Abstract: Vehicular edge computing(VEC) is an important application of mobile edge computing(MEC) in Internet of vehicles.In VEC,to meet the computing requirement of task vehicles(TaV),TaV can pay to offload tasks to the VEC server or service vehicles(SeV)with abundant idle computing resources.For the VEC provider,one of its goals is to maximize revenue.Since the computing requirements and the computing resources of the system change dynamically,it is an important issue to design a reasonable pricing strategy in vehicle collaboration scenarios.To solve this problem,this paper designs a dynamic pricing strategy.In this strategy,service prices of the VEC server and SeV are adjusted dynamically according to the relationship of the supply and demand of computing resources.On this basis,a vehicle collaborative computation offloading scheme is designed to maximize provider's revenue.By transforming the revenue maximization problem of VEC provider under the delay constraint into a multi-user matching problem,the offloading results are obtained using the Kuhn-Munkres(KM)algorithm.Simulation results show that compared to existing strategies,with this dynamic pricing strategy,the prices of the VEC server and SeV can be adjusted dynamically with the supply and demand of resources,so as to maximize the provider's revenue.Compared to existing offloading schemes,this scheme can improve the provider's revenue while meeting task delay.

Key words: Vehicular edge computing, Computation offloading, Collaborative computing, Dynamic pricing