An Efficient Sparrow Search Algorithm based Clustering and Energy Efficient Opportunistic Routing Protocol (EEOR) for Wireless Sensor Network

Wireless Sensor Networks (WSNs) have had a substantial influence on diverse industries such as education, agriculture, manufacturing, and environmental monitoring. However, due to their wireless characteristics, sensor nodes (SNs) in distant or unsupervised regions cannot substitute their batteries, prompting extensive research to extend their operational lifespan. Although cluster-based routing has proven effective in tackling this challenge, there is still room for improvement in optimizing the selection of cluster heads (CHs) by incorporating crucial parameters. Traditionally, the emphasis has been on either selecting CHs or transmitting data between nodes, but meta-heuristic methods present a promising strategy for attaining optimal network performance. This proposed research work presents a novel approach called the sparrow search optimization algorithm (SSO) for improving performance through the combination of clustering and the energy-efficient opportunistic routing (EEOR) protocol. The suggested hybrid methodology was implemented using MATLAB, and the obtained outcomes were compared to three commonly employed techniques: low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed (HEED), and a whale optimization algorithm (WOA)-based clustering approach. The QoS parameters are represented by the following performance results: packet delivery ratio (PDR) of 98%, throughput of 0.92 Mbps, energy consumption of 8.70 mJ, network lifetime (NLT) of 6000 rounds, and an end-to-end delay (E2ED) of 0.01843 ms for the proposed method.