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For decades, from even before we called everything the IoT (Internet of Things), maintenance has been the barrier to digital sensing and operating of the physical world. Wired sensors were reliable but expensive to install, and often an esthetic nightmare once installed. With self-power, sensors became cheap enough to put everywhere, but faced a new challenge—maintenance.

For a long time, deployments were limited by battery life. Many initiatives were short-lived, running until the batteries wore out. Changing the batteries was expensive, sometimes more than the initial installation. Committed organizations developed scheduled battery changes to control costs, just as they had done before for re-lamping projects.

We solved that problem by making sensors so cheap we could just leave them and install replacements. Or we (notably members of the EnOcean Alliance) tuned communications to be so light-weight that in situ energy harvesting could keep systems working.

Today’s sensors have become smarter, sometimes referred to by the indeterminate name “edge devices.” Sensors and Edge Devices likely transmitted more than 20 zettabytes of data for central storage last year, although there are no firm estimates on 2017 data gathering. With that much data being stored, the communications requirement was easily in yottabytes.

This much data creates a new challenge. The IoT not only requires that we get actionable information that matters, but that we get it before it is too late to matter. There is too much data and too many situations to rely on timely central decisions.

To enable drinking this firehose of data, we are starting to rely on sips at the edge. Edge Devices are making the initial decisions as to what data means, and what data needs to be brought into the middle. Local decisions are made faster and more accurately locally, without interference from temporary higher priorities in the cloud. For all but the simplest scenarios, this model requires learning at the edges. There are large now open source libraries now of Artificial Intelligence (AI) code for Raspberry Pi and Arduino.

This presents a new maintenance problem, managing and updating AI routines and algorithms.

The big software companies are preparing the tools we will need. Thousands of AI systems in each building will require tools to manage the rapid evolution algorithms. New algorithms will require managed roll-outs Rapid evolution forces diversity of algorithm and information as systems will change far faster than their installed life. Oracle is pushing GraphPipe, an open source software project for efficiently deploying and managing AI models at scale. Microsoft is right there with them, with large platform management announcements expected this Fall.

The problem of managing intelligence in millions of devices is solved already before most people know they have the problem.

In the last year, Pi architecture devices have blown right past the $40 and even $20 price points, with full systems expected for $7 and perhaps $4. Arduino platforms not only run open source Linux and Android but with open source hardware offer potential easy integration directly onto integrated specialty hardware components.

The barriers to fully intelligent small systems across every aspect of buildings are falling even faster than pioneers such as Alper Üzmezler and the Project Sandstar for smart controls have publicly projected. It is my personal belief that while full platforms such as the Pi have higher initial costs, in part because they include a GPU not needed to manage a display, that this means they are pre-adapted for high-speed signal processing. This will not play out slowly.

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