Incentive-driven computation offloading and resource pricing strategy in vehicular edge computing assisted with idle mobile vehicles

Abstract

The emergence of vehicular edge computing (VEC) has introduced a new computational paradigm for high-quality processing of computing services in Internet of Vehicles (IoV) scenarios. However, due to the limited computational resources of the VEC server, it is not sufficient to adequately meet the demand for highly concurrent computational services in high-density vehicular communication networks. To address this issue, we consider an idle mobile vehicles assisted vehicular edge computing framework and propose a hybrid Stackelberg-Match cooperative task offloading and resource pricing algorithm (SMOP). The algorithm considers the mobility of vehicles and the duration of channels, coordinating the computational resources of fixed VEC servers and idle mobile vehicles within the vehicular network (VN). This enhances offloading efficiency and maximizes participant benefits. Specifically, the Stackelberg game is used to derive differentiated pricing schemes for idle mobile vehicles and VEC servers for different vehicular tasks, and the stable matching method is employed to determine task offloading strategies. Finally, we conduct experiments on a real Chengdu traffic dataset. The results demonstrate that the proposed solution effectively reduces offloading costs and exhibits strong robustness in handling latency-sensitive and data-intensive service requests.