Budget-Constrained Resource Allocation and Pricing in VEC: A MSMLMF Stackelberg Game With Contract Incentive Mechanism

As a rapid increase in Internet of Things (IoT) devices, vehicle fog/edge computing (VFC/VEC) has seen swift development. Consequently, these devices often opt to purchase computing resources from the edge–cloud service providers (ECSPs) to expand their capabilities. However, due to limited edge server resources, they may face risks of overload or breakdowns. Meanwhile, there are often large amounts of underutilized idle resources near roads (such as parked vehicles), which can provide additional computing and communication capabilities to the system. Inspired by this, we propose a scheme that utilizes idle vehicles to assist in computation. To coordinate the interests of various participating entities and incentivize resource sharing, we construct a multistage multileader multifollower (MSMLMF) Stackelberg game model that encompasses the collaboration and competition among users, ECSPs, vehicle operators (VOPs), and idle vehicles. Participants aim to maximize their utility while considering the potential actions of others. Additionally, considering the information asymmetry between VOP and vehicles, we introduce individual rationality (IR) and incentive compatibility (IC) constraints from the contract theory to analyze and ensure the effectiveness of contracts. Next, we employ backward induction to gradually simplify the game model into convex optimization problems and theoretically prove the existence and uniqueness of Nash equilibrium (NE) points. Finally, through simulation experiments verify that our proposed model and scheme outperform other baselines in overall social welfare.