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Recently, unmanned aerial vehicles (UAVs) assisted multi-access edgecomputing (MEC) systems emerged as a promising solution for providingcomputation services to mobile users outside of terrestrial infrastructurecoverage. As each UAV operates independently, however, it is challenging tomeet the computation demands of the mobile users due to the limited computingcapacity at the UAV's MEC server as well as the UAV's energy constraint.Therefore, collaboration among UAVs is needed. In this paper, a collaborativemulti-UAV-assisted MEC system integrated with a MEC-enabled terrestrial basestation (BS) is proposed. Then, the problem of minimizing the total latencyexperienced by the mobile users in the proposed system is studied by optimizingthe offloading decision as well as the allocation of communication andcomputing resources while satisfying the energy constraints of both mobileusers and UAVs. The proposed problem is shown to be a non-convex, mixed-integernonlinear problem (MINLP) that is intractable. Therefore, the formulatedproblem is decomposed into three subproblems: i) users tasks offloadingdecision problem, ii) communication resource allocation problem and iii)UAV-assisted MEC decision problem. Then, the Lagrangian relaxation andalternating direction method of multipliers (ADMM) methods are applied to solvethe decomposed problems, alternatively. Simulation results show that theproposed approach reduces the average latency by up to 40.7\% and 4.3\%compared to the greedy and exhaustive search methods.

Collaboration in the Sky: A Distributed Framework for Task Offloading and Resource Allocation in Multi-Access Edge Computing