Electrical Engineering

Finding simplicity in the complexity of the Internet of Things

The Solid Conference – Hardware, Software & the Internet of Things (solidcon.com), recently held in San Francisco, is touted as a revolutionary conference that goes beyond the Internet of Things.

What lies beyond the Internet of Things (IoT) is pretty mind stretching and includes wetware (biological hardware) using BPUs (biological processing units) and cells that can execute Boolean logic. Mostly due to my introduction to wetware, the conference was a bit of an emotional roller coaster for me. One moment I felt competent, current and relevant, the next I felt like a dinosaur. Synthetic biology is not my current reality, nonetheless, I was left wondering if the creation of the IoT is simple or if it is exceedingly complex.

I feel as though I’ve been involved with the IoT (although it wasn’t called that) for my entire career of nearly 17 years. At its core, it is hardware and software coming together to interact with people and other machines. However, the main difference now is that previous products I’ve developed were closed designs with well-defined (mostly) functions and purpose and a clear line of sight to a revenue stream. The IoT is necessarily open, not well defined, required functionality is not obvious, and extracting value from the “things” purpose is often secondary to its connectedness.

Searching the web for “IoT” and “simple” yields many results. The results are found in hardware, software, platforms and protocols. Stabilized hardware and standardized software are making connected device development simpler. Open hardware is readily available and open-source software libraries continue to grow. The tools for development have also improved considerably over the past five or so years with the expansion of 3D printing and open-source development environments. So, in this sense, the IoT is simple.

But when you dig deeper you find the complexity in the solutions being offered. The hardware ecosystems are far better than they once were, but each one has nuance and requires experience to be efficient. There is a sea of software platforms and protocols to choose from. The software platforms and protocols promise a lot, but still require ramp up and a lot of time in verification and debug.

It's easier to create a proof-of-concept than it has ever been. The challenge lies in ensuring you are learning what you need to learn from it.

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