Stackelberg Game-Based Computation Offloading and Pricing in UAV Assisted Vehicular Networks

Abstract

Unmanned aerial vehicle (UAVs) have the advantages of high flexibility and ease of deployment, making it possible to provide mobile edge computing services as an aerial server for remote or hot spot areas, e.g., computation offloading. However, there are bottlenecks in guaranteeing the reliability of computing resource allocation and incentivizing their participation in edge services. In view of this, we study the computation offloading and resource pricing joint optimization problem in the UAV-enabled vehicular edge computing network. In this article, we first formulate the interaction between vehicles and one UAV as a Stackelberg game, which maximizes the profits of the UAV and the utilities of vehicles considering delay, energy consumption, and urgency. Then, we analyze the existence and uniqueness of Stackelberg equilibrium (SE) under uniform and discriminatory pricing schemes applying backward induction. Finally, we implement such SE in both complete interaction information and incomplete interaction information scenarios. Specifically, one Stackelberg game-based dynamic iterative decision algorithm (SDID) and one reinforcement learning (RL)-based joint optimization offloading and pricing algorithm (RLOP) are proposed to intelligently obtain offloading and pricing strategies, respectively. Simulation results show that our proposed SDID and RLOP achieve significant improvements in the utility, compared to other baseline algorithms.