BICSF: Bio-inspired Clustering Scheme for FANETs

Flying ad hoc networks (FANETs) have dynamic topology because of the mobile unmanned aerial vehicles (UAVs). The limited battery resource and mobility of UAVs cause unstable routing in the FANET. In this paper, we try to minimize this issue with the help of an efficient clustering scheme. We propose a bio-inspired clustering scheme for FANETs (BICSF), which uses the hybrid mechanism of glowworm swarm optimization (GSO) and hill herd (KH). The proposed scheme uses energy aware cluster formation and cluster head election on the basis of the GSO algorithm. Furthermore, we propose an efficient cluster management algorithm using the behavioral study of KH. We also use genetic operators such as mutation and crossover for the optimal position of the UAV. For route selection, we propose a path detection function based on the weighted residual energy, number of neighbors, and distance between the UAVs for efficient communication. The performance of BICSF is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with grey wolf optimization and ant colony optimization-based clustering algorithms.