Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things

Abstract

Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and the industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including cost of deployment, transmission loss of telecommunication, and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer. Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services while obtaining shorter path length and greater energy efficiency.