Distance is relative. For an industrial robot on the factory floor, the distance between it and the nearest compute resource is critical.
Latency between the robot’s hardware and the software that tells it what to do can cause product defects or worse. For example, if a robotic paint sprayer’s nozzle is a bit clogged it might miss a spot on the car it is painting. If that information is not analyzed and acted upon immediately, then the next car – and the next – will have faulty paint jobs until the fault is corrected.
In most factories, compute power resources are limited because – for cybersecurity reasons – they cut off cloud access during production (more about this in another post). That means that the compute power must reside as close to the performing robot as possible. And the communications between the device and the compute resource must have very low latency.
The edge is closer
This is where edge computing comes in. With edge computing, resources are shifted from the cloud to the place where the data is generated. The edge computing device acts as a hub that (pre-)processes data and enables cross-communication between the actual end-device and the cloud, distributing the computing load in the process.
The reason 5G comes into play is because it is an enabler of edge computing as standard and natively enhances it by routing data traffic and the provision of edge services with various 5G functions. It is also multiple times faster than 4G and ultra-secure.
The number of potential use cases is endless, ranging from to the industrial Internet of Things, to smart home applications. Wherever there is an increase in the amount of data that needs to be processed, and a significant time factor comes into play, 5G edge computing can be the solution of the future.
But we are not there yet; demand for 5G edge computing solutions is still suffering from the slow overall expansion of 5G applications, as it requires not only relevant use cases, but also a corresponding spread of 5G devices.
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