Spotted Hyena with Fire Hawk Optimization Algorithm Driven Cluster Based Routing for Wireless Sensor Networks

A Wireless Sensor Network (WSN) collects data about the environment and transmits it to a central location via distributed Sensor Nodes (SNs). Advancements in sensor equipment, size, interfaces, and cost have led to many WSN applications. Energy effectualness is the most researchable topic of the energy-constrained WSN. Many models are used to handle energy consumption (ECON), and the most promising methods are clustering and routing. The WSN requires a routing protocol to transmit data to the sink via a cost-effective link. A primary issue is detecting the element's constrained energy so that the higher power is utilized consistently over time. Energy-efficient routing can extend lifespan by using less energy. This study develops a spotted hyena with fire hawk optimization algorithm-driven cluster-based routing (SHFHOA-CBR) technique in WSN. The SHFHOA-CBR technique follows two significant processes: energy-efficient clustering and routing. To accomplish this, the SHFHOA-CBR technique includes a spotted hyena optimizer-based clustering approach (SHO-CA) to choose an optimal set of CHs and generate clusters. The SHO-CA technique derives a fitness function (FF) comprised of distance to neighbouring nodes (DTN), residual energy (RE), and trust level (TL). For route selection, the SHFHOA-CBR technique encompasses the fire hawk optimizer-based routing (FHO-R) technique to choose optimal routes to BS. Finally, the FHO-R technique includes three input variables: node degree (ND), RE, and DTN. The investigational evaluation of the SHFHOA-CBR model is conducted using diverse measures, namely ECON, Latency, Packet Delivery Ratio (PDR), Throughput (THRO), Network Life-Time (NLT), End-to-End Delay (EED). The experimental outputs infer that the SHFHOA-CBR technique achieves promising performance over current techniques.