Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

Abstract

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