A Whale Optimization (WOA): Meta-Heuristic based energy improvement Clustering in Wireless Sensor Networks

In WSN, clustering is the prevailing technique that involves identifying the object network based on attribute values. It is the sink nodes' responsibility in WSNs toward receive and process the collected data from cluster members. On the subject of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. Genetic algorithm, optimization of particle swarm, differential evolution, whale optimization algorithm, and optimization of the grey wolf is now becoming efficient clustering methods as per the meta-heuristic approach. Evaluation of the life span of the entire network, this paper proposes a whale optimization algorithm. The core objective of WOA-P proposed method is tends to decrease energy consumption and extend the life of the WSNs. The purpose of the objectives has been formulating to reduce power consumption and increase the lifespan of network to achieve these goals. Compared to three recognized optimization methods, the investigational results showed that the planned WOA completed better proficiency towards dropping the total energy consumption: differential evolution, GA, particle swarm algorithm, grey wolf optimization over the network.