Multi-Objective Remora Optimization Algorithm for Efficient Routing in Cognitive Wireless Sensor Network

Cognitive Wireless Sensor Networks (CWSNs) are exposed to severe energy constraints, requiring investigation into solutions for extended network lifetimes. Radio Frequency Wireless Energy Harvesting (RFWEH) has proven its ability to efficiently mitigate these limits. However, the dynamic nature of many CWSN applications, influenced by node mobility, causes issues as network topologies changes. A unique Multi-Objective Remora Optimization Algorithm (MROA) is developed to handle the combination of energy harvesting and node mobility. MROA is designed for mobility-aware CWSNs and incorporates energy harvesting effortlessly. To choose cluster heads, the protocol applies an R-factor parameter that incorporates residual node energy, residual node data, and node speed with appropriate weighting. As a result, the MROA framework improves the network's energy performance, which leads to an extended using lifetime. When compared to existing methods namely RMAC-M, R-MAC, and WORP the suggested method performed well in the 500 rounds. It achieved in terms, Alive nodes -3400, Throughput - 170.57 kbps and Delay - 45.00 seconds respectively.