The low latency networking method for task-driven MEC-enabled UAV swarm

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly being used for special missions as Mobile Edge Computing (MEC) devices, and the utilization of multiple UAVs who are integrated into formations for collaboratively executing tasks that cannot be performed by single aircraft has also been a recent research hit. In this paper, we present a low latency networking method aims to shorten the preparation time so that MEC-enabled swarm can as quick as possible concentrate on the task execution. For reducing the time of swarm initialization, we consider applying the Leader-Follower (L-F) topology model in networking and formation control. Firstly, by combining the nature of wireless communication and correlation analysis, we improve the traditional L-F model to avoid too many nodes accessing the same node and raise the communication efficiency. Secondly, in order to choose the topology that can provide better performance in network, we adopt an improved Particle Swarm Optimization (PSO) algorithm to customize the appropriate factor coefficients during leader selection for swarms with different densities and communication initial conditions. Experiments and evaluations demonstrate that the proposed algorithm has significant effect in reducing network latency.