Energy‐Efficient Cluster‐Based Routing in Wireless Sensor Networks Using Fuzzy‐Reinforcement Whale Optimization

Wireless sensor networks (WSNs) have attracted significant attention from researchers due to their wide range of environmental monitoring applications and their ability to adapt and integrate seamlessly with various advanced technologies. However, the limited computational capabilities and energy‐constrained batteries of sensor nodes present major challenges that restrict the broader deployment of WSNs. Clustering has emerged as a key technique to mitigate these challenges, as cluster‐based routing significantly reduces the energy consumption of nodes. The proposed work introduces a hybrid algorithm named Fuzzy‐Reinforcement Whale‐Optimized Clustered Routing (FRWCR), aimed at enhancing energy‐efficient routing through intelligent clustering. FRWCR leverages fuzzy logic for the selection of energy‐efficient cluster heads, employs Q‐learning for adaptive and efficient cluster formation, and utilizes the whale optimization algorithm (WOA) to determine the most energy‐efficient path from the source node to the base station. Simulation results demonstrate that FRWCR outperforms three existing related algorithms in terms of lower average energy consumption, extended network lifetime, higher throughput, improved packet delivery ratio, and reduced routing overhead.