Idle-parked vehicles assisted collaborative resource allocation in VEC based on Stackelberg game.

With the advent of autonomous vehicles, how to meet the computing demands of vehicles with computing -intensive and latency-sensitive tasks becomes a challenge. Vehicular Edge Computing (VEC) is an advanced computing paradigm that improves the Quality of Service (QoS) of vehicles by offloading tasks to VEC servers. However, the computing resources of the VEC server are limited and thus cannot meet the offloading needs of the vehicle. This paper proposes to use idle-parked vehicles to assist the VEC server to offload computing tasks, which increases the resource capacity and expands the communication range. Firstly, we propose an idle-parked vehicle-assisted VEC model, which incentivizes idle-parked vehicles to assist VEC server computing tasks by obtaining rewards from VEC servers. Secondly, this paper proposes a more flexible dynamic offloading scheme based on comprehensive consideration of the selection strategy and pricing strategy. Then, based on the Stackelberg game, the interaction between the VEC server and the idle-parked vehicle is analyzed, and utilize backward induction to prove that there is a unique Nash equilibrium in this game. Finally, an improved joint selection decision and pricing decision algorithm based on the Branch and Bound (JSPBB) algorithm is proposed, which achieves the optimal pricing under the condition of maximizing the utility of idle-parked vehicles and each VEC server under this pricing optimal offloading strategy to maximize system utility. The simulation results can show that the proposed algorithm can solve the problem effectively and has good performance so that the system can obtain higher benefits.