Adaptive Clustering in FANETs: A Fuzzy Logic Approach for Energy Efficiency and Network Stability

Flying Ad Hoc Networks (FANETs), composed of unmanned aerial vehicles (UAVs), face challenges such as high mobility, rapid topology changes, and limited energy. This paper addresses the cluster head (CH) selection problem in FANETs, a complex NP-hard optimization due to dynamic topology and energy constraints, by proposing ESOFCluster, a novel fuzzy logic-based clustering algorithm. ESOFCluster integrates four normalized parameters: residual energy, safe average distance, relative velocity, and link connectivity duration to form energy-efficient and stable clusters. Extensive NS-3 simulations across different swarm sizes demonstrate that ESOFCluster significantly outperforms LEACH, EMASS, and SOFCluster. Specifically, it reduces role change frequency by $$\approx 82\%$$ ≈ 82 % at 30 m/s, increases packet delivery ratio (PDR) up to 0.95 (vs. 0.55 for LEACH), extends average cluster lifetime by $$62\%$$ 62 % , and achieves $$10\%$$ 10 % lower energy consumption over simulations. These results demonstrate ESOFCluster’s scalability and robustness, making it a promising framework for UAV swarms in disaster response, environmental monitoring, and other critical missions requiring reliable and energy-aware communication.