Investigating particle swarm optimization and various mobility algorithms for autonomous navigation in flying ad-hoc networks

Abstract

Flying ad-hoc networks (FANETs) have gained significant attention within the research community due to the widespread availability of unmanned aerial vehicles (UAVs) and the electronic components required for their control and connectivity. Various applications, such as 3D mapping, construction inspection, and emergency response operations, stand to benefit from leveraging a swarm of UAVs instead of a single UAV. This necessitates the establishment of an ad-hoc network for communication and coordination. An important aspect of implementing FANETs involves autonomously determining the optimal position of the UAVs to ensure communications with the ground nodes while maximizing coverage and connectivity to a remote server. In this research, an application of particle swarm optimization (PSO) algorithm is proposed to achieve optimal positioning of the UAVs facilitating air-to-ground communications to several ground nodes whose positions within the grid are unknown. The performance of the PSO algorithm is compared with fixed and hybrid models across varying grid sizes (1000 × 1000 and 1500 × 1500), numbers of UAVs (N = 3, …, 9), and numbers of sensor nodes (n = 10, 20, and 30). The log-normal propagation model is considered to account for channel fading effects resulting from multipath propagation.