Chemotaxis Bio-Inspired Clustering Algorithm to Develop Self-Organized UAV Topologies with Adaptive Behavior

This paper proposes a distributed and selforganized dynamic clustering scheme for Unmanned Aerial Vehicle (UAV) networks. The model is bio-inspired by the chemotaxis behavior of bacteria and uses light signals to dynamically adapt the movement of cluster follower nodes toward cluster heads. The proposed algorithm relies only on tumble and run movements, without requiring geographic data or complex computations. The results demonstrate the emergence of selforganized clustering behavior that adapts to the constant motion of nodes. Performance evaluations show that the optimal number of clusters achieved by the model outperforms existing bioinspired approaches, such as Glowworm swarm intelligence schemes.