Artificial Rabbit Combined K-medoid Golden Eagle Optimization for Energy Efficient Routing in WSN

Energy harvesting (EH) in Wireless Sensor Network (WSN) collects ambient energy to power sensor nodes which reduces battery reliance and extends the network lifetime (NL). However, drawbacks include variability in energy sources, conversion inefficiencies, and energy distribution imbalances, that impact network stability. To overcome these issues a novel artificial Rabbit Optimization-based Adaptive K-Medoid golden eagle for WSN (ROAM-WSN) is proposed for reducing the reliance on conventional power sources and extending the network's operational lifetime. Initially, the system utilises a hybrid energy harvesting system to gather energy from diverse natural and artificial sources to ensure consistent energy availability for sensor nodes in the network. The harvested energies are converted into usable electrical energy through an energy conversion mechanism and stored in the energy storage units. After energy conversion, a hybrid clustering-based optimisation selects optimal Cluster Heads (CHs) to minimise energy consumption (EC). Finally, an artificial optimization algorithm enhances the CH selection for reliable transmission through routing. The ROAM-WSN framework is simulated by using a network simulator (NS3). In comparison, the proposed ROAM-WSN approach achieves a low energy consumption of 20J, then EHRWO of 37J, LCEHA of 42, and LANTERN of 33J respectively.