Abstract
With the development of intelligent mobile devices, spatial crowdsourcing (SC) has become popular recently, and SC platforms cooperation has attracted people’s attention, which can enlarge the whole social-economic benefits. The way to encourage platforms to take part in cooperation is essential. A viable method is to make each platform in cooperation get higher revenue via allocating the revenue generated by cooperation. However, the current work lack studies on the revenue allocation in SC platform cooperation. In this paper, based on cooperative game theory, we propose some ideal properties that the revenue allocation method should satisfy. Then, based on Shapley value, we propose a fair revenue allocation method named SRA, measured by the marginal contribution of each platform. Given the exponential complexity of the method, we propose an efficient approximation method, Coalition-based Shapley value Revenue Allocation (CSRA). Extensive experimental results verify the effectiveness and efficiency of our algorithms.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61572336, 61632016, 62072323), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20211307, BK20191420), the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China (Grant Nos. 18KJA520010, 19KJA610002), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Wang, X., Liu, A., Liu, S., Fang, J., Xu, J. (2021). Incentive Mechanism for Spatial Crowdsourcing Cooperation: A Fair Revenue Allocation Method. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, Hy. (eds) Service-Oriented Computing. ICSOC 2021. Lecture Notes in Computer Science(), vol 13121. Springer, Cham. https://doi.org/10.1007/978-3-030-91431-8_49
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DOI: https://doi.org/10.1007/978-3-030-91431-8_49
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