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How To Architect for The Internet of Things (IoT)

This article has been updated.

While this podcast is not a recent one, interestingly enough, the architecture question is still very relevant and useful to understand how Savi products are engineered to handle massive data ingest.

Jim Haughwout had the pleasure of doing a podcast with Forbes-contributor Mike Kavis on how to architect for the Internet of Things (“IoT”). They originally connected on Twitter regarding a discussion on whether the IoT and sensors are Big Data. That discussion led to 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 is an excerpt of what they 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 acknowledgment). 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 sensors in and of itself are 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:

Savi would like to extend a big thank you to the folks at Cloud Technology PartnersSYS-CON Media, and Cloud Computing Journal for sharing this podcast. We hope all readers will do the same!