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

2025-04-21

AI Summary

This paper addresses the resource allocation challenge in 6G-integrated intelligent Internet of Vehicles (IoV), where vehicle service providers’ (VSPs’) utility functions are unknown, yet customized network slicing requirements must be satisfied while maximizing social welfare. Method: We propose the first dual-currency VCG auction mechanism, introducing “information value” as a novel metric. Theoretically proven to be strongly incentive-compatible under privacy preservation and collusion resistance, it is further integrated with mean-field multi-agent reinforcement learning (MFMARL) to construct a dynamic information value quantification model. Contribution/Results: Experiments demonstrate that our approach significantly accelerates convergence compared to conventional quantized VCG methods, improves average social welfare by 18.7%, and enhances collusion robustness by 42.3%. These results validate its effectiveness and superiority for coordinated resource optimization in 6G IoV networks.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Maximizing social welfare with unknown VSP utility functions
• Efficient resource allocation for dynamic IoV services
• Preventing collusion among VSPs in auction mechanisms

Innovation

• MFMARL optimizes IoV resource allocation dynamically
• Enhanced VCG auction uses value of information
• DSIC mechanism prevents VSP collusion effectively