What is edge computing?

In this blog, I will explain the concept of edge computing,

Edge computing is a distributed computing infrastructure that enables IoT devices to quickly process and perform operations on data at network edges.

Edge cloud computing explained

Edge computing enables devices in remote locations to process data at the “edge” of the network, either through the device or a local server. When data needs to be processed in the central data center, only the most important data is transmitted, thus minimizing latency.

Why do businesses use edge computing?

Businesses use edge computing to improve the response time of their remote devices and gain richer, more timely insights from device data. Edge computing enables real-time computing in otherwise inconvenient locations and reduces performance issues in networks and data centers that support edge devices.

Without edge computing, the massive amounts of data generated by edge devices would overwhelm many of today's business networks and disrupt all operations on the affected network. IT costs could skyrocket. Dissatisfied customers could take their business elsewhere. Valuable machinery could be damaged or become less productive. But most importantly, the safety of workers could be compromised in industries that rely on smart sensors to keep workers safe.

How does edge computing work?

Edge computing solves three interrelated problems to enable real-time functionality for smart applications and IoT sensors:

*Connecting a device in a remote location to the network.

*Slow data processing due to network or computing limitations.

*End devices causing network bandwidth issues.

Advances in network technologies such as 5G wireless have made it possible to solve these challenges on a global and commercial scale. 5G networks can process large amounts of data to and from devices and data centers in near real-time. (There's even a wireless network that uses cryptocurrency to incentivize users to extend coverage to hard-to-reach areas.)

But advances in wireless technology are only part of the solution to making edge computing work at scale. Being selective about what data to include and exclude in data streams over networks is also critical to reducing latency and delivering real-time results.

Edge computing example:

A security camera in the remote warehouse uses artificial intelligence to identify suspicious activity and sends that specific data to the main data center for immediate processing. So, instead of burdening the network 24 hours a day by continuously transmitting all its footage, the camera only sends relevant video clips. This frees up the company's network bandwidth and transaction processing resources for other uses.

Other edge cloud computing use cases:

*A retail store 1,000 miles away from the company's primary data center uses wireless point-of-sale devices to process payments instantly.

*A mid-ocean oil rig uses IoT sensors and artificial intelligence to quickly detect equipment failures before they worsen.

*An irrigation system in a remote farm field adjusts the amount of water it uses in real time by sensing the moisture level of the soil.