As the increased number of endpoints in the Internet of Things (IoT) technology, consolidating and processing the data in a single data center is critical. Edge computing brings the computing platform close to the data source, which improves the performance and speed of data without sending the data to the centralized systems. It is an extension of the self-healing network, remote cloud services, distributed data, and peer-to-peer networking. Edge computing pushes the communication capabilities, processing power, and intelligence of an edge gateway into the devices.

With the increasing quantity of data generated from the IoT devices, transmitting the data at rapid speed becomes the bottleneck in the cloud computing environment. Edge computing needs to satisfy the requirements of high resilience and low latency. For example, in the smart transportation system, edge computing responds through the wireless Internet rather than waiting for the response from the cloud. Moreover, edge computing plays a vital role in adjusting the suitable resolution of the images or videos at the edge before uploading to the remote server.

Nowadays, different applications use the edge computing technology to speed up the processing. Augmented and virtual reality, self-driving cars, and smart logistics, video surveillance, smart manufacturing, smart environment monitoring, smart healthcare, and smart homes are few potential applications of the edge computing.

I think it is better to apply distributed parallel processing approaches. This is can be done by subdividing the computation power into clustered load groups that will be processed by processors clusters.

Dear Antharaju Chakravarthy,

I would suggest you to investigate edge computing, fog computing, and Cloudlet behaviors in smart factory. In this case, you can suggest the best computing platform for this factory generation to industry managers.