Stackelberg Game-Based Offloading Strategy for Digital Twin in Internet of Vehicles
Weibo Qin, Chao Zhang, Haipeng Yao, Tianle Mai, Shan Huang, Dong Ming Guo, Ran Gao · 2023
The combination of digital twin (DT) and Internet of Vehicles (IoV) has gained significant attention from both academia and industry in recent times. DT can establish a high fidelity virtual representation of IoV based on the real-time sensor data, and feedback the decision policy, therefore generating possible improvements. Especially, as the advance of Mobile Edge Computing (MEC) technique, it has the potential to facilitate digital twin¡¯s computationally intensive tasks. However, how to schedule the MEC computing resource is the key to efficient operation of the whole system. Therefore, this study aims to investigate pricing considerations and resource management that exist between the vehicle and MEC server in order to mitigate this issue. Specifically, we model the interaction between the MEC server and vehicles as a Stackelberg game, where the leader (i.e., the MEC service provider) sets prices, and then the vehicles act as followers. By leveraging information about social interactions from other vehicles, utility functions are formulated by the vehicles. Additionally, the study analyzes the existence and uniqueness of the Stackelberg equilibrium, and proposes a dynamic iterative algorithm to find the appropriate Nash equilibrium for the proposed Stackelberg game. Experimental results demonstrate that the proposed scheme effectively formulates suitable prices and meets computational requirements.