Edge computing is growing in popularity and is already used in a wide of variety of industries and applications. Its integration is only going to increase in the future, so it is a good idea to understand exactly what it is and how it works.

One obstacle to overcome is that it manifests in many ways, thus there is no one single example that encapsulates everything that edge computing is or does. However, at its core, it is a simple concept, and we can break it down with a bicycle wheel analogy.

Bicycle Wheel Analogy of Edge Computing

Edge computing can be simply explained to the layman by visualizing a bicycle wheel, with the central hub being the cloud and the outer tire being the edge, which represents the local networks that share and process data via the cloud. The spokes in between the cloud and the edge are the communication channels through which the cloud communicates with the local networks, and through which most information travels in order to be processed.

Instead of transferring data from the local networks on the edge to the cloud to be processed and then sent back, the data is processed much closer to the local networks. So ‘edge computing’ means data collection and analysis happening closer to the network where it is generated without transferring the data back and forth from the cloud.

This makes data analysis processes much faster and more efficient. It means this localized data analysis can happen in real-time, increasing productivity by enabling a faster decision-making process.

Case Examples

Edge computing is used in smart grids to help businesses and organizations better manage their energy usage by using devices to monitor and analyze energy consumption in real-time. Smart homes can also use edge computing to collect and process data from around the house. There are a variety of home applications, which all involve eliminating the need to send and store data to a cloud-based system. For example, voice assistant systems such as Amazon’s Alexa can respond much faster when operated with edge computing.

Traffic management also benefits as it removes the need to transfer huge volumes of traffic data to the cloud for analysis, significantly reducing the bandwidth usage and the amount of latency that affects decision-making times.

Manufacturing plants also use edge computing to locally analyze data from their production lines. This means they will be able to detect changes faster and act before a failure occurs. This is especially useful in industries such as oil and gas production, as failures here can be catastrophic. Such plants are usually in remote locations as well, so sending data back and forth from the cloud takes even more time than usual.

There are many other applications, but as you can see from the above examples, the common factor is analyzing gathered data locally so it can be processed much faster.

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