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Fair Incentive Mechanism for Mobile Crowdsensing

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Incentive Mechanism for Mobile Crowdsensing

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

In this chapter, we jointly address practical issues in the incentive mechanism for MCS to fairly incentivize high-quality users’ participation, like (1) the platform has no knowledge about users’ sensing qualities beforehand due to their private information. (2) The platform needs users’ continuous participation in the long run, which results in fairness requirements. (3) It is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. Particularly, we propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multi-armed bandits (CVMAB) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves sublinear regret performance to learn unknown quality with fairness and privacy guarantee.

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References

  1. Liu, Y., Kong, L., Chen, G.: Data-oriented mobile crowdsensing: a comprehensive survey. IEEE Commun. Surv. Tuts. 21(3), 2849–2885 (2019)

    Article  Google Scholar 

  2. Guo, B., Liu, Y., Wang, L., Li, V.O.K., Jacqueline, C.K., Yu, Z.: Task allocation in spatial crowdsourcing: current state and future directions. IEEE Internet Things J. 5, 1749–1764 (2018)

    Article  Google Scholar 

  3. Bardin, N.: Waze (2008). https://www.waze.com

  4. Chen, H., Li, F., Hei, X., Wang, Y.: Crowdx: enhancing automatic construction of indoor floorplan with opportunistic encounters. Proc. ACM Interact. Mobile Wearable Ubiquit. Technol. 2(4), 1–21 (2018)

    Article  Google Scholar 

  5. Wang, L., Zhang, D., Pathak, A., Chen, C., Xiong, H., Yang, D., Wang, Y.: CCS-TA: quality-guaranteed online task allocation in compressive crowdsensing. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 683–694 (2015)

    Google Scholar 

  6. Li, J., Cai, Z., Yan, M., Li, Y.: Using crowdsourced data in location-based social networks to explore influence maximization. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 1–9 (2016)

    Google Scholar 

  7. Li, H., Li, T., Li, F., Yang, S., Wang, Y.: Multi-expertise aware participant selection in mobile crowd sensing via online learning. In: IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 433–441 (2018)

    Google Scholar 

  8. Liu, W., Yang, Y., Wang, E., Wu, J.: Dynamic user recruitment with truthful pricing for mobile crowdsensing. In: IEEE International Conference on Computer Communications (INFOCOM) (2020)

    Google Scholar 

  9. Gao, G., Wu, J., Xiao, M., Chen, G.: Combinatorial multi-armed bandit based unknown worker recruitment in heterogeneous crowdsensing. In: IEEE International Conference on Computer Communications (INFOCOM) (2020)

    Google Scholar 

  10. Gao, L., Hou, F., Huang, J.: Providing long-term participation incentive in participatory sensing. In: IEEE International Conference on Computer Communications (INFOCOM) (2015)

    Google Scholar 

  11. Han, K., Huang, H., Luo, J.: Quality-aware pricing for mobile crowdsensing. IEEE/ACM Trans. Netw. 26(4), 1728–1741 (2018)

    Article  Google Scholar 

  12. Lin, J., Yang, D., Li, M., Xu, J., Xue, G.: Bidguard: a framework for privacy-preserving crowdsensing incentive mechanisms. In: IEEE Conference on Communications and Network Security (CNS), pp. 145–153 (2016)

    Google Scholar 

  13. Li, T., Qiu, Z., Cao, L., Li, H., Guo, Z., Li, F., Shi, X., Wang, Y.: Participant grouping for privacy preservation in mobile crowdsensing over hierarchical edge clouds. In: IEEE Proceedings of the 37th International Performance Computing and Communications Conference (IPCCC), pp. 1–8. IEEE, Piscataway (2018)

    Google Scholar 

  14. Zhang, C., Zhu, L., Xu, C., Liu, X., Sharif, K.: Reliable and privacy-preserving truth discovery for mobile crowdsensing systems. IEEE Trans. Depend. Secur. Comput. 18, 1245–1260 (2019)

    Google Scholar 

  15. Liu, Y., Wang, H., Peng, M., Guan, J., Wang, Y.: An incentive mechanism for privacy-preserving crowdsensing via deep reinforcement learning. IEEE Internet Things J. 8(10), 8616–8631 (2020)

    Article  Google Scholar 

  16. Chen, L., Xu, J., Lu, Z.: Contextual combinatorial multi-armed bandits with volatile arms and submodular reward. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 3247–3256 (2018)

