet is allowing it to speak to other devices at a massive scale. I think we'd be able to achieve this goal even with a variety of languages powering the IoT. So from that standpoint, I think it's totally okay for various devices to run on whichever programming language suits them best.

On the other hand, we need to honestly look at the future of this industry from a developer adoption and consistency perspective. The world of connected devices is going to skyrocket. We aren't talking about a computer in every home, we're talking dozens of interconnected devices in every home. If each one of those devices is from a different company who each decided on a different programming language to use, things are going to get very tough to maintain. Are we going to expect developers to understand all programming languages like C, C++, JavaScript, Java, Go, Python, Swift and more to be able to develop solutions for the IoT? Whilst I'm not saying that's impossible to do and I'm sure there'll be programmers up to the task of that - I worry that will impact the quality of our solutions. Every language comes with its quirks and best practices, it'll be tough to ensure every developer knows how to create best practice software for every language. Managing the IoT ecosystem might become a costly and difficult endeavour if it is that fragmented.

I've no issue with language evolution, however if every company decides to start its own language to better meet the needs of the IoT, we're going to be in a world of trouble too. The industry needs to work together on the difficulties of the IoT, not separately. The efforts of the Open Interconnect Consortium, AllSeen Alliance and IoT Trust Framework are all positive signs towards a better approach.

C, C++ and Java always seem to be foundational languages that are used by all platforms, why do you think JavaScript will be the programming language of IoT?

My position is actually a bit more open than having JavaScript as the sole programming language of the IoT. I don't think that's feasible. JavaScript isn't great as a lower level language for memory management and the complexities of managing a device to that extent. That's okay. We are likely to have a programming language more suited to that purpose, like C or C++, as the de facto standard operational language. That would make perfect sense and has worked for plenty of devices so far. The issues I see are in connecting these devices together nicely and easily.

My ideal world would involve having devices running on C or C++ with the ability to also run JavaScript on top for the areas in which JavaScript is strongest. The ability to send out messages in JSON to other devices and web applications. That ability alone is golden when it comes to parsing messages easily and quickly. The Internet can speak JavaScript already, so for all those times when you need to speak to it, why not speak JavaScript? If you've got overall functionality which you can share between a Node server, front end web application and a dozen connected IoT devices, why not use that ability?

JavaScript works well with the event driven side of things too. When it comes to responding to and emitting events to a range of devices and client web applications at once, JavaScript does this pretty well these days.

JavaScript is also simpler to use, so for a lot of basic functionality like triggering a response on a hardware pin or retrieving data from a sensor, why overcomplicate it? If it's possible to write code that is clear and easy for many developers to understand and use without needing to worry about the lower level side of things - why not? We have a tonne of JavaScript developers out there already building for the web and having them on board to work with joining these devices to their ecosystem of web applications just makes sense.

Basically, I think we're looking at a world where devices run programming languages like C at their core but also can speak JavaScript for the benefits it brings. Very similar to what it looks like IoT.js and JerryScript will bring. I really like the Pebble Smartwatch's approach to this. Their watches run C but their apps use JavaScript for the web connectivity.

When it comes to solutions like IoT.js and JerryScript, they're written initially in C++. However they're providing an entire interface to work with the IoT device via JavaScript. One thing I really like about the IoT.js and JerryScript idea is that I've read that it works with npm - the Node Package Manager. This is a great way of providing access to a range of modules and solutions that already exist for the JavaScript and Node ecosystems. If IoT.js and JerryScript manage memory effectively and can provide a strong foundation for all the low level side of things, then it could be a brilliant way to help make developing for the IoT easier and more consistent with developing for the web with all the benefits I mentioned earlier. It would be especially good if the same functionality was ported to other programming languages too, that would be a fantastic way of getting each IoT device to some level of compatibility and consistency.

I'm hoping to try IoT.js and JerryScript out on a Raspberry Pi 2 soon, I'm intrigued to see how well it runs everything.

What do developers need to consider when building apps for IoT?

Security - If you are building an IoT device which is going to ship out to thousands of people, think security first. Make sure you have a way of updating all of those devices remotely (yet securely) with a security fix if something goes wrong. There will be bugs in your code. Security vulnerabilities will be found in even the most core technologies you are using. You need to be able to issue patches for them!

Battery life - If everyone needs to change your brand of connected light bulbs every two months because they run out of juice - that affects the convenience of the IoT. IoT devices need to last a long time. They need to be out of the way. Battery life is crucial. Avoid coding things in a way which drains battery power unnecessarily.

Compatibility - Work towards matching a standard like the Open Interconnect Consortium or AllSeen Alliance. Have your communication to other devices be simple and open so that your users can benefit from the device working with other IoT devices in new and surprising ways. Don't close it off to your own ecosystem!

What tools do you recommend for developing apps in IoT?

I'm a fan of the simple things. I still use Sublime Text for my coding most of the time as it's simple and out of the way, yet supports code highlighting for a range of languages and situations. It works well!

Having a portable 4G Wi-Fi dongle is also very very valuable for working on the go with IoT devices. It serves as a portable home network and saves a lot of time as you can bring it around as a development Wi-Fi network you turn on whenever you need it.

Heroku is great as a quick free platform to host your own personal IoT prototypes on too while you're testing them out. I often set up Node servers in Heroku to manage my communication between devices and it is the smoothest process I've found out of all of the hosting platforms so far.

For working locally - I've found a service called ngrok is perfect. It creates a tunnel to the web from your localhost, so you can host a server locally but access it online via a publicly accessible URL while testing. I've got a guide on this and other options like it on SitePoint.

Are you seeing an uptick in demand for IoT developers?

I've seen a demand slowly rising for IoT developers but not much of a developer base that is taking the time to get involved. I think partially it is because developers don't know where to start or don't realise how much of their existing knowledge already applies to the IoT space. It's actually one of the reasons I write at SitePoint as a contributing editor - my goal is to try and get more developers thinking about this space. The more developers out there who are getting involved, the higher the chances we hit those breakthrough ideas that can change the world. I really hope that having devices enabled with JavaScript helps spur on a whole community of developers who've spent their lives focused on the value of interconnected devices and shared information get involved in the IoT.

My latest big website endeavour called Dev Diner (http://www.devdiner.com) aims to try and make it easier for developers to get involved with all of this emerging tech too by providing guides on where to look for information, interviews and opinion pieces to get people thinking. The more developers we get into this space, the stronger we will all be as a community! If you are reading this and you're a developer who has an Arduino buried in their drawer or a Raspberry Pi 2 still in their online shopping cart - just do it. Give it a go. Think outside the box and build something. Use JavaScript if that is your strength. If you're stronger at working with C or C++, work to your strength but know that JavaScript might be a good option to help with the communication side of things too.

For more on Patrick’s thoughts on Javascript, read his blog post “Why JavaScript and the Internet of Things?” and catch his O’Reilly seminar here.

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What's Hot in Hiring: Data Security Consulting!

Big Growth in Data Security Provides Opportunities for Consultants

By 2016, the worldwide data security market is expected to approach almost $90 billion in total value. This means that security is big business, and it should be. Data security has become increasingly critical as businesses utilize increasingly complex technology. Likewise, businesses that are directly involved in technology, such as Internet of Things and connected devices startups, cloud service providers, and even internet service providers, all have a vested interest in maintaining the security of their data.

Three Core Influencers on the Security Market

There are three core areas of influence that are driving the key players in data security consulting. Market influencers, according to Gartner Research, include BYOD (Bring Your Own Device), big data, and the security threats themselves.

BYOD is changing the way that SMBs and enterprise clients think about security. In the past, security solutions could be rolled out and controlled across a limited number of devices that were usually owned and maintained by employers. Today, it is more common for executives and staff at all levels to bring their own devices, which can then connect to company applications and networks. This creates the challenge of implementing robust security policies and technologies that can cover a range of devices and access methods.

Increased connectivity has led to increasing levels of "big data" in business. Considering all of the channels where data is collected, whether it be through software, customer interactions, or even data that comes from IoT connected devices, it is becoming critical that big data is not only collected, identified, and categorized, but that it is kept secure. Security in the future will be essential for protecting IP, trade sensitive information, and maintaining privacy.

Finally, the increasing number of security threats that are present, are reshaping the market, and will continue to do so in the future. In addition to the attacks and exploits that have been common in the past, data security consulting professionals now have new technologies where compromises must be patched and anticipated. IoT devices, SaaS solutions, and an increasingly widespread cloud adoption will be major factors that shape the needs of future data security.

 

Data Security Consulting: What is Hot?

Recent graduates, professionals looking for new opportunities, and even CIOs within existing organizations can anticipate the opportunities and needs, by identifying current roles and niches in the data security consulting market.

