A Novel Hybrid Beluga Whale and Jellyfish Optimization for EEIICR Based Algorithm for WSN

Abstract

Based on the monitoring environment, the frequent generation of extensive amounts of redundant sensing data is processed in the WSN applications. Aggregating all of these data using effective data aggregation techniques is essential for lowering the energy usage in a network. Numerous clustering approaches have been employed to aggregate the copious amounts of data supplied by sensor nodes. Due to the inefficiency of the clustering and CH selection algorithms, the CH nodes in the majority of clustering environments are compelled to send the sink node, which is located far from the CHs. Therefore a novel framework for energy-aware clustering was proposed. Phases including Data Aggregation, Routing, and Optimal Clustering are included in the model. The first stage, known as optimal clustering and it involves the introduction of the new Jelly Customised Beluga Whale optimisation algorithm (JC-BWOA) to complete the clustering process while taking trust, energy, delay, and intra- and inter-cluster distance into account. By taking link quality and distance into account, the JC-BWOA algorithm performs optimal routing. Based on the investigation, the suggested methods produced the best outcomes for WSN clustering that is energy-efficient.