Monthly Archives: May 2016

MITProfessionalX Course Diary – IOTx Internet of Things: Roadmap to a Connected World – Week 5 & Conclusion

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Week 5 of the MITProfessionalX IOTx Internet of Things: Roadmap to a Connected World course concluded with the Applications module, specifically:

Beyond IoT – Ubiquitous Sensing and Human Experience (Joe Paradiso)

    • Emerging Descriptive data standards for IoT and sensors
    • Immersive visualization of diverse sensor data using game engines (part of IoT’s ‘control panel’)
    • Wearable sensing for IoT (future user interfaces for IoT – new ways to control and interact with your environment)
    • Sensors and paradigms for seamless Interaction with the Built Environment (lighting, heating, etc.)
    • Smart Tools for IoT
    • Smart, sensate materials

Wireless Technologies for Indoor Localization, Smart Homes, and Smart Health (Dina Kitabi)

      • Smart health
      • Home automation
      • Location tracking

Smart Cities (Carlo Ratti)

      • The city as a cyber physical system
      • Principles of cybernetics: sensing and actuating
      • Collection of information: opportunistic sensing (a)
      • Collection of information: crowd sensing (b)
      • Collection of information: ad hoc sensing (c)
      • Response of the system: analytics and optimization
      • Response of the system: distributed action, people as intelligent actuators
      • Price of anarchy
      • Hacking the city: the risk for cyber attacks in centralized and distributed systems
      • Smart city equals Smart Citizens

The final module of the course (and the previous module) complemented, conveniently, much of what was revealed this week at Google I/O 2016 – Smart Homes, Smart Buildings and Smart Cities. The world is changing very quickly. Things will change in the near future as sensors become ubiquitous and the way we plug into them becomes more and more intimate. The sensors are already out there, piggybacking on the back of devices that are already in place. Sensors are getting cheaper, as the cost comes down everything becomes accessible and the ability to innovate will be widespread. At I/O this year, Google displayed its vision for a more ubiquitous and conversational way of interacting with technology. Its Assistant is chattier, answering natural language queries with a more human voice, and it’s found its way into several new Google products: the messenger Allo and the Echo-like speaker Home. Both are areas where other companies have a lead, but Google’s strength in AI gave these services some nice twists, doing things like automatically generating surprisingly specific reactions to photos. But you don’t have to have Google’s resources to able to play in this space, a RasPi or Arduino can get things going and as Profesor Sarma pointed out in his last slide ‘Just do it, thoughtfully. But do something.

  • Why? Because IoT is in your future, and IoT literacy is essential.
  • IoT is very personal to your company. You need to figure out how it will impact your business.

His roadmap, beyond the ‘walled gardens’ of NEST, HomeKit and Smart Hub is encouraging and his advice applies to all of us and our organisations.



‘Finally, over time, I think that what’s going to happen is we’re going to go to a three-tier architecture. You have the device, you have the cloud, and you have edge computing, if you need performance. And that’s really my prediction for where this world is going to go.
IoT is in the future. Devices you buy will be IoT-enabled. Your homes will be IoT enabled. And it’s going to become a competitive thing.
And so what you need is what I call IoT literacy. It’s a way of thinking, which is how do I instrument and take advantage of it because it is happening. Don’t fight it, in fact, try and win it.
Just imagine if you had fought the cell phone 10 years ago. If you didn’t use your iPhone, your Android phone, or your Microsoft phone. If you didn’t do text messaging. If you didn’t do scheduling on your phone. If you didn’t use Google Maps or Apple Maps, just imagine, you would have been at a disadvantage.
I would use the same thing. I mean if you have a factory that refuses to monitor valves using connectivity, compared to a company that has a factory that does. And if their insurance goes down, you’re at a disadvantage. So it is in your future. I predict it. And don’t fight it.
But it is very personal to you. What I mean by that is when we bring the technology in, let’s say a cell phone. The cell phone is very personal to me. I use it in a way that is different from even a close colleague of mine. For example, I may use certain features more than she does.
My wife and I use our cell phones subtly differently, but within our family we have a certain pattern. We know how to reach each other. We prefer text to a call. IoT is like that.

If your business is your family, you will adapt IoT to your business. Your business probably has an advantage– you do something different and it, gives you an advantage. So IoT has to wrap itself around that, so that you can use IoT to make the thing that makes you different more advantageous.
And so you have to figure out how to use it. Now, I’m not saying don’t work with consultants. But if you work with a consultant, work with a consultant who understands the process. The IT part of it will come later.

But if you start with the IT, you will put the cart before the horse. The IT will dictate what you should be doing as opposed to the process. So figure out your process and figure out precisely where IoT can help you, then let’s figure out the IT.

