Modeling a Novel Self-Optimized Wolf Optimizer for a Heterogeneous Network Model for Energy and Node Lifetime Analysis

The vital services of surveillance, information collection, and data transmission from high-risk environments to safer locations are still provided by Wireless Sensor Networks (WSNs). These services are improved by the majority of energy-efficient routing protocols structured for this purpose. A homogeneous routing protocol is applied to decrease the energy utilization of far-off hubs more efficiently; however, the energy utilization rate is higher for this protocol, poorer dependability, and more unfavorable information broadcast to the Wireless Router (WR) or base station (BS) when employed for a longer timeframe. To overcome these drawbacks, a modified Self-Optimized Wolf Optimizer (SOWO) is employed in this research. Incorporating heterogeneous nodes into the current approach, selecting the head based on remaining energy introduces a multi-level interaction strategy throughout the connections. Employing an energy hole elimination method is the foundation of the developed routing technique. Each approach aims to extend the network's lifetime and reduce energy consumption. Based on the findings, the proposed routing scheme demonstrates superior consistency periods, residual energy, throughputs, and network lifespan compared to existing ones. The research addresses the classical clustered-WSN problem of maximizing lifetime and sustained delivery under tight per-node energy budgets while keeping load/fairness balanced. The simulation results show a 3.4% and 32.22% improvement in network stability and residual energy, respectively, over existing algorithms.