    Google Scholar 

  17. Jin, H., Su, L., Xiao, H., Nahrstedt, K.: Inception: incentivizing privacy-preserving data aggregation for mobile crowd sensing systems. In: ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 341–350 (2016)

    Google Scholar 

  18. Jin, H., Su, L., Ding, B., Nahrstedt, K., Borisov, N.: Enabling privacy-preserving incentives for mobile crowd sensing systems. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 344–353. IEEE, Piscataway (2016)

    Google Scholar 

  19. Wang, X., Liu, Z., Tian, X., Gan, X., Guan, Y., Wang, X.: Incentivizing crowdsensing with location-privacy preserving. IEEE Trans. Wireless Commun. 16(10), 6940–6952 (2017)

    Article  Google Scholar 

  20. Zhang, X., Liang, L., Luo, C., Cheng, L.: Privacy-preserving incentive mechanisms for mobile crowdsensing. IEEE Pervasive Comput. 17(3), 47–57 (2018)

    Article  Google Scholar 

  21. Wang, Z., Pang, X., Hu, J., Liu, W., Wang, Q., Li, Y., Chen, H.: When mobile crowdsensing meets privacy. IEEE Commun. Mag. 57(9), 72–78 (2019)

    Article  Google Scholar 

  22. Wang, Z., Li, J., Hu, J., Ren, J., Li, Z., Li, Y.: Towards privacy-preserving incentive for mobile crowdsensing under an untrusted platform. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 2053–2061 (2019)

    Google Scholar 

  23. Zhao, B., Tang, S., Liu, X., Zhang, X.: Pace: privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing. IEEE Trans. Mobile Comput. 20(5), 1924–1939 (2020)

    Article  Google Scholar 

  24. Wang, L., Cao, Z., Zhou, P., Zhao, X.: Towards a smart privacy-preserving incentive mechanism for vehicular crowd sensing. Securi. Commun. Netw. 2021, 5580089 (2021)

    Google Scholar 

  25. Li, F., Liu, J., Ji, B.: Combinatorial sleeping bandits with fairness constraints. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 1702–1710 (2019)

    Google Scholar 

  26. Xiao, M., Gao, G., Wu, J., Zhang, S., Huang, L.: Privacy-preserving user recruitment protocol for mobile crowdsensing. IEEE/ACM Trans. Netw. 28(2), 519–532 (2020)

    Article  Google Scholar 

  27. Yu, H., Iosifidisy, S., Biying, L., Huang, J.: Market your venue with mobile applications: collaboration of online and offline businesses. In: IEEE International Conference on Computer Communications (INFOCOM) (2018)

    Google Scholar 

  28. Chen, W., Wang, Y., Yuan, Y.: Combinatorial multi-armed bandit: general framework and applications. In: Proceedings of the 30th International Conference on Machine Learning (ICML), pp. 151–159. ACM, New York (2013)

    Google Scholar 

  29. Chen, W., Hu, W., Li, F., Li, J., Liu, Y., Lu, P.: Combinatorial multi-armed bandit with general reward functions. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1659–1667. MIT Press, Cambridge (2016)

    Google Scholar 

  30. Xiong, H., Zhang, D., Chen, G., Wang, L., Gauthier, V., Barnes, L.E.: ICrowd: near-optimal task allocation for piggyback crowdsensing. IEEE Trans. Mobile Comput. 15(8), 2010–2022 (2015)

    Article  Google Scholar 

  31. Liu, Y., Guo, B., Wang, Y., Wu, W., Yu, Z., Zhang, D.: Taskme: multi-task allocation in mobile crowd sensing. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 403–414 (2016)

    Google Scholar 

  32. Tao, X., Song, W.: Location-dependent task allocation for mobile crowdsensing with clustering effect. IEEE Internet Things J. 6(1), 1029–1045 (2018)

    Article  Google Scholar 

  33. Zhou, P., Chen, W., Ji, S., Jiang, H., Yu, L., Wu, D.: Privacy-preserving online task allocation in edge-computing-enabled massive crowdsensing. IEEE Internet Things J. 6(5), 7773–7787 (2019)

    Article  Google Scholar 

  34. Karaliopoulos, M., Telelis, O., Koutsopoulos, I.: User recruitment for mobile crowdsensing over opportunistic networks. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2254–2262. IEEE, Piscataway (2015)

    Google Scholar 

  35. Wang, E., Yang, Y., Wu, J., Liu, W., Wang, X.: An efficient prediction-based user recruitment for mobile crowdsensing. IEEE Trans. Mobile Comput. 17(1), 16–28 (2017)

