Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Abstract

In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

“…However, its effectiveness may be limited by the need for accurate guidance mechanisms and the potential vulnerability to sophisticated security threats. Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Networks (16) aims to optimize energy consumption and enhance routing efficiency in sensor networks. While this approach offers potential benefits in prolonging network lifetime and improving energy efficiency, it may face challenges in balancing multiple conflicting objectives and adapting to dynamic network conditions.…”