Edge computing refers to using computing power near its point of use rather than centralising it through servers and data centers, instead relying on local compute power at its point of delivery instead. This can speed up response times, reduce traffic volumes and cut costs by eliminating the need to send large volumes of information over the internet, which often incurs latency issues and security breaches.
Edge devices typically utilize fanless and ventless enclosures that can withstand dust, dirt, humidity, temperature changes and other harsh environmental conditions, making them ideal for applications requiring reliability and resilience in harsh environments that could compromise production or endanger workers - such as factories with factory robots and mining companies using remote rigs.
Cloud and edge computing are often combined in order to deliver big data analysis in real time, which is particularly popular in industries like financial services where traders must make decisions based on events as they happen in real time, as any delays could cost the business considerable losses.
Edge computing enables data processing nearer where it's collected, cutting down the amount of information sent backhaul and saving valuable bandwidth costs - both factors which contribute significantly to IoT system costs.
Edge computing also boasts the advantage of increasing IoT device reliability in environments with poor connectivity or limited bandwidth, such as oil rigs, ships at sea or remote villages without proper internet access.
Edge devices can process data locally, saving it for transmission when connectivity becomes available - this feature can be particularly helpful in smart homes where sensors collect information around the house before being sent directly to a centralised server.
Content Delivery Networks (CDNs), used for content distribution to the edge, enable faster and lower latency content delivery - useful for video streaming and music delivery services, for instance.
Edge computing can also be utilized for traffic management within cities, where data collected by smart city infrastructure can be utilized to help regulate traffic flow and optimize bus or train frequency, including adapting bus schedules in response to changing demand or managing extra lanes as necessary.
Edge devices can also be used to run machine learning models on big data sources, helping businesses gain greater insights. This may allow a factory to predict which of its robots is likely to fail so that action can be taken immediately to prevent failure from occurring.
Increase productivity and ensure workplace equipment remains up-to-date to reduce production mistakes that disrupt production or even put workers' safety at risk. Wearable devices and other medical devices that collect vast amounts of patient data can also be processed near real time to assist doctors and nurses in providing faster treatment to sick patients.
