Firefly Swarm Intelligence Based Automatic Clustering and Tracking for UANETs

Subject to high mobility, dynamic topology, and limited energy of unmanned aerial vehicles (UAVs), maintaining stable communication performance is a challenging task in UAV ad-hoc networks (UANETs). As a potential solution, clustering routing algorithm divides the entire network into multiple clusters and various optimal strategies can be adopted to achieve strong network performance. In this paper, we propose a firefly swarm intelligence based automatic clustering and tracking algorithm (FSIACT) for UANETs, which is inspired by the collective behavior of fireflies. Firstly, we propose the fitness function consisting of link survival possibility, average distance and residual energy, and utilize it as the light intensity of the firefly. Secondly, firefly algorithm (FA) is put forward for cluster head (CH) selection and cluster management. Based on the characteristics of the FA, the whole swarm can be automatically divided into several clusters and cluster members (CMs) are willing to track the CH in the cluster. It is verified in simulations that the proposed algorithm achieves the lower handover rate of CHs, longer link expiration time (LET) and longer node lifetime.