Analysis of UAV s Flight Energy Minimization for RIS-Assisted Multi-UAV-Enabled MEC Network

With the rapid developments of unmanned aerial vehicle (UAV)-unmanned surface vehicle (USV) cooperative network, a list of novel wireless inland waterway communication services has emerged. However, UAV s are generally battery-empowered with limited operation hours, USV s communication and computing demands still cannot be promised by deploying a large number of UAV s. In this paper, a reconfigurable intelligent surface (RIS)-assisted multi-UAV-enabled mobile edge computing (MEC) network architecture is proposed. UAV s flight energy minimization problem is formulated by jointly considering USV s task execution mode indicators, UAVs hovering coordinates, UAVs flight route indicators and RIS phase shift vector. To solve the formulated challenging problem, we decouple it into several subproblems, e.g., the optimization of UAVs flight route indicators subproblem, the optimization of USVs task execution mode indicators subproblem and the joint optimization of UAVs hovering coordinates and RIS phase shift vector subproblem. Then, each subproblem is solved using the proposed enhanced firefly (EF) algorithm, augmented Lagrangian method (ALM), and Lagrange multipliers (LRM), respectively. The results verify the effectiveness of the proposed solution in comparison with numerous advanced benchmarks. The obtained results can be utilized in wireless indoor RIS-assisted UAV communications, such as intelligent library inspection and positioning.