Abstract
In recent years, with the popularization and widespread use of mobile smart devices, Crowdsensing has gradually become one of the current research hotspots. The incentive mechanism is an important issue in the research of Crowdsensing. The existing incentive mechanism usually relies on the bank-like trustworthy center, but the trustworthy center has the problem of system trust deficiency because of its opaque control and vulnerable to attack. Blockchain is decentralized, open, tamper-evident, and anonymous, which can be used as a solution to the trust deficit problem in Crowdsensing incentive mechanism. A blockchain-based incentive mechanism is proposed for Crowdsensing, which adopts a blockchain-secured distributed architecture, and the sensing users and data demanders participate in the sensing tasks as nodes in the blockchain. This incentive mechanism uses a reverse auction model to screen out irrational offer users and increase the execution efficiency of the sensing task. A Softmax regression algorithm is used to implement miners’ verification of the quality grade of sensing data, and the value of data is calculated by the quality grade of user offers and data to encourage users to upload high quality and reliable data. Finally, the accuracy of the classification algorithm is compared through simulation experiments, and the security, efficiency and feasibility of the system are analyzed.
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Acknowledgement
This work is supported by the Key Research and Development Project of Sichuan Province (No.2021YFSY0012, No. 2020YFG0307, No.2021YFG0332), the Key Research and Development Project of Cheng du (No. 2019-YF05–02028-GX), the Innovation Team of Quantum Security Communication of Sichuan Province (No.17TD0009), the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province (No. 2016120080102643).
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Jiang, Q. et al. (2022). Blockchain-Based Efficient Incentive Mechanism in Crowdsensing. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_10
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DOI: https://doi.org/10.1007/978-3-031-06791-4_10
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