Edge computing in the context of IIoT brings compute closer to the origin of data.
Without the edge computing layer, the data acquired from assets and sensors connected to the machines and devices will be sent to a remote data center or the public/private cloud.
This may result in increased latency, poor data locality, and increased bandwidth costs.
Here’s an interesting article on edge computing.
Edge computing is an intermediary between the devices and the cloud or data center. It applies business logic to the data ingested by devices while providing analytics in real-time. It acts as a conduit between the origin of the data and the cloud, which dramatically reduces the latency that may occur due to the roundtrip to the cloud. Since the edge can process and filter the data that needs to be sent to the cloud, it also reduces the bandwidth cost. Finally, edge computing will help organizations with data locality and sovereignty through local processing and storage.
The London “festival of A.I. and emerging technology” that takes place each June.
This year, due to Covid-19, the event took place completely online.
(For more about how CogX pulled that off, look here.)
One of the sessions veered towards privacy.
One of the most interesting sessions I tuned into was on privacy-preserving machine learning. This is becoming a hot topic, particularly in healthcare, and especially now due to the interest in applying machine learning to healthcare records that the coronavirus pandemic is helping to accelerate.