Hybrid Salp Swarm and Improved Whale Optimization Algorithm-based clustering scheme for improving network lifespan in wireless sensor networks

Wireless sensor networks (WSNs) represent the collection of restricted energy sensor nodes that are deployed in an area of target for gathering potential environment data for decision-making with respect to their objective of application. However, the implementation of energy-effective data gathering strategies in large-scale WSNs is the most challenging due to the limited energy resources. Clustering-based data gathering strategies are identified to be quite effective for energy saving that directly attributes to extended network lifetime. Moreover, optimal path amid the cluster head (CH) and sink node needs to be selected for sustaining energy efficiency and improving network lifespan. In this article, Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)-based clustering scheme is proposed for improving the network lifetime and routing optimization with maximized energy efficacy. It integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade-off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra-cluster distance, inter-cluster distance, and node centrality into account during the process of fitness evaluation. It performed well by constructing an optimized number of clusters, such that energy stability and network lifetime are maintained in the network. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches. Proposed HSSIWOA-CS scheme Hybrid Salp Swarm and Improved Whale Optimization Algorithm (HSSIWOA)-based clustering scheme integrated the exploration capability of Salp Swarm Optimization Algorithm (SSOA) with exploitation benefits of Improved Whale Optimization Algorithm (IWOA) for balancing the trade-off between the rate of exploration and exploitation during CH selection process. It utilized the parameters of residual energy, load balance, intra-cluster distance, inter-cluster distance, and node centrality into account during the process of fitness evaluation. The experimental results of the proposed HSSIWOA scheme confirmed extended network lifetime of 21.64%, minimized energy utilization of 23.42%, and maximized throughput of 18.56%, better than the baseline approaches. image