Abstract
In the mobile crowd sensing network, the existing research does not consider the completion quality factor of the task and the individualized difference of the participant’s ability. The location distribution of the participant will affect the quality of the task and the timeliness of obtaining the sensing task information. Participants in good positions can improve the completion rate of tasks, while participants with good reputation values can ensure the quality of the tasks. In this paper, the distance between the sensing point and the worker is used as one of the criteria for selecting the sensing task object. A utility-based location-distribution reverse auction incentive mechanism (ULDM) is proposed, which comprehensively considers budget constraints, worker’s reputation, and location characteristics in the sensing model, define the distance correlation and time correlation to evaluate the utility of the data collected by the winner. Finally the experimental results show that the successful package delivery rate, average delay and energy consumption are used as evaluation parameters, which improves the quality of task completion and suppresses the selfish behavior of selfish workers, which proves that ULDM has better incentive effect than reputation incentive mechanism.
This work is supported by Social Science Foundation of Liaoning Province (L18AXW001), Huzhou Public Welfare Application Research Project (2019GZ02).
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Liu, C., Wang, H., Wang, Y., Sun, D. (2020). Utility-Based Location Distribution Reverse Auction Incentive Mechanism for Mobile Crowd Sensing Network. In: Wen, S., Zomaya, A., Yang, L.T. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science(), vol 11945. Springer, Cham. https://doi.org/10.1007/978-3-030-38961-1_11
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DOI: https://doi.org/10.1007/978-3-030-38961-1_11
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