An Efficient Levy Flight-Based Optimized Energy Efficient Routing for WSN

Wireless Sensor Networks (WSN) play a vital role in IoT, smart agriculture and environmental monitoring, but their performance is severely limited by high energy consumption, inefficient routing and unbalanced clustering. This work introduces a hybrid energy-efficient routing framework that integrates Fuzzy Logic-based Extended Reinforcement Learning (FL-ERL) for adaptive cluster formation, Tent random function-based Bald Eagle Optimization (TBEO) for optimal Cluster Head (CH) selection and a genetic Levy flight-based optimized routing mechanism (GL-ORM) for global inter-cluster routing. The proposed model effectively optimizes node residual energy, node to Base Station (BS) distance and processing delay for a large-scale WSN with 1000 nodes. Simulated results in MATLAB demonstrate significant improvements over state-of-the-art models such as Black Widow Optimization Algorithm (BWOA), Spider Monkey Optimization (SMO), Fish Swarm Optimization (FSO), Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO). Specifically, the proposed method reduces energy consumption to 6.68 J, achieving 68%–72% reduction compared with GWO (20.12 J) and FSO (17.15 J). The model achieves a 50% higher throughput of 0.95 than ACO and maintains a 95% packet delivery ratio, outperforming existing schemes by 10–30%. These results highlight robustness, scalability and energy efficiency of the proposed framework, which demonstrates its ability to prolong WSN lifetime. This approach provides significance and reliable data delivery.