The next thing I recommend is build a real system and try and use it. I assure you the learnings will be fundamental. And it will give you a very gut-level, visceral IoT literacy that you will need.
And here’s the next thing– be ready to fail, be ready to iterate just as you would with math, just as you would with a new technique, just as you would if you, for example, decided to go buy a bike, and you’ve never ridden a bike. But you’ll figure it out. It’s the same thing. You got to learn to iterate because, again, this is going to be deep in your use, and you’ve got to figure out how this thing works.’

This has been an excellent course and the whole experience has enhanced my understanding of the Internet of Things, its technologies and applications. Taking the course has also prompted three important takeaways:

  • Updating our skills is critical, we must be constantly learning
  • We must get comfortable with change
  • If we don’t take heed of the first two points things could get tricky for ourselves and our organisations. – See Twelve ways to survive the race to irrelevance

MITProfessionalX Course Diary – IOTx Internet of Things: Roadmap to a Connected World – Week 4

Week 4 of the MITProfessionalX IOTx Internet of Things: Roadmap to a Connected World course continued with the Technologies module, specifically:

Security in IoT (Srini Devadas)

  • Why is security for IoT so hard?
  • Threat models
  • Defensive strategies and examples

HCI in an IoT World (Jim Glass)

  • Theory and applications of spoken dialogue for human-computer interaction
  • Combining speech with other modalities for natural interaction
  • Considerations for multilingual interactions
  • Paralinguistic information from speech for enhanced HCI
  • Future challenges for ubiquitous speech interfaces

Robotics and Autonomous Vehicles (John Leonard)

  • Potential benefits of self-driving vehicles and service robots
  • Sensing and data processing
  • Simultaneous mapping and localization
  • Levels of autonomy
  • Future research challenges

Security has been the most complex topic so far in my opinion. Security is a huge challenge and when privacy is added to the complexity its clear they are two very, very important topics in the Internet of Things. Srini Devadas (Professor, MIT Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology, MS and PhD from the University of California, received the IEEE Computer Society Technical Achievement Award in 2014 for inventing Physical Unclonable Functions and single-chip secure processor architectures.) says ‘security is a challenging problem, because it’s a negative goal’. Prefesor Devadas uses the example of accessing a .txt file. He gives us a myriad of ways that someone could attack another and discover the .txt, and he could keep going on and on. How do you know that you’ve thought of all the ways to stop an attack? You don’t, and that is why security is a challenging problem, because it’s a negative goal.

There are three defensive strategies for IoT systems: prevention, resilience, and detection & recovery. This is where the complexity factor begins to nudge up a quite a few notches, like physical unclonable functions that correspond to protecting integrated circuits from physical attacks to extract secret keys that are stored in the integrated circuits. I had to read the transcript a number of times as the video lecture wasn’t enough to be able to understand the concepts in one sitting. In particular there was a section that was very mathematical and gave examples of the gen algorithm and the Learning Parity with Noise problem. Profesor Devadas uses an interesting analogy to describe the notion of computation under encryption.

Let’s say Alice wants to buy a beautiful ring for herself. Not only that, she wants to design this ring. She is going to hire jewelry workers to create this ring for her and give them raw materials to do this. But there’s a problem here. The problem is one of theft. The jewelry workers could create the ring for her and just walk away with the ring. How could she protect against this scenario?

Alice could create a locked glove box and put her raw materials inside the locked glove box. Alice puts the raw materials in a locked glove box. The jewelry workers are going to put their hands into the locked glove box, work on the raw materials, and create a ring, except that they have no idea that they’re even creating a ring. It’s only Alice that knows that they’re creating a ring for her. The jewelry workers, after they have finished their task, are going to take their hands out of the locked glove box and walk away. Alice willpresumably pay them for their work. But now Alice is going to be able to open up the locked glove box in private and take out her beautiful ring and enjoy it.

Given this jewelry example, let me tell you exactly what happens from a mathematical standpoint. The analogy here is that encrypting is putting raw materials into the locked glove box. So the raw materials correspond to sensitive data that’s associated with Alice’s DNA, for example.

Decrypting is taking things out of the box. As I mentioned, the jewelers have no idea that they’re building a ring. They simply produce an encrypted result in the mathematical domain. Alice is able to take the encrypted results and decrypt it to obtain her diagnosis. The computation is the process of assembling the jewelry, and this corresponds to computing on encrypted data. We need particular mathematical structures corresponding to the encryption and decryption algorithms to ensure that the computation on the encrypted data, to produce an encrypted result, produces exactly the same result as if Alice had computed on the original sensitive data using standard operations.

The profesor gave one example for each of the defensive strategies, ‘There are many other examples. Typically, these examples correspond to different layers of abstraction or correspond to different layers of software and hardware in an IoT system. To build a secure system may require such mechanisms at all layers of abstraction– the compiler, operating system, the application, and the hardware.’