A data security role may be completely specialized, or in some cases, generalized and more leadership based, depending on the size of an organization.

Information security can be broken down into two main areas. These areas are hardware, and software. A data security consultant may be expected to have a wider understanding of their industry, but in reality they will only specialize in some key areas. This means that employers need to be specific about who they’re looking for and the technologies that they use. It also means that jobseekers need to be upfront about their expertise, or they may risk finding themselves in a position that is beyond their current skillset, which could lead to career impacting underperformance.

As a consultant, the role is to advise, develop, and implement change. This change is usually to address a problem that already exists. In the case of data security, this could mean that a security threat has already been identified, or it could be to mitigate possible threats with new technologies.

  • Consultants need superior application and network penetration skills. This means that they should be able to break down, and analyze the way that software works within any environment. This includes input and output channels. Networks need to be understood in the same way. The purpose of this knowledge, is to identify where risks exist, or where existing security breaches are occurring.

  • Software algorithms are known to provide false positives, so a consultant needs to be able to identify these, and should have skill in determining viable threats. This will help the consultant to allocate resources where they are most necessary, which can benefit their employer, financially.

  • Consultants should build an understanding of the technologies used by their employer. Whenever working on a contract, a consultant will deal with systems that they are unfamiliar with. Understanding the underlying technologies will be critical to implementing successful security solutions. This may require knowledge of cloud computing and infrastructure, IoT protocols and industry practices, or even specifics of networking or programming languages.

  • Successful consultants will be experts in risk management. This should not just include software and hardware, but also their employer’s strategy when it comes to risk management. Some companies are willing to accept higher levels of risk, while some have more stringent expectations. Understanding the culture of any particular company will be critical.

 

As Data Becomes More Important, Security Consulting Becomes a Necessity

It does not matter whether a business processes EPS payments, collects consumer information for a large retail operation, or even deals exclusively in cloud technology and the Internet of Things. The reality is that, as long as they are collecting and storing data, they will need dedicated security professionals.

Protecting that data for commercial and privacy reasons, will best be achieved with the right candidates, who have the skills and experience to deal with security threats in the modern business landscape.

I found a great resource for planning for and making decisions about information security at the Gartner Research Security and Risk Management page.

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The topic of IoT and farming keeps coming up.

Last month Steve Lohr of the New York Times wrote a fantastic piece on The Internet of Things and the Future of Farming. His colleague Quentin Hardy wrote a similar piece, albeit with a big data slant, in November 2014. If you have not yet read either article, I suggest you take the time to do so and also watch the video of IoT at work at a modern farm. It’s one of the better IoT case studies I’ve come across and shows real and practical applications and results.

Both stories highlight Tom Farms, a multi-generation, family owned farm in North Indiana. The Toms won’t be setting up a stand at your local farmers market to hawk their goods. With over 19,000 acres they are feeding a nice portion of America and conduct farming on an industrial scale producing shipments of more than 30 million pounds of seed corn, 100 million pounds of corn, and 13 million pounds of soybeans each year.

As the video points out, technology, data and connectivity have gotten them to this scale. After the farm crisis of the 1980s, they double-downed and bought more land from other struggling farmers. Along the way they were proactive in researching and developing new production technologies - everything from sensors on the combine, GPS data, self-driving tractors, and apps for irrigation on an iPhone.

Farmers and Tablet PC

Photo Credit: Gary McKenzie on Flickr

All this technology is taking farming to a new level, in what is know as Precision Agriculture. The holy grail of precision agriculture is to optimize returns on inputs while preserving resources. The most common use of of modern farming is used for guiding tractors with GPS. But what other technologies are out there?

For that, the Wall Street Journal explored yesterday startups that put data in farmers' hands. Startups like Farmobile LLC, Granular Inc. and Grower Information Services Cooperative are challenging data-analysis tools from big agricultural companies such as Monsanto Co., DuPont Co., Deere & Co. and Cargill Inc.

The new crop from all of these technologies is data.

This changes the economics for farmers making them not just traders in crops, but in data, potentially giving them an edge against larger competitors that benefit from economies of scale (to compete against giants like Tom Farms).  

With the amount of venture investment in so-called agtech start-ups reaching $2.06 billion in the first half of this year there will be plenty of bytes in every bushel.

For a deep dive into Precision Agriculture, the history and the technologies behind it, I suggest registering for and reading the Foreign Affairs article, “The Precision Agriculture Revolution, Making the Modern Farmer.”

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Mapping the Internet of Things

You would think that in this day and age of infographics that finding a map laying out the ecosystem of the Internet of Things would exist. Surprisingly, a Google search doesn’t appear to return much. Neither does a Twitter a search.

Recently though I found two worth sharing. One from Goldman Sachs and the other from Chris McCann which I found very interesting - A Map of The Internet of Things Market.

Goldman Sachs’ map is pretty generic but it takes IoT related items all the way from the consumer to the Industrial Internet. In a September 2014 report, “The Internet of Things: Making sense of the next mega-trend”, Goldman states that IoT is emerging as the third wave in the development of the Internet. Much of what we hear about today are on the consumer end of the spectrum - early simple products like fitness trackers and thermostats. On the other end of the spectrum, and what I think IoT Central is all about, is the Industrial Internet. The opportunity in the global industrial sector will dwarf consumer spend. Goldman states that industrial is poised to undergo a fundamental structural change akin to the industrial revolution as we usher in the IoT. All equipment will be digitized and more connected and will establish networks between machines, humans, and the Internet, leading to the creation of new ecosystems that enable higher productivity, better energy efficiency, and higher profitability. Goldman predicts that  IoT opportunity for Industrials could amount to $2 trillion by 2020.

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Chris McCann, who works at Greylock Partners, has an awesome map of the Internet of Things Market (below). This is what venture capitalists do of course - analyze markets and find opportunities for value by understanding the competitive landscape. This map is great because I think it can help IoT practitioners gain a better understanding of the Internet of Things market and how all of the different players fit together.

The map is not designed to be comprehensive, but given the dearth in available guidance, this is a great starting point. The map is heavily geared towards the startup space (remember the author is a VC) and I think he leaves out a few machine-to-machine vendors, software platforms and operating systems.

Other maps I found that are interesting are:

Thingful, a search engine for the Internet of Things. It provides a geographical index of connected objects around the world, including energy, radiation, weather, and air quality devices as well as seismographs. Near me in earthquake prone Northern California I of course found a seismograph, as well as a weather station, and an air quality monitoring station.  

Shodan, another search engine of sorts for IoT.

And then there is this story of Rapid7’s HD Moore who pings things just for fun.

If you have any maps that you think are valuable, I would love for you to share them in the comments section.



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The Internet of Things and the Right to Record

Today there are over 5 billion connected devices in the world that make up the Internet of Things (IoT). Research firms like IDC and Gartner predict that within five years’ time, this number will skyrocket to 25 billion. Although we often think of the ways these IoT devices can make our lives easier, make our homes smarter, improve manufacturing, and even revolutionize healthcare, there are some uses for IoT that aren’t as straightforward.

 

One of these, is how IoT has changed our ability to record the world around us, and immediately share what we capture. Combined with social media, this ‘right to record’ has brought into question when it is appropriate or not appropriate to record. More importantly, is it legal?

The Legalities of Recording in Public

Smartphones, tablets, and even connected eyewear are all part of IoT, and they’re all capable of recording pictures and video. The most obvious example to look at is the phenomenon of members of the public recording law enforcement officers, performing their duties.

  • There are a number of states that have an ‘all parties consent’ law, requiring that subjects be made aware of video, image, or audio capture that is taking place.
  • There is a clause, however. There should also be a reasonable expectation of privacy on behalf of the subjects. This means, with interpretation, that filming in public places, without consent, would be acceptable and legal.
  • Illinois and Massachusetts have ‘all parties consent’ laws, however they don’t allow for the provision regarding the expectation of privacy. In 2010, Tiawanda Moore was arrested for attempting to record law enforcement personnel with a cell phone. She was later acquitted of all charges (http://articles.chicagotribune.com/2011-08-25/news/ct-met-eavesdropping-trial-0825-20110825_1_eavesdropping-law-police-officers-law-enforcement).
  • It is not legal to record on private property, to make commercial gain from recorded material of another person’s likeness, or to use recordings to commit libel.

The Right to Record is a Two Way Street

Tech Republic, a leading trade publication for IT professionals, recently ran an opinion piece on how IoT and smart devices can cause controversy when it comes to the right to record. (http://www.techrepublic.com/article/the-right-to-record-is-not-a-question-of-technology-but-rather-power-and-policy/).

