Joint task scheduling, resource allocation, and UAV trajectory under clustering for FANETs

This paper establishes a new layered flying ad hoc networks (FANETs) system of mobile edge computing (MEC) supported by multiple UAVs, where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members (CMs) within a cluster to its cluster head (CH). Then, we need to determine whether each CH' tasks are executed locally or offloaded to one of the MEC UAVs for remote execution (i.e., task scheduling), and how much resources should be allocated to each CH (i.e., resource allocation), as well as the trajectories of all MEC UAVs. We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them, respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.