Joint Trajectory, Resource, and Access Optimization in Multi-UAV Collaborative Mobile Edge Computing Networks for Low-Altitude Economy

This article addresses trajectory optimization, resource allocation, and access management in a multi-uncrewed aerial vehicle (UAV) assisted collaborative mobile edge computing (MEC) network for the low-altitude economy. In the network, UAVs collaborate to compute offloaded tasks and improve fairness among time-varying UAV battery levels. The objective of this article is to maximize the network utility defined by the size of successful offloaded tasks, the fairness among the user equipments (UPs), and the processing time and the energy consumption of the UAVs. In particular, we consider the time-varying UAV battery model, which affects the energy cost weights of the UAVs. Therefore, we propose a heuristic optimization framework which integrates the utility partitioning two-stage matching (UPTSM) algorithm and the variables constrained whale optimization algorithm (VC-WOA). The UPTSM algorithm decomposes the original optimization problem into two subproblems and models them as the bipartite graph matching problems. The VC-WOA achieves the search for legal solutions by limiting the variables which violate the task processing time constraints. Simulation results demonstrate the effectiveness of the proposed heuristic optimization framework in speeding up the convergence and improving the fairness among the UAV battery levels.