Edge computing, IoT and AI can be confusing terms individually; combine the three and confusion only grows. Learn three key rules for deploying AI and IoT at the edge of your network.
AI and IoT-Edge computing. IoT. AI.
It’s hard to read a headline lately without at least one of these technologies making an appearance. To say they are overhyped is an understatement. Many enterprises have difficulty deciphering how to plan for deploying one of the three, let alone combining them. Even beside the hype, all three technologies are relatively new.
So, how can an enterprise plan for a deployment of AI and IoT at the edge? The answer is to look at the three technology topics individually, then view them as a unit, to have any chance at successful implementation.
Edge computing is all about reducing latency between where a condition that needs handling emerges and where the handling process takes place, which is sometimes called shortening the control loop.
Cloud data centers are often hundreds or even thousands of miles away from the place where user devices connect, and that can mean a round-trip delay of 100 milliseconds or more. Since this is additive to any processing time in the application, total delay time can amount to as much as a half-second, which is unacceptable in many scenarios — think connected healthcare devices or autonomous vehicles.
Cloud providers know latency can be a problem, which is why they offer on-premises hosting of some or all of their IoT features. Running AI at the edge is also a viable model.
The next place to look for practical strategies is IoT, which doesn’t have much to do with the internet at all, in a practical context. It’s about harnessing the raw information from sensors and facilitating machine control from applications rather than by people. This raw information almost always comes in the form of events or signals that something has happened or as status changes — for example, the temperature of a freezer, the location of an item in the supply chain or the revolution of a conveyer belt in a manufacturing plant.
Cloud providers offer event-processing and IoT tools, including the serverless dynamic hosting of processes on demand. These tools are normally run in the cloud data center and collect events from multiple sources, even from multiple geographic areas. But when combined with edge computing, this cloud event processing can support both low-latency responses to time-critical events and more complex multi-event analysis. Having the same cloud platform at the network edge and deeper in the cloud facilitates development of IoT applications.