Edge computing is a networking concept that seeks to get computation as close to the database as possible to reduce latency and bandwidth utilization. Simply put, edge computing involves running fewer cloud operations and transferring those operations to local sites, such as on a user’s phone, an IoT system, or an edge server. Bringing computation to the edge of the network minimizes the amount of long-distance communication between a client and a server that must occur.
Imagine a safe house with dozens of high-definition IoT video cameras. Those are ‘dumb’ cameras that essentially emit a raw video signal and send it continuously to a cloud server. On the cloud platform, a motion-detection program takes the video output from all the cameras to guarantee that only clips of action are transferred to the file archive. It ensures there is a persistent and substantial burden on the building’s Internet connectivity, as a large amount of video content being transmitted absorbs considerable bandwidth. Besides, there is a very heavy load on the cloud storage which will concurrently process the video footage from all the cameras.
Consider now that the processing for the motion sensor is pushed to the edge of the network. What if each camera used its internal computer to run the application for motion detection, and then submitted footage as needed to the cloud server? That will result in a substantial decrease in the usage of bandwidth, as most of the video footage would never have to move to the cloud server. The cloud service will now only be responsible for maintaining the relevant video, ensuring the system could connect with a greater range of cameras without overloading. And it feels like edge computing.The cost reduction alone will be a catalyst for several businesses to implement an edge-computing architecture. Companies who adopted the cloud for many of their applications may have noticed that bandwidth costs were higher than anticipated.
However, the main advantage of edge computing is potentially the potential to process and store data more efficiently, making for more effective real-time applications that are vital to businesses. A smartphone scanning a person’s face for facial recognition will need to run the facial recognition algorithm via a cloud-based database before edge computing which will take a lot of time to process.
For an edge computing model, considering the capacity of smartphones, the algorithm may be operated locally on an edge server or gateway, or even on the smartphone itself. Applications like virtual and augmented reality smart cities and even construction-automation systems need fast processing.Worldwide, 5 G broadband systems are introduced by networks that offer the advantages of broad speed and reduced latency for devices, enabling businesses to switch from a garden hose to a firehose for their network bandwidth. Instead of merely providing quicker speeds and advising companies to start storing data in the cloud, several providers are focusing on cutting-edge computing approaches in their 5 G implementations to provide quicker real-time processing, especially for mobile devices, connected cars, and self-driving cars.
It’s obvious that while the original aim for edge computing was to minimize IoT system latency costs over long distances, the proliferation of real-time apps needing local processing and storage resources would push the technology forward in the coming years.
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