Here’s a couple of edge computing examples to help bring the concept of edge computing to life:
With autonomous automobiles—essentially a datacenter on wheels—edge computing plays a dominant role. GE Digital partner, Intel, estimates that autonomous cars, with hundreds of on-vehicle sensors, will generate 40TB of data for every eight hours of driving. That’s a lot of data. It is unsafe, unnecessary, and impractical to send all that data to the cloud.
It’s unsafe because the sensing, thinking, and acting attributes of edge computing in this use case must be done in real-time with ultra-low latency to ensure safe operation for passengers and the public. An autonomous car sending data to the cloud for analysis and decision-making as it traverses city streets and highways would prove catastrophic. For example, consider a child chasing a ball into the street in front of an oncoming autonomous car. In this scenario, low latency is required for decision and subsequent actuation (the car needs to brake NOW!).
It’s unnecessary to send all that data to the cloud because this particular set of data has only short-term value (a particular ball, a particular child on a collision with a particular car). Speed of actuation on that data is paramount. It’s simply impractical (not to mention cost-prohibitive) to transport vast volumes of data generated from machines to the cloud.
However, the cloud is still an important part of IIoT equation. The simple fact that the car had to respond to such an immediate and specific event might be valuable data when aggregated into a digital twin, and compared with the performance of other cars of its class.
In a scenario where a company has a fleet (think trucking company, for example), the main goal could be to ingest, aggregate, and send data from multiple operational data points (think wheels, brakes, battery, electrical) to the cloud. The cloud performs analytics to monitor the health of key operational components. A fleet manager utilizes a fleet management solution to proactively service the vehicle to maximize uptime and lower cost. The operator can track KPIs such as cost over time by part, and/or the average cost of a given truck model over time. This in turn helps maintain optimal performance at a lower cost and higher safety.