Edge computing enables remote devices to process data at the network’s “edge,” either on-board or through a local server. Also, only the most crucial data is transported to the central datacenter when processing of data is required, reducing latency.
Why is edge computing used in businesses?
Edge computing helps businesses gain deeper, more timely insights from device data and speed up the response times of their remote devices. Edge computing eliminates bottlenecks on the networks and datacenters that serve edge devices and enables real-time computation in places where it would not otherwise be possible.
Without edge computing, the enormous amount of data that edge devices generate would overload the majority of today’s commercial networks, impairing all network functions. The price of IT may rise. Customers who are not satisfied might shop elsewhere. Valuable equipment may suffer damage or just perform less well. Most importantly, the security of employees may be jeopardized in fields where intelligent sensors are used to keep them safe.
How is edge computing implemented?
To make real-time functionality possible for smart apps and IoT sensors, edge computing solves three interrelated challenges:
- Connecting a device to a network from a remote location.
- Slow data processing due to network or computing limitations.
- Edge devices causing network bandwidth issues.
Advancements in networking technologies, like 5G wireless, have made it possible to solve these challenges on a global, commercial scale. 5G networks can handle vast amounts of data—going to and from devices and datacenters—in near-real time. (There’s even a wireless network that uses cryptocurrency to encourage users to extend coverage to harder-to-reach areas.)
But advances in wireless technology are only part of the solution for making work at scale. Being selective about which data to include and exclude in data streams over networks is also critical to reducing latency and delivering real-time results.
An example of edge computing:
A security camera in a remote warehouse uses AI to identify suspicious activity and only sends that specific data to the main datacenter for immediate processing. So, rather than the camera burdening the network 24 hours per day by constantly transmitting all of its footage, it only sends relevant video clips. This frees up the company’s network bandwidth and compute processing resources for other uses.
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