Abstract
Crowdsening plays an important role in spatiotemporal data collection by leveraging ubiquitous smart devices equipped with sensors. Considering rational and strategic device users, designing a truthful incentive mechanism is a crucial issue. Moreover, another key challenge is that there may not exist adequate participating users in reality. To encourage more users to participate, the social relationship among them can be leveraged, as users may be significantly influenced by their social friends. In this paper, we assume recruited users to diffuse uncompleted sensing tasks to their friends, and propose an efficient and truthful online incentive mechanism for a such social crowdsensing network. Specially, we model the time-varying social influence of a user by extending two metrics of node centrality used in social networks. In order to maximize the accumulated social welfare achieved by the network, we design a user selection algorithm and a payment determination algorithm respectively, in which payments given to participants not only depend on data qualities but also related with social influences. We theoretically prove that our mechanism achieves properties of computational efficiency, individual rationality, and truthfulness. Extensive simulations are conducted, and the results show the superiority of our mechanism.
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References
TalkingData. http://mi.talkingdata.com/app-rank.html?type=105060
Chen, Y., Li, B., Zhang, Q.: Incentivizing crowdsourcing systems with network effects. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Feng, Z., Zhu, Y., Zhang, Q., Ni, L.M., Vasilakos, A.V.: Trac: truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pp. 1231–1239. IEEE (2014)
Feng, Z., et al.: Towards truthful mechanisms for mobile crowdsourcing with dynamic smartphones. In: 2014 IEEE 34th International Conference on Distributed Computing Systems, pp. 11–20. IEEE (2014)
Gan, X., Li, Y., Wang, W., Fu, L., Wang, X.: Social crowdsourcing to friends: an incentive mechanism for multi-resource sharing. IEEE J. Sel. Areas Commun. 35(3), 795–808 (2017)
Jiang, L., Niu, X., Xu, J., Wang, Y., Wu, Y., Xu, L.: Time-sensitive and sybil-proof incentive mechanisms for mobile crowdsensing via social network. IEEE Access 6, 48156–48168 (2018)
Kauder, E.: History of Marginal Utility Theory, vol. 2238. Princeton University Press, Princeton (2015)
Lin, J., Li, M., Yang, D., Xue, G.: Sybil-proof online incentive mechanisms for crowdsensing. In: IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pp. 2438–2446. IEEE (2018)
Nie, J., Luo, J., Xiong, Z., Niyato, D., Wang, P.: A stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans. Wirel. Commun. 18(1), 724–738 (2018)
Nie, J., Luo, J., Xiong, Z., Niyato, D., Wang, P., Guizani, M.: An incentive mechanism design for socially aware crowdsensing services with incomplete information. IEEE Commun. Mag. 57(4), 74–80 (2019)
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, Cambridge (2007)
Sun, J.: An incentive scheme based on heterogeneous belief values for crowd sensing in mobile social networks. In: 2013 IEEE Global Communications Conference (GLOBECOM), pp. 1717–1722. IEEE (2013)
Xu, J., Bao, W., Gu, H., Xu, L., Jiang, G.: Improving both quantity and quality: Incentive mechanism for social mobile crowdsensing architecture. IEEE Access 6, 44992–45003 (2018)
Xu, J., Guan, C., Wu, H., Yang, D., Xu, L., Li, T.: Online incentive mechanism for mobile crowdsourcing based on two-tiered social crowdsourcing architecture. In: 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE (2018)
Yang, G., He, S., Shi, Z., Chen, J.: Promoting cooperation by the social incentive mechanism in mobile crowdsensing. IEEE Commun. Mag. 55(3), 86–92 (2017)
Acknowledgements
This work is supported by National Key R&D Program of China with No. 2017YFE0117500, NSFC (No. 61802245), the Shanghai Sailing Program (No. 18YF1408200), and STSCM (No. 19511121000). This work is also supported by the Open Project Program of Shanghai Key Laboratory of Data Science (No. 2020090600002).
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Fang, L., Liu, T., Gao, H., Cao, C., Li, W., Tong, W. (2021). An Efficient and Truthful Online Incentive Mechanism for a Social Crowdsensing Network. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_4
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DOI: https://doi.org/10.1007/978-3-030-67537-0_4
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