Abstract
With the rapid popularization of mobile devices, the mobile crowdsourcing has become a hot topic in order to make full use of the resources of mobile devices. To achieve this goal, it is necessary to design an excellent incentive mechanism to encourage more mobile users to actively undertake crowdsourcing tasks, so as to achieve maximization of certain economic indicators. However, most of the reported incentive mechanisms in the existing literature adopt a centralized platform, which collects the bidding information from workers and task requesters. There is a risk of privacy exposure. In this paper, we design a decentralized auction framework where mobile workers are sellers and task requesters are buyers. This requires each participant to make its own local and independent decision, thereby avoiding centralized processing of task allocation and pricing. Both of them aim to maximize their utilities under the budget constraint. We theoretically prove that our proposed framework is individual rational, budget balanced, truthful, and computationally efficient, and then we conduct a group of numerical simulations to demonstrate its correctness and effectiveness.
This work was supported in part by the Start-up Fund from Beijing Normal University under grant 310432104, the Start-up Fund from BNU-HKBU United International College under grant UICR0700018-22, the Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515110321 and 2022A1515010611, and Guangzhou Basic and Applied Basic Research Foundation under grant 202201010676.
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Guo, J., Ni, Q., Ding, X. (2022). A Decentralized Auction Framework with Privacy Protection in Mobile Crowdsourcing. In: Ni, Q., Wu, W. (eds) Algorithmic Aspects in Information and Management. AAIM 2022. Lecture Notes in Computer Science, vol 13513. Springer, Cham. https://doi.org/10.1007/978-3-031-16081-3_18
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DOI: https://doi.org/10.1007/978-3-031-16081-3_18
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