Distributed Incentives and Digital Twin for Resource Allocation in air-assisted Internet of Vehicles

Abstract

Internet of Vehicles (IoV) can realize seamless communication connection and computing offloading services with the assistance of air communication. Limited by the high network dynamics of the air-assisted IoV, resource allocation faces great challenges. In this paper, dynamic digital twin of air-assisted IoV is established to capture the time-varying resource supply and demands, so that unified resource scheduling and allocation can be performed. We designed an incentive mechanism for resource allocation based on Stackelberg games to maximize vehicle satisfaction and overall energy efficiency. In the game, the digital twin of air-assisted IoV within the coverage of unmanned aerial vehicles (UAV) are regarded as leaders, while RSUs that provide computing services are followers. At the same time, in order to reduce the delay and reduce the computational burden of the UAV, a distributed incentive mechanism based on the Alternating Direction Multiplier Method (ADMM) was designed to optimize the resource allocation strategy of each RSU. Simulation results show that the proposed scheme can improve the satisfaction of vehicles and the energy efficiency at the same time.