A Hybrid Meta-Heuristic Approach-Aided Optimal Cluster Head Selection and Multi-Objective Derivation for Energy Efficient Routing Protocol in Wireless Sensor Network

Abstract

Wireless Sensor Networks (WSN) are utilized mostly for the collection of data, specifically to perform complex schemes. Thus, the issues of sensor networks and mission-critical sensors are the implementation of Energy Efficiency (EE) routing protocols. Thus, the EE routing protocol in the WSN model is developed in this work to improve the lifespan of the network for the WSN. The Fuzzy C-Means (FCM) clustering is performed for generating cluster groups and here the CHs are optimized using the Best and Worst Fitness of Sailfish Whale Optimization (BWF-SWO). To further evaluate the efficacy of the network, the fitness function is considered by Intra and Inter-cluster Distance and Residual Energy. To determine the efficiency of the routing process, diverse constraints like shortest path distance, throughput, energy consumption, hop count, latency, and Packet Delivery Ratio (PDR) are considered. In the end, the performance is calculated using divergent parameters and contrasted against existing methodologies. From the results, the energy consumption of the implemented EE protocol in WSN is minimized by 55% of RPO, 10% of COA, 20% of SFO, and 50% of WOA appropriately when the node count is 100. Thus, the findings explored that the proposed protocol achieved enriched outcomes on energy-efficient routing in the WSN model.