An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective

TL;DR

This paper tackles efficient resource allocation in IoV by marrying a VoI-based dual-currency VCG auction with mean-field multi-agent reinforcement learning. The Bank-issued VoI Currency enables auctions under unknown VSP utilities, while DSIC and collusion resistance are theoretically analyzed. Empirical results show faster convergence and strong social welfare performance with significantly reduced information exchange compared to quantized or non-mean-field baselines. The approach is scalable to varying NSP resources and VSP counts, offering practical implications for 6G-enabled vehicular networks.

Abstract

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the ``value of information" as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6G networks.

Figures (10)

• Figure 1: Schematic illustration of how IoV resources are sliced and allocated.
• Figure 2: Three phases of VCG auction mechanism for dual currency.
• Figure 3: Three phases of VCG auction mechanism for dual currency under MFMARL framework.
• Figure 4: Comparative convergence of the total social welfare under different multi-agent reinforcement learning algorithms when the number of VSPs is 10.
• Figure 5: Comparative convergence of the total social welfare under different multi-agent reinforcement learning algorithms when the number of VSPs is 30.