The article not only discussed the recording of law enforcement by private citizens, but also how it can be beneficial for law enforcement officers to constantly record their daily duties. Doing so would add a layer of transparency, and would serve to protect the interests of officers and their relevant governments, as well as the general public. This recording would be in addition to the already present police vehicle dash cams, and the surveillance cameras in most urban centers.

The questions then, are not as much about recordings been made in the first place, but rather about how they are used. Two key questions are;

  • Should law enforcement agencies have the right to publish footage or images of suspects before they have been convicted of crimes?
  • Should individuals have the right to publish police activity when footage or an image doesn’t portray an event or incident within its full context?

The Internet of Things is hugely dependent on constant information, easy accessibility to information, and the almost instant distribution of that information. IoT has changed the way that people expect services to work. Almost one third of those surveyed by the American Red Cross in 2012 would expect law enforcement or emergency assistance if they posted a request for help on a public social media website. Would those who are embracing social media be happy to post controversial images or videos of law enforcement agents in the line of duty? What if they were the ones being featured on a law enforcement social media account?

As more connected devices are able to easily record and share the world around us, lines will become blurred when it comes to rights. The ‘right to record’ could be considered a civil liberty under the right to free speech, so does the government share that same right? As IoT devices become more commonplace, and the internet of everything becomes a part of daily life, these questions will be answered, laws will be tested, and new precedents will be set.

20 million more IoT devices will be installed, carried, or worn by people at all levels of society, by 2020. Users and creators of IoT technologies will need to keep a close eye on ‘the right to record’, and how it impacts the industry and public perception of these devices in the years to come.

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The Internet of Things might seem like a buzzword right now. Google Trends shows continual interest in the subject each year, and the marketplace is growing. With an estimated 26 billion devices projected to be connected by 2020, it is actually something that you need to take seriously. It is more than some hip term, and may actually be the way of the future. 

Seeing actual examples of Internet of Things devices in action can help shed light on what these devices actually do. 

Take for example, the use of the internet of things in medicine. In a hospital, devices are connected to pagers, computers and other devices so doctors and nurses can easily monitor the stats of a patient, regardless of where they are in the world. If there is an alert, such as a patient coding, healthcare professionals are alerted at once.

Major cities are even incorporating the internet of things into how they handle their parking. In city parking garages, the IoT helps drivers know how many free spaces are available on each level. These sensors help drivers to locate spaces easier and help the garage to determine when they are filled to capacity. This is incredibly useful when a major sporting event or concert is going on.

Cities are also using the Internet of Things to help them to better maintain roads. Sensors located on roadways monitor the normal flow of traffic at a given time. In areas where traffic is heavier than others, these devices transmit counts to a central system. The city then can plan maintenance for these areas and increase lanes, based on the statistics these devices transmit.

Another way the Internet of Things is being used today is in your car. Some car insurance companies now have devices you can plug into your vehicle. This device monitors the speed you are going, braking habits and even how loud your radio is. This information is sent over the internet to the provider. Based on the transmitted data, the provider determines if you are eligible for good driving discounts. Of course, this is a double-edged sword. Those who drive erratically may also face higher rates based on the transmissions from this device.

As you can see, the internet of things is becoming an important part of our world. Every year, new industries are rolling out new technologies that incorporate, or take advantage of IoT. Before long, our world will be universally connected and our devices will be far more powerful than they are today.

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IoT Security - Hacking Things with the Ridiculous

In the 1996 sci-fi blockbuster movie “Independence Day”, there is a comical seen near the end where actor Jeff Goldblum, playing computer expert David Levinson, writes a virus on his Macintosh PowerBook that disables an entire fleet of technologically advanced alien spaceships. The PowerBook 5300 used in the movie had 8 MB of RAM. How could this be?

Putting aside Apple paying for product placement, we’re not going to stop advanced alien life who are apparently Mac-compatible.

I cite the ridiculous Independence Day ending because I was recently reading through a number of IoT security stories and began thinking about the implications of connecting all these things to the network. How much computing power does one actually need to hack something of significance? Could a 1997 IBM Thinkpad running Windows 95 take down the power grid in the eastern United States? Far fetching, yes, but not ridiculous.

Car hacks seem to be in the news recently. Recall last month’s Jeep hack and hijack. Yesterday, stories came out about hackers using small black dongles connected to a Corvette’s diagnostic ports to control many parts of the car through, wait for it, text messages!

Beyond cars and numerous other consumer devices, IoT security has to reach hospitals, intelligent buildings, power grids, airlines, oil and gas exploration as well as every industry listed in the IRS tax code.

IBM’s X-Force Threat Intelligence Quarterly, 4Q 2014 notes that IoT will drag in its wake a host of unknown security threats. Even IBM, a powerful force in driving IoT forward, says that their model for IoT security is still a work in progress since IoT, as a whole, is still evolving. They do suggest however five security building blocks: secure operating systems, unique identifiers for each device, data privacy protection, strong application security, strong authentication and access control.

In the end, it will be up to manufacturers to build security from the ground up and continual work with the industry to make everything more secure. As we coalesce around an ever evolving threat landscape, it will be the responsibility of smaller manufacturers, giants like IBM and industry organizations like the Industrial Internet Consortium and Online Trust Alliance’s IoT Trust Framework to help prevent the ridiculous from happening.

 

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Do You Believe the Hype?

I’m guilty of hype.

As a communications consultant toiling away at public relations, media relations and corporate communications, I’ve had my fair share of businesses and products that I’ve helped get more attention than it probably deserved. Indeed, when it comes to over-hyping anything, it’s guys like me and my friends in the media who often take it too far.

Recently though, I came across an unlikely source of hype - the McKinsey Global Institute.

In a June 2015 report that I’m now reading, McKinsey states, “The Internet of Things—digitizing the physical world—has received enormous attention. In this research, the McKinsey Global Institute set to look beyond the hype to understand exactly how IoT technology can create real economic value. Our central finding is that the hype may actually understate the full potential of the Internet of Things…” (emphasis is mine).

If McKinsey is hyping something, should we believe it?

Their report, “The Internet of Things: Mapping the Value Beyond the Hype”, does point out that “capturing the maximum benefits will require an understanding of where real value can be created and successfully addressing a set of systems issues, including interoperability.”

I think this is where the race is today - finding the platforms for interoperability, compiling data sources, building security into the system and developing the apps that deliver true value. We have a long way to go, but investment and innovation is only growing.

If done right the hype just may be understated. McKinsey finds that IoT has a total potential economic impact of $3.9 trillion to $11.1 trillion a year by 2025. They state with consumer surplus, this would be equivalent to about 11 percent of the world economy!

Do you believe the hype?

 

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List of IoT Platforms

IoT platforms make the developer’s life easier by offering some independent functionality which can be used by the applications they write to achieve their objective. Saving them from the task of reinventing the wheel. Given here is a list of useful IoT platforms.

 

Kaa

Kaa is a flexible open source platform licenced under Apache 2.0 for building, managing, and integrating connected software in IoT. Kaa’s “data schema” definition language provides a universal level of abstraction to achieve cross-vendor product interoperability. Kaa supports multiple client platforms by offering endpoint SDKs in various programming languages. In addition, Kaa’s powerful back-end functionality greatly speeds up product development, allowing vendors to concentrate on maximizing their product’s unique value to the consumer.

 

Axeda

The Axeda Platform is a complete M2M and IoT data integration and application development platform with infrastructure delivered as a cloud-based service.

 

Arrayent

The Arrayent Connect Platform is an IoT platform that helps to connect products to smartphones and web applications. It comes with an an agent which helps the embedded devices to connect to cloud, A cloud based IoT operating system, A mobile framework and a business intelligence reporting system

 

Carriots

Carriots is a Platform as a Service (PaaS) designed for Internet of Things (IoT) and Machine to Machine (M2M) projects. It provides tools to Collect & store data from devices, SDK to build powerful applications, deploy and scale from tiny prototypes to thousands of devices

 

Xively

Xively offers an enterprise IoT platform which helps in connecting products and users, manage the information and an interface to for product deployment and health check

 

ThingSpeak

ThingSpeak is an open source Internet of Things application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network. ThingSpeak enables the creation of sensor logging applications, location tracking applications, and a social network of things with status updates

 

The Intel® IoT Platform

The Intel® IoT Platform is an end-to-end reference model and family of products from Intel, that works with third party solutions to provide a foundation for seamlessly and securely connecting devices, delivering trusted data to the cloud, and delivering value through analytics.

A votable & rankable list of these platform can be found at Vozag

 
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Originally posted on Data Science Central

Summary:  Thanks to the IOT (internet of things) an internet-like experience of recommendations and awareness of your preferences is coming to the brick and mortar store near you.