The HCI and Robotics & Autonomous Vehicles lectures were an interesting history lesson on how both these technologies via Siri, Cortana, Alexa and Google’s driverless car etc are testament to the pace of technogical change. The future is much closer than we think.

Jim Glass:

I think speech based interfaces for IOT is inevitable. Our devices are getting smaller. We want to talk to them all of the time. It’s just so natural for people. We’ve crossed that point in our society where speech is out there and people want more of it. And I think that’s what is going to happen.

These interfaces are the future. They have to be untethered. They have to be robust to different environments, different contexts. They have to understand in larger context. Have to incorporate different modalities. Have to be multilingual. The types of things we see out there now coming out of the commercial market on smartphones and other devices is just the tip of the iceberg. Much more remains to be done. There’s lots of challenges, but the future is exciting.

And finally from John Loenard:

I want to see learning on steroids, lifelong learning where you can really think about the limit, as time goes to infinity, how does a system get better and better and learn more and more about the world?

And ultimately this entails connecting to the cloud. When one robot learns a Coke can, every robot should know what a Coke can is. This notion of sharing information, things getting logged to the cloud. I have this notion of a robot that operates autonomously each day, capturing new experiences. And then at night when it goes back and connects to charge its batteries, there’s a sort of dreaming that happens overnight, of trying to makesense of all the data of that day and connect it to the data previously acquired by itself and other robots to try to build ever richer and deeper understandings of the world.

Next week the course covers applications, specifically: Beyond IoT – Ubiquitous Sensing and Human Experience and Wireless Technologies for Indoor Localization, Smart Homes, and Smart Health

MITProfessionalX Course Diary – IOTx Internet of Things: Roadmap to a Connected World – Week 3

Week 3 of the MITProfessionalX IOTx Internet of Things: Roadmap to a Connected World course concentrated on the Technologies module, specifically:

Network Connectivity for IoT (Hari Balakrishnan)

A simplified IoT network architecture
Room/body-are networks: Bluetooth Low Energy
Extending communication range

Data Processing and Storage (Sam Madden)

Managing high rate sensor data
Processing data streams
Data consistency in an intermittently connected or disconnected environment
Identifying outliers and anomalies

Localization (Daniela Rus)

Localization algorithms
Indoor localization
Localization for mobile systems

And this is where things (no pun intended) start to get complicated. There is the complexity of IoT networking options for example. Why can’t we just use the wireless technologies that we have for the internet, our cellphones to build IoT systems? Can’t we just use cylinder networks and Wi-Fi technologies? Why do we need something new? The answers aren’t immediately obvious but when you think about it cellular networks are limited by the battery life of, for example, your mobile devices (aka gateways) and are expensive. Wi-Fi networks are limited by their range, the fundamental problem of power consumption is why cellular and Wi-Fi technologies are not applicable to a wide range of IoT scenarios.

Basically, IoT is about unusual events. Well, more specifically, data is at the core of those events. Consider applications in the space of infrastructure monitoring, like home monitoring, or monitoring pipes or other industrial equipment, or medical device monitoring. This is really about understanding when something interesting happens in these monitored devices. And the interesting thing that happens is fundamentally conveyed in data. For example, you might want to know that the temperature in your home went below some threshold, and the pipes are about to burst. Or with a medical patient, you might want to see some signal, like a brain or heart signal, that is showing some sort of anomalous value. Data starts from the sensors, it flows through the phones and base stations, and then ultimately ends up in a cloud-based infrastructure. Then there is the issue of missing and noisy data. These sensors, because they are sampling the real world, have periods of time that are not covered by the data itself. Also, the data that is coming from these sensors and these applications often has anomalies in it, things that are unusual or outliers. And so one of the real challenge is how do we detect and correct those kinds of outliers and anomalies? The classification-based method and frequent itemsets of course! Classification, for those of you like me who weren’t aware of it, is basically a way of giving outliers and anomalies a data set and dividing them into multiple classes. Frequent itemset mining basically compares the frequency of different sets of outliers to the frequency of the sets that occur in the inliers. What are the common things that occur in the outliers? In the frequent itemset mining world its about support. Support means detecting the elements that occur in one of set of data with more than some sort of frequency, ie more than two times.

However, the most complicated part is localization. Devices will have to instantaneously localize themselves. They will have to have a sense of identity and they will have to have a sense of the surrounding world. How does a device compute its position and its heading in the world? Range-based localization and bearing-based localization of course! Unfortunately, this is where trigonometry and algorithms begin to play their part. I never thought I’d be uttering the words robust quadrilateral but that’s just the rabbit hole that this course is taking me down and I have to admit I’m thoroughly enjoying it.

This week the Technologies module concludes with Security in IoT, HCI (Human Computer Interaction) in an IoT World and Robotics and Autonomous Vehicles.