Adaptive Energy‐Aware Routing for Wireless Sensor Networks Using Fuzzy Logic‐Driven Greylag Goose Optimization

Wireless Sensor Networks (WSNs) commonly form part of smart monitoring and data collection systems, in which energy‐efficient communication is still a challenging issue caused by the insufficient battery capacity of sensor nodes. A hybrid fuzzy logic‐based clustering and Gray Wolf Optimization (GWO) based routing, ECFGWO, called ECFGGO, is proposed in this paper for sustainable and adaptive data transmission in WSNs. The suggested fuzzy‐based inference mechanism for cluster head selection takes two variables, namely distance from the base station and remaining energy, into account, resulting in optimized energy usage and better network stability. To further enhance routing efficiency, the GGO algorithm exploits the V‐formation flight behavior of greylag geese to construct energy‐aware and balanced communication paths between cluster heads and the sink node. In addition, the adaptive optimization strategy enhances the convergence behavior of the routing process and mitigates premature stagnation commonly encountered in metaheuristic optimization. MATLAB‐based simulations demonstrate that the proposed ECFGGO framework outperforms the compared routing protocols regarding network lifespan, energy efficiency, packet delivery ratio, and throughput. The results support the potential of the proposed intelligent optimization framework for sustainable and reliable communication in WSN environments. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.