You’ve probably noticed the huge difference in the tone of the conversation between data scientists and the general public over the issue of privacy and personalization.  The professional community is largely quiet but for the public you’d think we were developing bionic eyeballs tracking their most minute and private habits.

In my house my wife is always complaining that I can’t remember how many sweeteners she takes in her tea; who her favorite actors are, or whether she liked that Indian restaurant we visited last year enough to want to go back.  But if a web site shows her a picture of something she browsed yesterday, or if the recommended books and movies on Amazon are a little too on target she’s the first one to raise the hue and cry that her privacy is being violated.  My failing to remember – bad.  Their being helpful by remembering or recommending – also bad???

This is beginning to look like a real Catch 22.  Behaviors we wish for at home are suddenly evil if a web site does an even better job than your spouse at remembering your likes and dislikes.

Personally I think site personalization is a real blessing.  I don’t really want to see ads for rock climbing walls or baby diapers.  I’m not in that market so not being exposed to a random untargeted bunch of ads (think your Sunday paper – what’s a Sunday paper you say?) is all for the good.

Well web sites are one thing but these days with the emerging IOT our brick and mortar stores are gearing up to behave more like a web site and less like a random walk up one aisle and down another.  Here’s a brief update on who’s doing what in retail IOT.  I’m sure there are many providers I’ve missed and can’t say if these folks are good or bad at what they do but my hat’s off to them for trying something new that might make my life better even if my wife would find it a little spooky.

In retail Heat Maps (which products get picked up more often than others) and Flow Charts (how customers navigated the store) are all the rage.  Sensors also allow retailers to offer coupons over your smart phone that are tailored to your shopping pattern.  And by moving desirable merchandise with long linger times to better locations, frequently to deeper in the store, they can achieve that same ‘stickiness’ we associate with web sites to make us stay a little longer.  Where exactly are the customers going in the store, where do they pause and ponder, and how can the retailer use this information to revise the store layout, the merchandise displays, pricing, or anything else to squeeze out another dollar. 

The specifics of sensors and strategies differ from one vendor to another and in this early stage of adoption it’s fair to say that we’re waiting for the market to tell us which are most successful.  Some use your cell phone to triangulate your position, some use cameras, radio beacons, or even more exotic sensor types.  This is a good thing since all this experimentation will tell us what’s worth the investment and what’s not.  Any number of major retailers are running experiments. To name just a few:

Nordstrom – Euclid Analytics

Macy’s – Shopkick

Timberland and Kenneth Cole -Swirl Networks

Goldman’s Dept. Stores - RetailNext

The Future of Privacy Forum, a Washington, D.C., think tank, estimates that about 1,000 retailers are testing some sort of sensor strategy.

Swarm Solutions says 6,000 retailers have installed its door sensors to compare foot traffic with transactions.

Others working with Wi-Fi triangulation include Ekahau, Wifislam, and Prism Skylabs.  Apple’s iBeacon technology probably belongs in this group as well.

Blinksight and Insiteo are working with radio beacons.

Bytelight, Aisle411, Everyfit, and PointInside are all working with other sensor types including embedded floor sensors and even LED lights.

These 15 innovators are probably only the tip of the iceberg.  This is one of those ‘stay tuned for results’ stories.  The results aren’t in but there are lots of horses in the race.  Meanwhile, I’m still looking for the sensors I can install at home that will make my wife think I am a better husband.

Bill Vorhies, President & Chief Data Scientist – Data-Magnum - © 2014, all rights reserved.

 

About the author:  Bill Vorhies is President & Chief Data Scientist of Data-Magnum and has practiced as a data scientist and commercial predictive modeler since 2001.  He can be reached at:

[email protected]

The original blog can be viewed at:

http://data-magnum.com/privacy-personalization-and-the-iot-retail/

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Understanding the nature of IoT data

Originally posted on Data Science Central

This post is in a series Twelve unique characteristics of IoT based Predictive analytics/machine learning.

Here, we discuss IoT devices and the nature of IoT data

Definitions and terminology

    Business insider makes some bold predictions for IoT devices

    The Internet of Things will be the largest device market in the world.  

    By 2019 it will be more than double the size of the smartphone, PC, tablet, connected car, and the wearable market     combined.

    The IoT will result in $1.7 trillion in value added to the global economy in 2019.

    Device shipments will reach 6.7 billion in 2019 for a five-year CAGR of 61%.

    The enterprise sector will lead the IoT, accounting for 46% of device shipments this year, but that share will decline     as the government and home sectors gain momentum.

    The main benefit of growth in the IoT will be increased efficiency and lower costs.

    The IoT promises increased efficiency within the home, city, and workplace by giving control to the user.

And others say internet things investment will run 140bn next five years

 

Also, the term IoT has many definitions – but it's important to remember that IoT is not the same as M2M (machine to machine). M2M is a telecoms term which implies that there is a radio (cellular) at one or both ends of the communication. On the other hand, IOT means simply connecting to the Internet. When we are speaking of IoT(billions of devices) – we are really referring to Smart objects. So, what makes an Object Smart?

What makes an object smart?

Back in 2010, the then Chinese Premier Wen Jiabo once said “Internet + Internet of things = Wisdom of the earth”. Indeed the Internet of Things revolution promises to transform many domains .. As the term Internet of Things implies (IOT) – IOT is about Smart objects

 

For an object (say a chair) to be ‘smart’ it must have three things

  • An Identity (to be uniquely identifiable – via iPv6)
  • A communication mechanism(i.e. a radio) and
  • A set of sensors / actuators

 

For example – the chair may have a pressure sensor indicating that it is occupied

Now, if it is able to know who is sitting – it could co-relate more data by connecting to the person’s profile

If it is in a cafe, whole new data sets can be co-related (about the venue, about who else is there etc)

Thus, IOT is all about Data ..

How will Smart objects communicate?

How will billions of devices communicate? Primarily through the ISM band and Bluetooth 4.0 / Bluetooth low energy.

Certainly not through the cellular network (Hence the above distinction between M2M and IoT is important).

Cellular will play a role in connectivity and there will be many successful applications / connectivity models (ex Jasper wireless which primarily require a SIM card in the device).

A more likely scenario is IoT specific networks like Sigfox(which could be deployed by anyone including Telecom Operators).  Sigfox currently uses the most popular European ISM band on 868MHz (as defined by ETSI and CEPT), along with 902MHz in the USA (as defined by the FCC), depending on specific regional regulations.

Also, when 5G networks are deployed (beyond 2020) - Cellular will provide wide area connectivity for IoT devices

In any case, Smart objects will generate a lot of Data .

.

Understanding the nature of IoT data

In the ultimate vision of IoT, Things are identifiable, autonomous, and self-configurable. Objects  communicate among themselves and interact with the environment. Objects can sense, actuate and predictively react to events

 

Billions of devices will create massive volume of streaming and geographically-dispersed data. This data will often need real-time responses.

There are primarily two modes of IoT data: periodic observations/monitoring or abnormal event reporting.

Periodic observations present demands due to their high volumes and storage overheads. Events on the other hand are one-off but need a rapid response.

In addition, if we consider video data(ex from surveillance cameras) as IoT Data, we have some additional characteristics.

Thus, our goal is to understand the implications of predictive analytics to IoT data. This ultimately entails using IoT data to make better decisions.

I will be exploring these ideas in the Data Science for IoT course /certification program when it's launched.

Comments welcome. In the next part of this series, I will explore Time Series data 

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Big Data, IOT and Security - OH MY!

While we aren’t exactly “following the yellow brick road” these days, you may be feeling a bit like Dorothy from the “Wizard of Oz” when it comes to these topics. No my friend, you aren’t in Kansas anymore! As seem above from Topsy, these three subjects are extremely popular these days and for the last 30 days seem to follow a similar pattern (coincidence?).

 

The internet of things is not just a buzzword and is no longer a dream, with sensors abound. The world is on its way to become totally connected, although it will take time to work out a few kinks here and there (with a great foundation, you create a great product; this foundation is what will take the most time). Your appliances will talk to you in your “smart house” and your “self-driving car” will take you to your super tech office where you will work with ease thanks to all the wonders of technology. But let’s step back to reality and think, how is all this going to come about, what will we do with all the data collected and how will we protect it?

