Game-Theoretic Optimization for Task Offloading and Resource Allocation in Parked-Vehicle-Enhanced Internet of Vehicles

Vehicular Edge Computing (VEC) is widely regarded as a promising paradigm for enhancing the Quality of Service in the Internet of Vehicles (IoV). However, due to limited computing resources and increasing computational demands, especially during peak traffic hours in urban environments, VEC faces significant challenges. Meanwhile, the idle computing resources of nearby Parked Vehicles (PVs) remain largely underutilized. To address this issue, we propose a PV-enhanced IoV framework that models hierarchical interactions among multiple Task Vehicles (TVs), Service Providers (SPs), and PVs in task offloading and resource allocation, efficiently leveraging the computing resources of PVs to complement VEC servers. Specifically, we first design a Game-based Pre-offloading Assignment (GPA) algorithm to determine the optimal SP selection for each TV. Once TVs have selected their respective SPs, we model the interaction between each TV and SP as a Stackelberg game, where the SP acts as the leader by setting the service price, and each TV responds by determining its offloading workload. Furthermore, we prove the existence of a Nash equilibrium through backward induction and develop a Gradient-based Stackelberg Pricing (GSP) algorithm to compute this equilibrium. Finally, to emphasize the role of PVs, we propose an Optimized Recruitment Allocation (ORA) algorithm, which enables SPs to efficiently recruit PVs and allocate their computing resources, while ensuring that each PV selects the SP that maximizes its utility. Extensive simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of offloading performance and overall utility improvement.