Task Offloading in UAV Swarm-Based Edge Computing: Grouping and Role Division

Abstract

Due to the outstanding characteristics of unmanned aerial vehicles (UAV), i.e., maneuverability and flexibility, UAV enabled mobile edge computing (MEC) has become a widely attractive research direction. However, single-UAV cannot be qualified for numerous tasks and application scenarios in view of its limited computing capacity, while multi-UAV enabled MEC is still in the initial stage, and most existing work transformed the problem of multi-UAV enabled MEC into multiplied single-UAV. The UAV swarm can make UAVs cooperate intelligently, and accomplish diversified tasks in complex environments at low cost, which is regarded as a promising development direction of UAV technology. Nevertheless, it is inefficient since each UAV node is responsible for both communication and computation, and multi-hop transmission among UAVs may lead to a very high delay. Toward this end, the paper takes the lead in studying the problem of grouping and role division in UAV swarm-based edge computing, and puts forward a grouping and role division algorithm to solve it. Final experimental results corroborate that the complexity of our algorithm is less than that of the traditional algorithm, and role division can maximize the use of communication and computing resources.