 

First thing first is all the sensors have to be put in place, and many questions have to be addressed. Does a door lock by one vendor communicate with a light switch by another vendor, and do you want the thermostat to be part of the conversation and will anyone else be able to see my info or get into my home? http://www.computerworld.com/article/2488872/emerging-technology/explained--the-abcs-of-the-internet-of-things.html

How will all the needed sensors be installed and will there be any “human” interaction? It will take years to put in place all the needed sensors but there are some that are already engaging in the IOT here in the US. Hotels (as an example but not the only one investing in IOT) are using sensors connected to products that they are available for sale in each room, which is great but I recently had an experience with how “people” are the vital part of “IOT” – I went to check out of a popular hotel in Vegas, when I was asked if I drank one of the coffees in the room, I replied, “no, why” and was told that the sensor showed that I had either drank or moved the coffee, the hotel clerk verified that I had “moved” and not “drank” the coffee but without her, I would have been billed and had to refute the charge. Refuting charges are not exactly good for business and customers service having to handle “I didn’t purchase this” disputes 24/7 wouldn’t exactly make anyone’s day, so thank goodness for human interactions right there on the spot.

 

“The Internet of Things” is not just a US effort - Asia, in my opinion, is far ahead of the US, as far as the internet of things is concerned. If you are waiting in a Korean subway station, commuters can browse and scan the QR codes of products which will later be delivered to their homes. (Source: Tesco) - Transport for London’s central control centers use the aggregated sensor data to deploy maintenance teams, track equipment problems, and monitor goings-on in the massive, sprawling transportation systemTelent’s Steve Pears said in a promotional video for the project that "We wanted to help rail systems like the London Underground modernize the systems that monitor it’s critical assets—everything from escalators to lifts to HVAC control systems to CCTV and communication networks." The new smart system creates a computerized and centralized replacement for a public transportation system that used notebooks and pens in many cases. http://www.fastcolabs.com/3030367/the-london-underground-has-its-own-internet-of-things

 

But isn't the Internet of Things too expensive to implement? Many IoT devices rely on multiple sensors to monitor the environment around them. The cost of these sensors declined 50% in the past decade, according to Goldman Sachs. We expect prices to continue dropping at a steady rate, leading to an even more cost-effective sensor. http://www.businessinsider.com/four-elements-driving-iot-2014-10

 

 

The Internet of Things is not just about gathering of data but also about the analysis and use of data. So all this data generated by the internet of thing, when used correctly, will help us in our everyday life as consumer and help companies keep us safer by predicting and thus avoiding issues that could harm or delay, not to mention the costs that could be reduced from patterns in data for transportation, healthcare, banking, the possibilities are endless.

 

Let’s talk about security and data breaches – Now you may be thinking I’m in analytics or data science why should I be concerned with security? Let’s take a look at several breaches that have made the headlines lately.

 

Target recently suffered a massive security breach thanks to attacker infiltrating a third party. http://www.businessweek.com/articles/2014-03-13/target-missed-alarms-in-epic-hack-of-credit-card-data and so did Home depot http://www.usatoday.com/story/money/business/2014/11/06/home-depot-hackers-stolen-data/18613167/ PC world said “Data breach trends for 2015: Credit cards, healthcare records will be vulnerable http://www.pcworld.com/article/2853450/data-breach-trends-for-2015-credit-cards-healthcare-records-will-be-vulnerable.html

 

 

Sony was hit by hackers on Nov. 24, resulting in a company wide computer shutdown and the leak of corporate information, including the multimillion-dollar pre-bonus salaries of executives and the Social Security numbers of rank-and-file employees. A group calling itself the Guardians of Peace has taken credit for the attacks. http://www.nytimes.com/2014/12/04/business/sony-pictures-and-fbi-investigating-attack-by-hackers.html?_r=0

 

http://www.idtheftcenter.org/images/breach/DataBreachReports_2014.pdf

 

So how do we protect ourselves in a world of BIG DATA and the IOT?
Why should I – as a data scientist or analyst be worried about security, that’s not really part of my job is it? Well if you are a consultant or own your own business it is! Say, you download secure data from your clients and then YOU get hacked, guess who is liable if sensitive information is leaked or gets into the wrong hands? What if you develop a platform where the client’s customers can log in and check their accounts, credit card info and purchase histories are stored on this system, if stolen, it can set you up for a lawsuit. If you are a corporation, you are protected in some extents but what if you operate as a sole proprietor – you could lose your home, company and reputation. Still think security when dealing with big data isn’t important?

Organizations need to get better at protecting themselves and discovering that they’ve been breached plus we, the consultants, need to do a better job of protecting our own data and that means you can’t use password as a password! Let’s not make it easy for the hackers and let’s be sure that when we collect sensitive data and yes, even the data collected from cool technology toys connected to the internet, that we are security minded, meaning check your statements, logs and security messages - verify everything! When building your database, use all the security features available (masking, obfuscation, encryption) so that if someone does gain access, what they steal is NOT usable!

 

Be safe and enjoy what tech has to offer with peace of mind and at all cost, protect your DATA.

 

I’ll leave you with a few things to think about:


“Asset management critical to IT security”
"A significant number of the breaches are often caused by vendors but it's only been recently that retailers have started to focus on that," said Holcomb. "It's a fairly new concept for retailers to look outside their walls." (Source:  http://www.fierceretail.com/)

 

“Data Scientist: Owning Up to the Title”
Enter the Data Scientist; a new kind of scientist charged with understanding these new complex systems being generated at scale and translating that understanding into usable tools. Virtually every domain, from particle physics to medicine, now looks at modeling complex data to make our discoveries and produce new value in that field. From traditional sciences to business enterprise, we are realizing that moving from the "oil" to the "car", will require real science to understand these phenomena and solve today's biggest challenges. (Source:  http://www.datasciencecentral.com/profiles/blogs/data-scientist-owning-up-to-the-title)

 

 

Forget about data (for a bit) what’s your strategic vision to address your market?

Where are the opportunities given global trends and drivers? Where can you carve out new directions based on data assets? What is your secret sauce? What do you personally do on an everyday basis to support that vision? What are your activities? What decisions do you make as a part of those activities? Finally what data do you use to support these decisions?

http://www.datasciencecentral.com/profiles/blogs/top-down-or-bottom-up-5-tips-to-make-the-most-of-your-data-assets



Originally posted on Data Science Central 

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Guest blog post by Cameron Turner

Executive Summary

Though often the focus of the urban noise debate, Caltrain is one of many contributors to overall sound levels along the Bay Area’s peninsula corridor. In this investigation, Cameron Turner of Palo Alto’s The Data Guild takes a look at this topic using a custom-built Internet of Things (IoT) sensor atop the Helium networking platform.

Introduction

If you live in (or visit) the Bay Area, chances are you have experience with the Caltrain. Caltrain is a commuter line which travels 77.4 miles between San Francisco and San Jose , carrying over 50 thousand passengers on over 70 trains daily.[1]

I’m lucky to live two blocks from the Caltrain line, and enjoy the convenience of the train. My office, The Data Guild, is just one block away. The Caltrain and its rhythms, bells and horns are a part of our daily life, and connect us to the City and with connections to BART, Amtrak, SFO and SJC, the rest of the world.

Over the holidays, my 4-year-old daughter and I undertook a project to quantify the Caltrain through a custom-built sensor and reporting framework, to get some first-hand experience in the so-called Internet of Things (IoT). This project also aligns with The Data Guild’s broader ambition to build out custom sensor systems atop network technologies to address global issues. (More on this here.)

Let me note here that this project was an exploration, and was not conducted in a manner (in goals or methodology) to provide fodder for either side of the many ongoing caltrain debates: the electrification project, quiet zone, or tragic recent deaths on the tracks.

Background

My interest in such a project began with an article published in the Palo Alto Daily in October 2014. The article addressed the call for a quiet zone in downtown Palo Alto, following complaints from residents of buildings closest to the tracks. Many subjective frustrations were made by residents based on personal experience.

According the the Federal Railroad Administration (FRA), the rules by which Caltrain operates, train engineers “must begin to sound train horns at least 15 seconds, and no more than 20 seconds, in advance of all public grade crossings.”

Additionally: “Train horns must be sounded in a standardized pattern of 2 long, 1 short and 1 long blasts.” and “The maximum volume level for the train horn is 110 decibels which is a new requirement. The minimum sound level remains 96 decibels.“

Questions

Given the numeric nature of the rules, and the subjective nature of current analysis/discussion, it seemed an ideal problem to address with data. Some of the questions we hoped to address including and beyond this issue:

  • Timing: Are train horns sounded at the appropriate time?
  • Schedule: Are Caltrains coming and going on time?
  • Volume: Are the Caltrain horns sounding at the appropriate level?
  • Relativity: How do Caltrain horns contribute to overall urban noise levels?

Methodology

Our methodology to address these topics included several steps:

  1. Build a custom sensor equipped to capture ambient noise levels
  2. Leverage an uplink capability to receive data from the sensor in near real-time
  3. Deploy sensor then monitor sensor output and test/modify as needed
  4. Develop a crude statistical model to convert sensor levels (voltage) to sound levels (dB)
  5. Analysis and reporting

Apparatus

We developed a simple sensor based on the Arduino platform. A baseline Uno board, equipped with a local ATmega328 processor, was wired to and Adafruit Electret Microphone/Amplifier 4466 w/adjustable gain.

