Energy- and Latency-Efficient Resource Allocation for RIS-Assisted UAV-USV Cooperative MEC Network

The potential of cooperative unmanned aerial vehicle (UAV)-unmanned surface vehicle (USV) multi-access edge computing (MEC) platform cannot be fully revealed without thoroughly investigating USVs bidirectional data computation. In this paper, a novel reconfigurable intelligent surface (RIS)-assisted UAV-USV cooperative MEC network architecture is proposed considering USVs bidirectional tasks with hard time window. The weighted sum minimization of UAVs energy consumption and task execution latency is formulated by jointly considering USVs task execution mode selection indicators, UAVs flight route indicators, UAVs arrival time, UAVs hovering coordinates and RIS phase shift vector. A heuristic solution is proposed to solve the formulated challenging problem, where we divide the original problem into three subproblems, i.e., UAVs flight route indicators subproblem, the joint task execution mode selection indicators and UAVs arrival time subproblem and the joint UAVs hovering coordinates and RIS phase shift vector design subproblem; each of which is solved by the proposed enhanced grey wolf optimizer (EGWO) algorithm, the integer constrained-removed augmented Lagrangian (ICRAL) algorithm and the proposed multi-objective differential evolution (MODE) algorithm, respectively. The results verify that the proposed solution can significantly decrease the weighted sum of UAVs energy consumption and task execution latency in comparison with numerous selected advanced algorithms.