Dissatisfaction Feedback and Stackelberg Game-Based Task Offloading Mechanism for Parked Vehicle Edge Computing
Songxin Lei, Xinyao Guo, Junyi Li, Yixiao Wang, Zhang Yu, Lu Zheng, Huaming Wu
Abstract
Considering the limited computing power of Mobile Edge Computing (MEC) servers and the emergence of Vehicular Ad-Hoc Networks (VANETs), we employ the computing paradigm known as Parked Vehicle Edge Computing (PVEC) to leverage the computational capabilities of idle vehicles, thereby enhancing the overall computing performance of these vehicles. We establish a multi-stage Stackelberg game model, which captures the interactions among the requester (RV), the service provider (SP), and the parked vehicles (PV). In order to incentivize parked vehicles to provide computing power, we design a dissatisfaction feedback mechanism. To optimize the system and maximize the relative benefits of all stakeholders, we formulate an optimization problem to find an optimal pricing scheme that guides task allocation and resource utilization. We employ reverse induction and gradient descent to solve this problem. Simulation results demonstrate the effectiveness of the dissatisfaction feedback mechanism and provide insights into the changing trends of optimal strategies at each stage as the task density increases. These findings contribute to the understanding of PVEC and offer guidance for real-world task offloading scenarios.