H-Gwmfo: Efficient Energy Aware Clustering For Wsn Using Hybrid Grey Wolf Based Multi-Objective Forest Optimization

Abstract

WSNs face issues like energy constraints, limited memory, and computation time.  The clustering process helps in reduction of energy consumption. Clustering faces several challenges in the Cluster head selection and the network’s optimal data routing. So, this paper presents an efficient clustering approach using hybrid Grey Wolf Optimization based multi-objective forest optimization algorithm (H-GWMFO) approach to shorten the distance at which sensor nodes in wireless sensor networks transmit data, which offers energy-efficient clustering and the best data routing to increase the longevity of the network. The cluster head selection was performed by the grey wolf Optimization, followed by the Multi objective Forest optimization that enhanced the CH on the basis of Energy, Euclidian distance, trust, and delay of the sensor Nodes. When compared to the well-known cluster-based protocol created for WSNs, like WOA, GWO-DNN, RF, and the K-means with GWO methods in MATLAB for Delay, Energy consumption, Packet Delivery Ratio, Throughput and Network Lifetime of the proposed protocol's performance is evaluated. Simulation results demonstrated that our proposed H-GWMFO achieved a higher network lifetime that existed for 253 seconds and higher throughput of 303.1123, proving it performed better than the existing systems