We were lucky to be introduced through the O’Reilly Strata NY event to a local company: Helium. Backed by Khosla Ventures et al, Helium is building an internet of things platform for smart machines. They combine a wireless protocol optimized for device and sensor data with cloud-based tooling for working with the data and building applications.

We received a Beta Kit which included a Arduino shield for uplink to their bridge device, which then connects via GSM to the Internet. Here is our sensor (left) with the Helium bridge device (right).

Deployment

With our instrument ready for deployment, we sought to find a safe location to deploy. By good fortune, a family friend (and member of the staff of the Stanford Statistics department, where I am completing my degree) owns a home immediately adjacent to a Caltrain crossing, where Caltrain operators are required to sound their horn.

Conductors might also be particularly sensitive to this crossing, Churchill St., due to its proximity to Palo Alto High School and the tragic train-related death of a teen, recently.

From a data standpoint, this location was ideal as it sits approximately half-way between the Palo Alto and California Avenue stations.

We deployed our sensor outdoors facing the track in a waterproof enclosure and watched the first data arrive.

Monitoring

Through a connector to Helium’s fusion platform, we were able to see data in near real-time. (note the “debug” window on the right, where microphone output level arrives each second).

We used another great service, provided by Librato, (now a part of SolarWinds) a San Francisco-based monitoring and metrics company. Using Librato, we enabled data visualization of the sound levels as they were generated. We were able to view this relative to its history. This was a powerful capability as we worked to fine-tune the power and amplifier.

Note the spike in the middle of the image above, which we could map to a train horn heard ourselves during the training period.

Data Preparation

Next, we took a weekday (January 7, 2015), which appeared typical of a non-holiday weekday relative to the entire month of data collected. For this period, we were able to construct a 24-hour data set at 1-second sample intervals for our analysis.

Data was accessed through the Librato API, downloaded as JSON, converted to CSV and cleansed.

Analysis

First, to gain intuition, we took a sample recording gathered at the sensor site of a typical train horn.

Click HERE to hear the sample sound.

Using matplotlib within an ipython notebook, we are able to “see” this sound, in both its raw audio form and as a spectrogram showing frequency:

Next, we look at our entire 24 hours of data, beginning on the evening of January 6, and concluding 24 hours later on the evening of January 7th. Note the quiet “overnight” period, about a quarter of the way across the x axis.

To put this into context, we overlay the Caltrain schedule. Given the sensor sits between the Palo Alto and California Avenue stations, and given the variance in stop times, we mark northbound trains using the scheduled stop at Palo Alto (red), and southbound trains using the scheduled stop at California Ave (green).

Initially, we can make two converse observations: many peak sound events tend to lie quite close to these stop times, as expected. However: many of the sound events (including the maximum recorded value, the nightly ~11pm freight train service) occur independent of the scheduled Caltrains.

Conversion to Decibels

On the Y axis above, the sound level is reported in the raw voltage output from the Microphone. To address the questions above we needed a way to convert these values to decibel units (dB).

To do so, a low-cost sound meter was obtained from Fry’s. Then an on-site calibration was performed to map decibel readings from the sensor to the voltage output uploaded from our microphone.

Within R Studio, these values were plotted and a crude estimation function was derived to create a linear mapping between voltage and dB:

The goal of doing a straight line estimate vs. log-linear was to compensate for differences in apparatus (dB meter vs. microphone within casing) and overall to maintain conservative approximations. Most of the events in question during the observation period were between 2.0 and 2.5 volts, where we collected several training points (above).

A challenge in this process was the slight lag between readings and data collection with unknown variance. As such, only “peak” and “trough” measurements could be used reliably to build the model.

With this crude conversion estimator in hand, we would now replot the data above with decibels on the y axis.

Clearly the “peaks” above are of interest as outliers from the baseline noise level at this site. In fact, there are 69 peaks (>82 dB) observed (at 1-second sample rate), and 71 scheduled trains for this same period. Though this location was about 100 yards removed from the tracks, the horns are quieter than the recommended 96dB-115dB range recommended by the FRA. (With caveat above re: crude approximator)

Interesting also that we’re not observing the “two long-two short-one long” pattern. Though some events are lost to the sampling rate, qualitatively this does not seem to be a standard practice followed by the engineers. Those who live in Palo Alto also know this to be true, qualitatively.

Also worth noting is the high variance of ambient noise, the central horizontal blue “cloud” above, ranging from ~45 dB to ~75 dB. We sought to understand the nature of this variance and whether it contained structure.

Looking more closely at just a few minutes of data during the Jan 7 morning commute, we can see that indeed there is a periodic structure to the variance.

In comparing to on-site observations, we could determine that this period was defined by the traffic signal which sits between the sensor and the train tracks, on Alma St. Additionally, we often observe an “M” structure (bimodal peak) indicating the southbound traffic accelerating from the stop line when the light turned green, followed by the passing northbound traffic seconds later.

Looking at a few minutes of the same morning commute, we can clearly see when the train passed and sounded its horn. Here again, green indicates a southbound train, red indicates and northbound train.

In this case, the southbound train passed slightly before its scheduled arrival time at the California Avenue station, and the Northbound train passed within its scheduled arrival minute, both on time. Note also the peak unassociated with the train. We’ll discuss this next.

Perhaps a more useful summary of the data collected is shown as a histogram, where the decibels are shown on the X axis and the frequency (count) is shown on the Y axis.

We can clearly see a bimodal distribution, where sound is roughly normally distributed, with a second distribution at the higher end. The question still remained why several of the peak observed values fell nowhere near the scheduled train time?

The answer here requires no sensors: airplanes, sirens and freight trains are frequent noise sources in Palo Alto. These factors, coupled with a nearby residential construction project accounted for the non-regular noise events we observed.

Click HERE to hear a sample sound.

Finally, we subsetted the data into three groups, one to look at non-Train minutes, one to look at northbound train minutes and one to look at southbound train minutes. The mean dB levels were 52.13, 52.18 and 52.32 respectively. While the order here makes sense, these samples bury the outcome since a horn blast may only be one second of a train-minute. The difference between northbound and southbound are consistent with on-site observation-- given the sensor lies on the northeast corner of the crossing, horn blasts from southbound trains were more pronounced.

Conclusion

Before making any conclusions it should be noted again that these are not scientific findings, but rather an attempt to add some rigor to the discussion around Caltrain and noise pollution. Further study with a longer period of analysis and duplicity of data collection would be required to statistically state these conclusions.

That said, we can readdress the topics in question:

Timing: Are train horns sounded at the appropriate time?

The FRA recommends engineers sound their horn between 15 and 20 seconds before a crossing. Given the tight urban nature of this crossing this recommendation seems a misfit. Caltrain engineers are sounding within 2-3 seconds of the crossing, which seems more appropriate.

Schedule: Are Caltrains coming and going on time?

Though not explored in depth here, generally we can observe that trains are passing our sensor prior to their scheduled arrival at the upcoming station.

Volume: Are the Caltrain horns sounding at the appropriate level?

As discussed above, the apparent dB level at a location very close to the track was well below the FRA recommended levels.

Relativity: How do Caltrain horns contribute to overall urban noise levels?

The Caltrain horns generate roughly an additional 10dB to peak baseline noise levels, including period traffic events at the intersection observed.

Opinions

Due to their regular frequency and physical presence, trains are an easy target when it comes to urban sound attenuation efforts. However, the regular oscillations of traffic, sirens, airplanes and construction create a very high, if not predictable baseline above which trains must be heard.

Considering the importance of safety to this system, which operates just inches from bikers, drivers and pedestrians, there is a tradeoff to be made between supporting quiet zone initiatives and the capability of speeding trains to be heard.

In Palo Alto, as we move into an era of electric cars, improved bike systems and increased pedestrian access, the oscillations of noise created by non-train activities may indeed subside over time. And this in turn, might provide an opportunity to lower the “alert sounds” such as sirens and train horns required to deliver these services safely. Someday much of our everyday activity might be accomplished quietly.

Until then, we can only appreciate these sounds which must rise above our noisy baseline, as a reminder of our connectedness to the greater bay area through our shared focus on safety and convenient public transportation.

Acknowledgements:

Sincere thanks to Helen T. and Nick Parlante of Stanford University, Mark Phillips of Helium and Nik Wekwerth/Jason Derrett/Peter Haggerty of Librato for their help and technical support.

Thanks also to my peers at The Data Guild, Aman, Chris, Dave and Sandy and the Palo Alto Police IT department for their feedback.

