The lighting industry has become comfortable with the idea of internet connected (IoT) lighting that contains a computer chip in a luminaire for control, storing data, and communications. Buckle up, because “the edge” (computing within IoT devices) is about to get more complex with the addition of artificial intelligence (AI) computer chips at the edge. In fact, one leading IoT blogger, Stacey Higginbotham (aka Stacey on IoT) has dubbed the new trend, “The Complex Edge.”
The Complex Edge will require a system of computer chips to apply AI decision making (algorithms) within lighting systems. Higginbotham gives the following commercial lighting example in a recent blog post about the Complex Edge:
“Consider, for example, a connected lighting system designed for offices. A gateway computer for managing the system might need direct access to lux sensors or even air quality sensors, as well as specialized chips inside to run algorithms associated with building management. This is a workload or computing use case that may not become as ubiquitous as a smartphone, but it is something that will be needed in millions of offices around the world. Particle is trying to provide a more customizable, off-the-shelf option so the lighting company won’t need to hire a host of chip and connectivity engineers to build their product.”
The logical extension of the lighting example above, is that luminaires may end up with systems of different types of computer chips to perform traditional LLLC+NLC tasks, but now also apply AI at the luminaire level. I’m not personally aware of a luminaire with this capability…..yet. If you know of one, please share the manufacturer and the product in the comments below.
Complex devices, such as cars, have driven (pun intended) adoption of the Complex Edge, over the past few years. You may be skeptical because networked lighting controls have very low adoption rates, to-date, which is true. However, there is good reason to believe that rapid electrification will stress the electric grid, spike electricity prices, as well as the cost to increase building electrical infrastructure required for electrification. These grid stresses are creating several new forms of ROI for much deeper energy efficiency retrofits, including advanced lighting controls. For more on the impacts of rapid electrification, read my recent 7-part Electrification Impacts On Lighting posts (1, 2, 3, 4, 5, 6, 7).
Higginbotham has also written about the need for sensors to get smarter and process more information at the edge, providing another driver for the Complex Edge. Higginbotham gives additional examples:
“Depending on the models running and the use case, we might see a single hop from a sensor to a larger, local computer, or we might see multiple sensors feeding into a gateway device for some pre-processing and then even more computing happening later.”
“But when it comes to us bringing computing, connectivity, and specifically AI to more devices, it’s clear that the way we think about semiconductors and our models for computing have to change. When we talk about the complex edge, we’re really talking about a huge variety of chips that will each have different optimizations based on their position at the edge, their job, and their connectivity. Then we have to figure out how to scale these complex edge systems. And write software for them. And manage them.”
Higginbotham’s article on the Complex Edge is available here.