AI and mobile edge computing help unlock the full potential of the Industrial Internet of Things
While data-gathering is the first step, the sheer amount of data generated by machines can quickly become overwhelming. Given that the Internet of Things (IoT) will be generating a whopping 80 zettabytes of data by 2025, you need a way of sifting through all of it and separating the signal from the noise. This is an example of what artificial intelligence (AI) can do. Machine learning (ML) algorithms, a subset of AI, can be developed to look for problems based on past history and flag them. AI and ML help make sense of the data that IIoT delivers in an automated way to help organizations make better business decisions.
Traditionally, data garnered from machines had to be analyzed somewhere. AI and ML applications, depending on the use case and desired outcome, could be deployed on-site, on an edge or in the cloud. Manufacturers that leverage AI and ML as part of their digitization and automation strategy have to take in consideration the requirements (like application performance and latency) that need to be in place for data-driven automation to be successful. After all, what's the point of gathering information about machines in real-time if you can't act on it quickly?
Edge computing processes the data close to the source, helping to decrease response times for applications. Mobile edge computing (MEC) drives efficiencies even further by using 5G. 5G can enable massive sensorization, an order of magnitude higher than what's possible with today's cellular technologies, paving the way for large-scale IIoT deployments. The 5G architecture inherently supports lower latencies and faster response times, natively amplifying the benefits of an edge computing solution.
MEC is not meant to replace cloud computing but introduce a hybrid option where the cloud works on heavy-duty computation for long-term insights, leaving on-premises edge computing for real-time analysis. Mobile edge computing also enables devices that don't just harvest data but also act on it at the edge.