And thanks to my daughter Tallulah for her help soldering and moral support.

[1] http://en.wikipedia.org/wiki/Caltrain

Originally posted on LinkedIn. 

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Guest blog post by ajit jaokar

By Ajit Jaokar @ajitjaokar Please connect with me if you want to stay in touch on linkedin and for future updates

Cross posted from my blog - I look forward to discussion/feedback here

Note: The paper below is best read as a pdf which you can download from the blog for free

Background and Abstract

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things  (modelled on the course I teach at Oxford University and UPM – Madrid). I will also present these ideas at the International conference on City Sciences at Tongji University in Shanghai  and the Data Science for IoT workshop at the Iotworld event in San Francisco

Please connect with me if you want to stay in touch on linkedin and for future updates

Deep Learning

Deep learning is often thought of as a set of algorithms that ‘mimics the brain’. A more accurate description would be an algorithm that ‘learns in layers’. Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts.

The obscure world of deep learning algorithms came into public limelight when Google researchers fed 10 million random, unlabeled images from YouTube into their experimental Deep Learning system. They then instructed the system to recognize the basic elements of a picture and how these elements fit together. The system comprising 16,000 CPUs was able to identify images that shared similar characteristics (such as images of Cats). This canonical experiment showed the potential of Deep learning algorithms. Deep learning algorithms apply to many areas including Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition etc

 

How does a Computer Learn?

To understand the significance of Deep Learning algorithms, it’s important to understand how Computers think and learn. Since the early days, researchers have attempted to create computers that think. Until recently, this effort has been rules based adopting a ‘top down’ approach. 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.

To overcome these limitations, a bottom-up approach was proposed. The idea here is to learn from experience. The experience was provided by ‘labelled data’. Labelled data is fed to a system and the system is trained based on the responses. This approach works for applications like Spam filtering. However, most data (pictures, video feeds, sounds, etc.) is not labelled and if it is, it’s not labelled well.

The other issue is in handling problem domains which are not finite. For example, the problem domain in chess is complex but finite because there are a finite number of primitives (32 chess pieces)  and a finite set of allowable actions(on 64 squares).  But in real life, at any instant, we have potentially a large number or infinite alternatives. The problem domain is thus very large.

A problem like playing chess can be ‘described’ to a computer by a set of formal rules.  In contrast, many real world problems are easily understood by people (intuitive) but not easy to describe (represent) to a Computer (unlike Chess). Examples of such intuitive problems include recognizing words or faces in an image. Such problems are hard to describe to a Computer because the problem domain is not finite. Thus, the problem description suffers from the curse of dimensionality i.e. when the number of dimensions increase, the volume of the space increases so fast that the available data becomes sparse. Computers cannot be trained on sparse data. Such scenarios are not easy to describe because there is not enough data to adequately represent combinations represented by the dimensions. Nevertheless, such ‘infinite choice’ problems are common in daily life.

How do Deep learning algorithms learn?

Deep learning is involved with ‘hard/intuitive’ problem which have little/no rules and high dimensionality. Here, the system must learn to cope with unforeseen circumstances without knowing the Rules in advance. Many existing systems like Siri’s speech recognition and Facebook’s face recognition work on these principles.  Deep learning systems are possible to implement now because of three reasons: High CPU power, Better Algorithms and the availability of more data. Over the next few years, these factors will lead to more applications of Deep learning systems.

Deep Learning algorithms are modelled on the workings of the Brain. The Brain may be thought of as a massively parallel analog computer which contains about 10^10 simple processors (neurons) – each of which require a few milliseconds to respond to input. To model the workings of the brain, in theory, each neuron could be designed as a small electronic device which has a transfer function similar to a biological neuron. We could then connect each neuron to many other neurons to imitate the workings of the Brain. In practise,  it turns out that this model is not easy to implement and is difficult to train.

So, we make some simplifications in the model mimicking the brain. The resultant neural network is called “feed-forward back-propagation network”.  The simplifications/constraints are: We change the connectivity between the neurons so that they are in distinct layers. Each neuron in one layer is connected to every neuron in the next layer. Signals flow in only one direction. And finally, we simplify the neuron design to ‘fire’ based on simple, weight driven inputs from other neurons. Such a simplified network (feed-forward neural network model) is more practical to build and use.

Thus:

a)      Each neuron receives a signal from the neurons in the previous layer

b)      Each of those signals is multiplied by a weight value.

c)      The weighted inputs are summed, and passed through a limiting function which scales the output to a fixed range of values.

d)      The output of the limiter is then broadcast to all of the neurons in the next layer.

Image and parts of description in this section adapted from : Seattle robotics site

The most common learning algorithm for artificial neural networks is called Back Propagation (BP) which stands for “backward propagation of errors”. To use the neural network, we apply the input values to the first layer, allow the signals to propagate through the network and read the output. A BP network learns by example i.e. we must provide a learning set that consists of some input examples and the known correct output for each case. So, we use these input-output examples to show the network what type of behaviour is expected. The BP algorithm allows the network to adapt by adjusting the weights by propagating the error value backwards through the network. Each link between neurons has a unique weighting value. The ‘intelligence’ of the network lies in the values of the weights. With each iteration of the errors flowing backwards, the weights are adjusted. The whole process is repeated for each of the example cases. Thus, to detect an Object, Programmers would train a neural network by rapidly sending across many digitized versions of data (for example, images)  containing those objects. If the network did not accurately recognize a particular pattern,  the weights would be adjusted. The eventual goal of this training is to get the network to consistently recognize the patterns that we recognize (ex Cats).

How does Deep Learning help to solve the intuitive problem

The whole objective of Deep Learning is to solve ‘intuitive’ problems i.e. problems characterized by High dimensionality and no rules.  The above mechanism demonstrates a supervised learning algorithm based on a limited modelling of Neurons – but we need to understand more.

Deep learning allows computers to solve intuitive problems because:

  • With Deep learning, Computers can learn from experience but also can understand the world in terms of a hierarchy of concepts – where each concept is defined in terms of simpler concepts.
  • The hierarchy of concepts is built ‘bottom up’ without predefined rules by addressing the ‘representation problem’.

This is similar to the way a child learns ‘what a dog is’ i.e. by understanding the sub-components of a concept ex  the behavior(barking), shape of the head, the tail, the fur etc and then putting these concepts in one bigger idea i.e. the Dog itself.

The (knowledge) representation problem is a recurring theme in Computer Science.

Knowledge representation incorporates theories from psychology which look to understand how humans solve problems and represent knowledge.  The idea is that: if like humans, Computers were to gather knowledge from experience, it avoids the need for human operators to formally specify all of the knowledge that the computer needs to solve a problem.

For a computer, the choice of representation has an enormous effect on the performance of machine learning algorithms. For example, based on the sound pitch, it is possible to know if the speaker is a man, woman or child. However, for many applications, it is not easy to know what set of features represent the information accurately. For example, to detect pictures of cars in images, a wheel may be circular in shape – but actual pictures of wheels may have variants (spokes, metal parts etc). So, the idea of representation learning is to find both the mapping and the representation.

If we can find representations and their mappings automatically (i.e. without human intervention), we have a flexible design to solve intuitive problems.   We can adapt to new tasks and we can even infer new insights without observation. For example, based on the pitch of the sound – we can infer an accent and hence a nationality. The mechanism is self learning. Deep learning applications are best suited for situations which involve large amounts of data and complex relationships between different parameters. Training a Neural network involves repeatedly showing it that: “Given an input, this is the correct output”. If this is done enough times, a sufficiently trained network will mimic the function you are simulating. It will also ignore inputs that are irrelevant to the solution. Conversely, it will fail to converge on a solution if you leave out critical inputs. This model can be applied to many scenarios as we see below in a simplified example.

An example of learning through layers

Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts. This approach works for subjective and intuitive problems which are difficult to articulate.

Consider image data. Computers cannot understand the meaning of a collection of pixels. Mappings from a collection of pixels to a complex Object are complicated.

With deep learning, the problem is broken down into a series of hierarchical mappings – with each mapping described by a specific layer.

