DRAM: Digital Twin-Driven Double-Layer Reverse Auction Method for Multi-Platform Vehicular Crowdsensing
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Wang, Zhenning, Yue Cao, Huan Zhou, Xiaokang Zhou, Jiawen Kang, and Houbing Song. “DRAM: Digital Twin-Driven Double-Layer Reverse Auction Method for Multi-Platform Vehicular Crowdsensing.” IEEE Transactions on Mobile Computing, 2025, 1–18. https://doi.org/10.1109/TMC.2025.3594488.
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Abstract
Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-driven Double-layer Reverse Auction Method (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.