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How to Architect for IoT

Last week I had the pleasure of doing a podcast with Forbe-contributor Mike Kavis on how to architect for the Internet of Things (“IoT”). We originally connected on Twitter regarding a discussion on whether the IoT and sensors are Big Data. That discussion led a podcast on architecture challenges–from device to data to data consumer–created by the onset of millions (or billions) of connected sensors and smart things.

Here in an excerpt of what we discussed

  • Connected devices bring back some classic engineering challenges back into the forefront.  How do you transmit data securely and with low power consumption? How do you handle lossy networks and cut-off transmissions?
  • Not everything is smartphone app transmitting JSON over HTTP (that would be cost prohibitive from both a hardware and bandwidth perspective). How do you handle communication myriad protocols, each of which could be using a near-infinite variety of data encoding formats?
  • IoT data is messy. Devices get cut off in mid-transition (or repeat over and over until they get an acknowledgement). How do you detect this–and clean it up–as data arrives?
  • IoT data is of incredibly high volume. By 2020, we will have 4x more sensor and IoT data than enterprise data. We already get more data today from sensors than we do from PCs. How do we scale to consume and use this. In addition, connected devices are not always smart or fault-tolerant. How do you ensure you are always ready to catch all that data (i.e., you need a zero-downtime IoT utility)
  • IoT and sensor and of itself is not terribly useful. It is rarely in a format that a (business or consumer) analyst would even be able to read. It would be incredibly wasteful to store all this as-is in a business warehouse, DropBox repo, etc.
  • IoT and sensor data needs context. Knowing device Knowing that FE80:0000:0000:0000:0202:B3FF:FE1E:8329 is at GPS location X,Y is of no use. You need to marry it to data about the “things” to get useful insights.
  • IoT data simultaneously “lives” in two points of view: what does this mean right now and what does this imply for the big picture. The Lambda Architecture is an ideal tool to handle this.
  • Finally, while all the attention is on the consumer stories, the real money is the Industrial and Enterprise Internet of Things. It’s also where smart things are far less creepy.

Listen to the podcast to hear more of the details

You can find the full podcast on Cloud Technology Partner’s website and SoundCloud:

I also want to take a moment to extend a big thank you to the folks at Cloud Technology Partners, SYS-CON Media, and Cloud Computing Journal for sharing this podcast.  I also want thanks to all of you on Twitter who retweeted it. I was happily overwhelmed by the sharing and interest!

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My MIT EF Podcast: IoT as Internet 3.0

Last week, I had the pleasure of doing a podcast with Randall Cronk of the MIT Enterprise Forum (my alma mater) on the practicalities and challenges of using the Internet of Things (a.k.a. IoT) to solve real-world problems.

Here in an excerpt of some of the things (no pun intended) we discussed:

IoT is not just about talking toasters (or creepy monitoring), it can be use to solve many high-cost, real-world problems. We already have some clear analogies for this:

  • Commercialization of the World Wide Web (Internet 1.0) radically changed how we get information. Instead of waiting to get it physically (via mail or newspapers) we could get it instantly from our desktops
  • The mobile Internet, smartphones, mobile web and app stores (Web 2.0 or Internet 2.0). Let us take the convenience of this instantaneous access virtually anywhere. We no longer had to go back to our desks and could now look up info on street corner at a restaurant, etc.
  • The Internet of Things (Internet 3.0) takes this convenience to the next level. We no longer have to go look at things to see where they are, what state they are in, etc. We can find out without manual effort. This lets us focus on things we really care about (instead of the drudgery of getting information)

Of course, this is not a simple prospect. We have many challenges to solve. The most obvious are the ones around data connectivity and protocols (these challenges, however, are pretty straightforward). The next is privacy and security (we have models for these from regulated industries like banking, healthcare, and medicine). The next is how to handle all that information. If we do not solve this problem, connected things will swarm us with so much useless data that it will make our email inboxes look simple.

Listen to the podcast to hear more of the details

You can find it at the MIT Enterprise Forum:

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or on iTunes:

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