Energy-Efficient Load Balancing Strategy for Wireless Sensor Networks using Quasi-oppositional based Jaya Optimization

The area of wireless sensor networks (WSNs) has gained significant attention from researchers due to its expansive range of applications, such as industrial regulation, human detection, and medical diagnosis. WSN is a vast number of geographically scattered devices that use sensor nodes to communicate and gather information from the target region. In clustered WSNs, the cluster head (CH) is deliberated as the relay node at a higher energy level than the non-CH nodes. The relay nodes perform more tasks related to the non-CHs, and these relay nodes are restricted by energy and communication capacity. Therefore, balancing the load of the relay node is a significant concern for improving the performance of the WSNs. Clustering is a well-known technique enforced to balance the load of the relay nodes. Often, densely loaded relay nodes dissolve their energy in less time and may cause changes in the topology of the network. In this study, we propose a Quasi-oppositional based Jaya load balancing strategy (QOJ-LBS) with a novel fitness function to address the issue of load balancing. The novel fitness function derived from the convex combination of the least lifespan of the relay node across the network and the entropy value of the lifespan of all relay nodes. The proposed QOJ-LBS justifies the network performance under two different WSN conditions called scenario-1 and scenario-2 with single-hop and multi-hop routing. The experimental analysis shows that improvement in network lifespan, total energy utilization, and the number of active sensor nodes of WSN is statistically significant in proposed QOJ-LBS compared to other state-of-the-art algorithms.