Energy Aware Routing in WSNs using Hybrid Fuzzy and Yellow Saddle Goatfish Optimization

Wireless Sensor Networks (WSNs) consist of numerous small, autonomous sensor nodes capable of sensing, processing, and wirelessly transmitting data about their surrounding environment. One of the most critical challenges in WSNs is achieving energy efficiency, which directly impacts network longevity and reliability. Clustering and optimized routing have proven effective in addressing this challenge. In this study, a hybrid clustering technique combining Fuzzy logic and the Yellow Saddle Goatfish Algorithm (HF-YSGA) is proposed for optimal cluster head (CH) selection. The approach aims to balance energy consumption, minimize inter-node communication delay, and reduce transmission distances. To further enhance performance, the Energy-Centric Butterfly Optimization Algorithm (EC-BOA) is employed for efficient routing and shortest path determination, dynamically minimizing network load. Simulation results demonstrate that the proposed method significantly outperforms existing techniques in key Quality of Service (QoS) parameters. For a network of 100 sensor nodes, the method achieves a Packet Delivery Ratio (PDR) of 100%, packet delay of 0.05 seconds, throughput of 0.99 Mbps, energy consumption of 1.97 mJ, network lifetime of 5908 rounds, and a Packet Loss Rate (PLR) of just 0.5%. These results highlight the effectiveness of the proposed hybrid optimization-based clustering and routing strategy for energy-efficient WSN operations.