Abstract
The rapid use of smartphones and devices leads to the development of crowdsensing (CS) systems where a large crowd of participants can take part in performing data collecting tasks in large-scale distributed networks. Participants/users in such systems are usually selfish and have private information, such as costs and identities. Budget-feasible mechanism design, as a sub-field of auction theory, is a useful paradigm for crowdsensing, which naturally formulates the procurement scenario with buyers’ budgets being considered and allows the users to bid their private costs. Although the bidding behavior is well-regulated, budget-feasible mechanisms are still vulnerable to the Sybil attack where users may generate multiple fake identities to manipulate the system. Thus, it is vital to provide Sybil-proof budget-feasible mechanisms for crowdsensing. In this paper, we design a budget-feasible incentive mechanism which can guarantee truthfulness and deter Sybil attack. We prove that the proposed mechanism achieves individual rationality, truthfulness, budget feasibility, and Sybil-proofness. Extensive simulation results further validate the efficiency of the proposed mechanism.
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Acknowledgements
The work is supported in part by the National Key Research and Development Program of China under grant No. 2019YFB2102200, National Natural Science Foundation of China under Grant No. 61672154, 61672370, 61972086 and the Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant No. KYCX19_0089.
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Liu, X., Wu, W., Wang, W., Xu, Y., Wang, X., Cui, H. (2022). Budget-Feasible Sybil-Proof Mechanisms for Crowdsensing. In: Li, M., Sun, X. (eds) Frontiers of Algorithmic Wisdom. IJTCS-FAW 2022. Lecture Notes in Computer Science, vol 13461. Springer, Cham. https://doi.org/10.1007/978-3-031-20796-9_19
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DOI: https://doi.org/10.1007/978-3-031-20796-9_19
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