What does Edge Computing mean?

A look into the near future already reveals how IoT and AI will develop in the coming years. In 2024, more than 20 billion IoT devices will already be connected. In the coming years, connectivity in the IoT will increase strongly. The consequence is high data flows that have to be processed in ever shorter periods of time.

A case study of self-driving cars communicating with each other illustrates the need for fast and efficient data processing. The idea is simple: all vehicles on the road are linked and able to communicate with each other in order to avoid traffic jams, for example. However, the amount of data cannot be processed by the cloud within a reasonable period of time due to latency times. Even with AI-based autopilots, processing with the cloud would not be feasible.

This is where edge computing comes into play.

Edge computing is a design approach for IoT architectures in which computing power and storage capacities are placed as close as possible to the corresponding end devices. The term “edge” refers to the outer edge (i.e. the connected devices) of an IoT network. The proximity of resources means that data does not have to be loaded into the cloud via external networks and latency times are eliminated.

Thanks to the use of edge computing, IoT solutions can also be equipped for computing-intensive situations. Edge computing can be used in parallel with the cloud. This means that the advantages of cloud computing are retained and supplemented by the use of edge computing.

The key benefits of edge computing include

• Faster response times: By performing data processing at the edge of the network, closer to where the data originates, latency times are reduced. This is particularly important for applications that require real-time or near-real-time data processing, such as in factory automation or autonomous vehicles.
• Reduced bandwidth costs: Local data processing means that not all data has to be transferred to the central server, which reduces the required bandwidth and therefore saves costs.
• Increased security: Edge computing can increase security as sensitive data can be processed and stored locally instead of being sent over the network. This reduces the risk of data leaks during transmission.
• Reliability: Decentralised processing reduces dependency on central servers, which improves system reliability in the event of failures.
• Scalability: Edge computing makes it possible to scale resources efficiently by providing processing capacity where it is needed.
• Lower energy consumption: As data processing takes place locally, this can lead to lower energy consumption compared to transmitting large amounts of data over long distances.
• Support for the Internet of Things (IoT): Edge computing is ideal for IoT applications where devices at the edge of the network generate large amounts of data that need to be processed quickly.

Overall, edge computing enables organisations to improve the efficiency of their networks and applications and is particularly important for applications that require fast processing and response times.