Dynamic Digital Twin and Distributed Incentives for Resource Allocation in Aerial-Assisted Internet of Vehicles

Abstract

Internet of Vehicles (IoV), when empowered by aerial communications, provides vehicles with seamless connections and proximate computing services. The unpredictable network dynamics of aerial-assisted IoV pose challenges to the resource allocation. In this article, dynamic digital twin (DT) of aerial-assisted IoV is established to capture the time-varying resource supply and demands, so that unified resource scheduling and allocation can be performed. We design a two-stage incentive mechanism for resource allocation based on Stackelberg game where DT of vehicles or road side units (RSUs) is deemed as the leader, and the RSUs who provide computing services are as the follower. In the first stage incentive, we determine the computing resources that RSUs are willing to offer according to vehicles’ preferences. To further maximize the satisfaction of vehicles and the overall energy efficiency, a distributed incentive mechanism based on alternating direction method of multipliers (ADMMs) is then designed, in which the resource allocation policy for each vehicle is optimized. Thanks to ADMM, the incentive mechanism can be run at multiple RSUs in parallel to reduce delay and relieve the computational burden of UAVs. Simulation results show that the proposed scheme can improve the satisfaction of vehicles and the energy efficiency at the same time.