Terrain-Aware UAV-Enabled Mobile Edge Computing in Urban Environments: A Constrained Multi-Objective Approach With Task-Adaptive Mechanism

With the increasing and multifaceted demands for wireless communication in smart cities, Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems have garnered substantial interest due to their potential to enhance connectivity and computational capabilities. In these systems, UAVs are deployed to predetermined locations to establish communication with nearby user devices (UDs), thereby providing network coverage and computational offloading services. Nevertheless, the presence of complex urban terrains, including rough topographies and dense buildings, results in significant path loss in UAV-to-UD communication links and increases the risk of collisions during UAV navigation, ultimately degrading the quality of service (QoS) and compromising UAV operational safety. To mitigate these challenges, this study introduces a terrain-aware channel model capable of precisely evaluating the effects of terrain characteristics on UAV-to-UD link performance. To improve QoS for UDs while ensuring UAV flight safety, we formulate a constrained multi-objective optimization problem (CMOP) to jointly optimize UAV trajectory, destination, and resource allocation, aiming to minimize task completion time and enhance UAV flight safety. To solve this CMOP, we propose a multi-task constrained multi-objective evolutionary algorithm with a task-adaptive mechanism, promoting the search for Pareto optimal solutions. Numerical results indicate that the proposed method outperforms baseline algorithms in achieving a well-converged set of feasible solutions with enhanced diversity.