Abstract
Mobile Crowd Sensing is an emerging paradigm, which engages ordinary mobile device users to efficiently collect data and share sensed information using mobile applications. The data collection of participants consumes computing, storage and communication resources; thus, it is necessary to give rewards to users who contribute their private data for sensing tasks. Furthermore, since the budget of the sensing task is limited, the Service Provider (SP) needs to select a set of participants such that the total utility of their sensing data can be maximized, and their bid price for sensing data can be satisfied without exceeding the total budget. In this paper, firstly, we claim that the total data utility of a set of participants within a certain area should be calculated according to the data quality of each participant and the location coverage of the sensing data. Secondly, a participant selection scheme has been proposed, which determines a set of participants with maximum total data utility under the budget constraint, and shows that it is a Quadratic Integer Programming problem. Simulations have been conducted to solve the selection problem. The Simulation results demonstrate the effectiveness of the proposed scheme.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61972081, 61672151, 61772340, 61420106010), National Key Research and Development Project (Grant No. 2016QY12Z2103-2), Shanghai Rising-Star Program (Grant No.17QA1400100), National Key R&D Program of China (Grant No. 2018YFC1900700), Shanghai Municipal Natural Science Foundation (Grant No. 18ZR1401200), the Fundamental Research Funds for the Central Universities (Grant No. EG2018028), DHU Distinguished Young Professor Program and 2017 CCF-IFAA Research Fund.
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Azhar, S., Chang, S., Liu, Y., Tao, Y., Liu, G., Sun, D. (2020). Utility-Aware Participant Selection with Budget Constraints for Mobile Crowd Sensing. In: Chu, X., Jiang, H., Li, B., Wang, D., Wang, W. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Systems. QShine 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-38819-5_3
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DOI: https://doi.org/10.1007/978-3-030-38819-5_3
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