Abstract
Recent years have witnessed the advance of mobile crowd sensing (MCS) system. How to meet the demands of task time requirements and obtain high-quality data with little expense has become a critical problem. We focus on exploring incentive mechanisms for a practical scenario, where the tasks are time window dependent. An important indicator, “quality of user’s data (QOD)” is also considered. First, we design a prediction model based on user history data (p-QOD), to calculate the next time of the user’s QOD. Second, we design a dynamic programming algorithm based on time windows and p-QOD, to ensure all of the task time windows are covered, as well as minimizing the platform’s cost. Finally, we determine the payment for each user through a Vickrey–Clarke–Groves auction (VCG) considering the user’s true data quality (t-QOD), which is based on their submission time. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, fairness, and individual rationality.
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Acknowledement
The research is supported by “National Natural Science Foundation of China” (No. 61572526) and “Innovation Project for Graduate Students in Central South University” (No. 502211708).
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Yan, H., Zhao, M. (2019). Time-Based Quality-Aware Incentive Mechanism for Mobile Crowd Sensing. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_10
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DOI: https://doi.org/10.1007/978-3-030-15093-8_10
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