The name edge computing signifies the corner or edge in a network diagram at which traffic enters or exits the network.
Edge computing pushes computing power to the edges of a network, so instead of devices like drones or smart traffic lights needing to call home for instructions or data analysis, they can perform analytics themselves on streaming data and communicate with other devices to accomplish tasks.
In edge computing, the big data analytics happens very close to the IoT devices and sensors. Edge computing thus can also speed up the analysis process, allowing decision makers to take action on insights faster than before.
For organizations, this offers significant benefits. They have fewer data sent over their networks, which can improve performance and save on cloud computing costs. It allows organizations to discard IoT data that is only valuable for a limited amount of time, reducing storage and infrastructure costs. Further edge computing improves time to action and reduces response time down to milliseconds, while also conserving network resources.
In Industrial Internet of Things, applications such as power production, smart traffic lights, or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimize production, and prevent product defects.
Coca-Cola Freestyle dispensers are using edge computing to quickly understand consumer behavior and help to be more responsive to needs.
GE locomotives take advantage of edge computing by gathering and processing real-time data about railway conditions, train maintenance, and even crew morale to help railroad companies move trains through crowded railway corridors in as safe and efficient a manner as possible.
With Digital Transformation and emerging technologies that will enable “smart” everything – cities, agriculture, cars, health, etc – in the future require the massive deployment of Internet of Things (IoT) sensors while edge computing will drive the implementations.