Resource Trading for Vehicular Edge Computing Networks: A Trust-Based Double Auction Approach

Vehicular edge computing (VEC) is an emerging computing paradigm that alleviates the limitations of local computing resources for the Internet of Vehicles. However, the lack of trust among distributed nodes and ineffective incentive mechanisms discourage Roadside Units (RSUs) from providing resources. In addition, information asymmetry can lead to the clearing prices of resources failing to accurately reflect the actual value of resources. To address these issues, we propose a trustbased double auction framework in VEC networks. In this framework, to incentivize the RSUs with high trust to participate in resource trading and ensure the clearing prices accurately reflect the actual value of resources, we first design a trustbased resource pricing mechanism. In this mechanism, RSUs with higher trust can set higher resource prices and the clearing prices of resources are closer to the buyers’ bids. Then, we develop a trust-based double auction mechanism that incorporates the Edmonds-Karp algorithm for efficient buyer-seller matching. Considering the time-varying nature of the VEC networks, we employ deep reinforcement learning to optimize decision-making to maximize the social welfare. Finally, we demonstrate that the proposed double auction model satisfies key economic properties such as individual rationality and incentive compatibility. Simulation results validate that our proposed approach outperforms benchmark schemes in terms of social welfare.