Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, IoT device used compute received WSN data from devices transfer it remote locations for assistance. general, WSNs, gateways a long distance base station (BS) communicated through nearer BS. At gateway, closer BS, energy drains faster because heavy load, issues around Since sensors battery-operated, either replacement or recharging those node batteries not possible after deployed their corresponding areas. that situation, plays vital role in survival. Concerning reducing network consumption increasing lifetime, paper proposed an efficient cluster head selection using Improved Social spider Optimization with Rough Set (ISSRS) routing path reduce load Grey wolf optimization (IGWO) approach. (i) Using ISSRS, initial clusters formed local nodes, chosen. (ii) Load balancing IGWO. The simulation results prove optimization-based approaches efficiently compared existing systems terms efficiency, packet delivery ratio, throughput, loss percentage.