A Stackelberg Game-Based Trajectory Planning Strategy for Multi-AAVs-Assisted MEC System

Nowadays, Mobile Edge Computing (MEC) has been widely deployed to enhance the computational capabilities of mobile devices. However, the geographic location of MEC servers is usually fixed. In order to provide flexible edge computing services, some works have considered integrating Autonomous aerial vehicles (AAVs) into MEC networks. In the context of AAV-assisted edge computing, there usually exist multiple AAVs and users, and each AAV may aim to maximize its profit by providing computing services, while users will decide which AAVs to utilize based on their preferences. In this context, how AAVs and users effectively plan their trajectories becomes particularly important as it will affect the profitability of AAVs and the user experience. Since the trajectories of AAVs and users are affected by each other, we model the trajectories of AAVs and users as a Stackelberg game, and then design trajectory planning strategies for users and AAVs based on Independent Proximal Policy Optimization (IPPO) and Proximal Policy Optimization (PPO) respectively, aiming to maximize AAVs’ profits while ensuring user acceptance of AAV services. Finally, we evaluate the proposed trajectory planning strategy against three typical benchmark strategies using synthetic and realistic datasets. The experimental results demonstrate that our strategy can outperform benchmark strategies in terms of AAV profit while guaranteeing users’ service experience.