The input (representing the variables we actually observe) is presented at the visible layer. Then a series of hidden layers extracts increasingly abstract features from the input with each layer concerned with a specific mapping. However, note that this process is not pre defined i.e. we do not specify what the layers select

For example: From the pixels, the first hidden layer identifies the edges

From the edges, the second hidden layer identifies the corners and contours

From the corners and contours, the third hidden layer identifies the parts of objects

Finally, from the parts of objects, the fourth hidden layer identifies whole objects

Image and example source: Yoshua Bengio book – Deep Learning

Implications for IoT

To recap:

  • Deep learning algorithms apply to many areas including Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition etc
  • Deep learning systems are possible to implement now because of three reasons: High CPU power, Better Algorithms and the availability of more data. Over the next few years, these factors will lead to more applications of Deep learning systems.
  • Deep learning applications are best suited for situations which involve large amounts of data and complex relationships between different parameters.
  • Solving intuitive problems: Training a Neural network involves repeatedly showing it that: “Given an input, this is the correct output”. If this is done enough times, a sufficiently trained network will mimic the function you are simulating. It will also ignore inputs that are irrelevant to the solution. Conversely, it will fail to converge on a solution if you leave out critical inputs. This model can be applied to many scenarios

In addition, we have limitations in the technology. For instance, we have a long way to go before a Deep learning system can figure out that you are sad because your cat died(although it seems Cognitoys based on IBM watson is heading in that direction). The current focus is more on identifying photos, guessing the age from photos(based on Microsoft’s project Oxford API)

And we have indeed a way to go as Andrew Ng reminds us to think of Artificial Intelligence as building a rocket ship

“I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. If you have a large engine and a tiny amount of fuel, you won’t make it to orbit. If you have a tiny engine and a ton of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel. The analogy to deep learning [one of the key processes in creating artificial intelligence] is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.”

Today, we are still limited by technology from achieving scale. Google’s neural network that identified cats had 16,000 nodes. In contrast, a human brain has an estimated 100 billion neurons!

There are some scenarios where Back propagation neural networks are suited

  • A large amount of input/output data is available, but you’re not sure how to relate it to the output. Thus, we have a larger number of “Given an input, this is the correct output” type scenarios which can be used to train the network because it is easy to create a number of examples of correct behaviour.
  • The problem appears to have overwhelming complexity. The complexity arises from Low rules base and a high dimensionality and from data which is not easy to represent.  However, there is clearly a solution.
  • The solution to the problem may change over time, within the bounds of the given input and output parameters (i.e., today 2+2=4, but in the future we may find that 2+2=3.8) and Outputs can be “fuzzy”, or non-numeric.
  • Domain expertise is not strictly needed because the output can be purely derived from inputs: This is controversial because it is not always possible to model an output based on the input alone. However, consider the example of stock market prediction. In theory, given enough cases of inputs and outputs for a stock value, you could create a model which would predict unknown scenarios if it was trained adequately using deep learning techniques.
  • Inference:  We need to infer new insights without observation. For example, based on the pitch of the sound – we can infer an accent and hence a nationality

Given an IoT domain, we could consider the top-level questions:

  • What existing applications can be complemented by Deep learning techniques by adding an intuitive component? (ex in smart cities)
  • What metrics are being measured and predicted? And how could we add an intuitive component to the metric?
  • What applications exist in Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition etc which also apply to IoT

Now, extending more deeply into the research domain, here are some areas of interest that I am following.

Complementing Deep Learning algorithms with IoT datasets

In essence, these techniques/strategies complement Deep learning algorithms with IoT datasets.

1)      Deep learning algorithms and Time series data : Time series data (coming from sensors) can be thought of as a 1D grid taking samples at regular time intervals, and image data can be thought of as a 2D grid of pixels. This allows us to model Time series data with Deep learning algorithms (most sensor / IoT data is time series).  It is relatively less common to explore Deep learning and Time series – but there are some instances of this approach already (Deep Learning for Time Series Modelling to predict energy loads using only time and temp data  )

2)      Multiple modalities: multimodality in deep learning. Multimodality in deep learning algorithms is being explored  In particular, cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time

3)      Temporal patterns in Deep learning: In their recent paper, Ph.D. student Huan-Kai Peng and Professor Radu Marculescu, from Carnegie Mellon University’s Department of Electrical and Computer Engineering, propose a new way to identify the intrinsic dynamics of interaction patterns at multiple time scales. Their method involves building a deep-learning model that consists of multiple levels; each level captures the relevant patterns of a specific temporal scale. The newly proposed model can be also used to explain the possible ways in which short-term patterns relate to the long-term patterns. For example, it becomes possible to describe how a long-term pattern in Twitter can be sustained and enhanced by a sequence of short-term patterns, including characteristics like popularity, stickiness, contagiousness, and interactivity. The paper can be downloaded HERE

Implications for Smart cities

I see Smart cities as an application domain for Internet of Things. Many definitions exist for Smart cities/future cities. From our perspective, Smart cities refer to the use of digital technologies to enhance performance and wellbeing, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens (adapted from Wikipedia). Key ‘smart’ sectors include transport, energy, health care, water and waste. A more comprehensive list of Smart City/IoT application areas are: Intelligent transport systems – Automatic vehicle , Medical and Healthcare, Environment , Waste management , Air quality , Water quality, Accident and  Emergency services, Energy including renewable, Intelligent transport systems  including autonomous vehicles. In all these areas we could find applications to which we could add an intuitive component based on the ideas above.

Typical domains will include Computer Vision, Image recognition, pattern recognition, speech recognition, behaviour recognition. Of special interest are new areas such as the Self driving cars – ex theLutz pod and even larger vehicles such as self driving trucks

Conclusions

Deep learning involves learning through layers which allows a computer to build a hierarchy of complex concepts out of simpler concepts. Deep learning is used to address intuitive applications with high dimensionality.  It is an emerging field and over the next few years, due to advances in technology, we are likely to see many more applications in the Deep learning space. I am specifically interested in how IoT datasets can be used to complement deep learning algorithms. This is an emerging area with some examples shown above. I believe that it will have widespread applications, many of which we have not fully explored(as in the Smart city examples)

I see this article as part of an evolving theme. Future updates will explore how Deep learning algorithms could apply to IoT and Smart city domains. Also, I am interested in complementing Deep learning algorithms using IoT datasets.

I elaborate these ideas in the Data Science for Internet of Things program  (modelled on the course I teach at Oxford University and UPM – Madrid). I will also present these ideas at the International conference on City Sciences at Tongji University in Shanghai  and the Data Science for IoT workshop at the Iotworld event in San Francisco

Please connect with me if you want to stay in touch on linkedin and for future updates

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Guest blog post by ajit jaokar

Often, Data Science for IoT differs from conventional data science due to the presence of hardware.

Hardware could be involved in integration with the Cloud or Processing at the Edge (which Cisco and others have called Fog Computing).

Alternately, we see entirely new classes of hardware specifically involved in Data Science for IoT(such as synapse chip for Deep learning)

Hardware will increasingly play an important role in Data Science for IoT.

A good example is from a company called Cognimem which natively implements classifiers(unfortunately, the company does not seem to be active any more as per their twitter feed)

In IoT, speed and real time response play a key role. Often it makes sense to process the data closer to the sensor.

This allows for a limited / summarized data set to be sent to the server if needed and also allows for localized decision making.  This architecture leads to a flow of information out from the Cloud and the storage of information at nodes which may not reside in the physical premises of the Cloud.

In this post, I try to explore the various hardware touchpoints for Data analytics and IoT to work together.

Cloud integration: Making decisions at the Edge

Intel Wind River edge management system certified to work with the Intel stack  and includes capabilities such as data capture, rules-based data analysis and response, configuration, file transfer and  Remote device management

Integration of Google analytics into Lantronix hardware –  allows sensors to send real-time data to any node on the Internet or to a cloud based application.

Microchip integration with Amazon Web services  uses an  embedded application with the Amazon Elastic Compute Cloud (EC2) service. Based on  Wi-Fi Client Module Development Kit . Languages like Python or Ruby can be used for development

Integration of Freescale and Oracle which consolidates data collected from multiple appliances from multiple Internet of things service providers.

Libraries

Libraries are another avenue for analytics engines to be integrated into products – often at the point of creation of the device. Xively cloud services is an example of this strategy through xively libraries

APIs

In contrast, keen.io provides APIs for IoT devices to create their own analytics engines ex (smartwatch Pebble’s using of keen.io)  without locking equipment providers into a particular data architecture.

Specialized hardware

We see increasing deployment  of specialized hardware for analytics. Ex egburt from Camgian which uses sensor fusion technolgies for IoT.

In the Deep learning space, GPUs are widely used and more specialized hardware emerges such asIBM’s synapse chip. But more interesting hardware platforms are emerging such as Nervana Systemswhich creates hardware specifically for Neural networks.

Ubuntu Core and IFTTT spark

Two more initiatives on my radar deserve a space in themselves – even when neither of them have currently an analytics engine:  Ubuntu Core – Docker containers+lightweight Linux distribution as an IoT OS and IFTTT spark initiatives

Comments welcome

This post is leading to vision for Data Science for IoT course/certification. Please sign up on the link if you wish to know more when launched in Feb.

Image source: cognimem

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