Edge computing handles the data sharing activities and operations at the source node of an IIoT without necessitating a centralized server. The edge computing of IIoT brings the computing closer to the edge that generates the network data optimizes the devices, equipments, and applications. Generally, the IoT devices are tiny in size and heterogeneous in bandwidth, battery power, and storage.

   Applying heavy computing on those devices leads to quick network failures, high latencies and reduces the packet delivery ratio. Hence, efficient edge computing is crucial to optimize the performance of IIoT. The edge computing model considers the source of an IIoT as an edge rich in storage and computing capabilities. By running the network operations at edge devices, edge computing reduces the delay in packet delivery, storage issues and quickly makes the decisions within milliseconds. The advantages of using edge computing in IIoT are maximum speed, minimum delay, low operational cost, improved security, high privacy, high reliability, and enhanced scalability.
   The IIoT connects various industrial equipment through a wireless connection. It consists of data acquisition, sharing, and analysis systems, but they increase costs and reduce productivity. In order to reduce the decision-making latency and ensure data privacy, the IIoT implements the edge computing concept. It is a distributed computing paradigm and offers additional computation and data storage. In order to utilize the entire advantages of edge computing in IIoT, load balancing and data offloading, and data sharing security have to be ensured.