Digital Twin Enabled Remote Data Sharing for Internet of Vehicles: System and Incentive Design

Abstract

With the boom in advanced intelligent vehicles, the amount of data associated with the Internet of Vehicles (IoVs) grows exponentially. Sharing road data among vehicles effectively can greatly improve their driving efficiency and road experience. This, however, is hindered by security concerns, bandwidth limitations, and mobility and communication distance between vehicles. In this article, we propose a citywide data sharing platform among vehicles based on the digital twin. Specifically, we propose a digital twin based vehicular platform on the cloud to facilitate the effective data sharing of vehicles. A digital twin is a digital representation of a vehicle on the cloud that synchronises data with the vehicle in real time. Therefore, data sharing among vehicles can be accomplished by their digital twins on the cloud without any physical limitations. Considering the data privacy of vehicles, a framework that combines federated learning and transfer learning is applied which can realise personalised data sharing among vehicles without disclosing their data privacy. An incentive mechanism based on the game-theoretic approach is devised to combat the mutual distrust between vehicles and encourage their contribution to data sharing. By sewing the above mechanisms, the proposed digital twin enabled data sharing platform can effectively address privacy, bandwidth limitation and incentive issues. Using extensive trace-driven simulations, we demonstrate the effectiveness and efficiency of the proposed system.