    Article  Google Scholar 

  36. Yang, D., Xue, G., Fang, X., Tang, J.: Incentive mechanisms for crowdsensing: crowdsourcing with smartphones. IEEE/ACM Trans. Netw. 24(3), 1732–1744 (2016)

    Article  Google Scholar 

  37. Cheung, M.H., Hou, F., Huang, J.: Make a difference: diversity-driven social mobile crowdsensing. In IEEE International Conference on Computer Communications (INFOCOM) (2017)

    Google Scholar 

  38. Xiao, L., Li, Y., Han, G., Dai, H., Poor, H.V.: A secure mobile crowdsensing game with deep reinforcement learning. IEEE Trans. Inf. Forensics Secur.13(1), 35–47 (2018)

    Article  Google Scholar 

  39. Chen, Y., Li, B., Zhang, Q.: Incentivizing crowdsourcing systems with network effects. In: IEEE International Conference on Computer Communications (INFOCOM) (2016)

    Google Scholar 

  40. Zhang, Y., Gu, Y., Pan, M., Tran, N.H., Dawy, Z., Han, Z.: Multi-dimensional incentive mechanism in mobile crowdsourcing with moral hazard. IEEE Trans. Mobile Comput. 17(3), 604–616 (2018)

    Article  Google Scholar 

  41. Jin, H., Guo, H., Su, L., Nahrstedt, K., Wang, X.: Dynamic task pricing in multi-requester mobile crowd sensing with markov correlated equilibrium. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 1063–1071 (2019)

    Google Scholar 

  42. Duan, Z., Li, W., Cai, Z.: Distributed auctions for task assignment and scheduling in mobile crowdsensing systems. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 635–644 (2017)

    Google Scholar 

  43. Duan, Z., Li, W., Zheng, X., Cai, Z.: Mutual-preference driven truthful auction mechanism in mobile crowdsensing. In: IEEE International Conference on Distributed Computing Systems (ICDCS), pp. 1233–1242 (2019)

    Google Scholar 

  44. Cai, Z., Duan, Z., Li, W.: Exploiting multi-dimensional task diversity in distributed auctions for mobile crowdsensing. IEEE Trans. Mobile Comput. 20(8), 2576–2591 (2020)

    Article  Google Scholar 

  45. Zhan, Y., Xia, Y., Zhang, J., Li, T., Wang, Y.: An incentive mechanism design for mobile crowdsensing with demand uncertainties. Inf. Sci. 528, 1–16 (2020)

    Article  MathSciNet  Google Scholar 

  46. Zhan, Y., Liu, C.H., Zhao, Y., Zhang, J., Tang, J.: Free market of multi-leader multi-follower mobile crowdsensing: an incentive mechanism design by deep reinforcement learning. IEEE Trans. Mobile Comput. 19(10), 2316–2329 (2019)

    Article  Google Scholar 

  47. Yu, H., Wei, E., Berry, R.A.: Monetizing mobile data via data rewards. IEEE J. Sel. Areas Commun. 38, 782–792 (2020)

    Article  Google Scholar 

  48. Neely, M.J.: Stochastic network optimization with application to communication and queueing systems. Synth. Lect. Commun. Netw. 3(1), 1–211 (2010)

    Article  Google Scholar 

  49. Bassily, R., Smith, A.: Local, private, efficient protocols for succinct histograms. In: Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing, pp. 127–135 (2015)

    Google Scholar 

  50. Chen, X., Zheng, K., Zhou, Z., Yang, Y., Chen, W., Wang, L.: (Locally) differentially private combinatorial semi-bandits. In: International Conference on Machine Learning (ICML) (2020)

    Google Scholar 

  51. Sun, L., Pang, H., Gao, L.: Joint sponsor scheduling in cellular and edge caching networks for mobile video delivery. IEEE Trans. Multimedia 20(12), 3414–3427 (2018)

    Article  Google Scholar 

  52. Krause, A., Golovin, D.: Submodular Function Maximization. Elsevier, Amsterdam (2014)

    Book  Google Scholar 

  53. Buchbinder, N., Feldman, M., Naor, J.S., Schwartz, R.: Submodular maximization with cardinality constraints. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1433–1452. SIAM, Philadelphia (2014)

    Google Scholar 

  54. Joulani, P., Gyorgy, A., Szepesvári, C.: Online learning under delayed feedback. In: International Conference on Machine Learning (ICML), pp. 1453–1461 (2013)

    Google Scholar 

  55. Bubeck, S., Cesa-Bianchi, N., et al.: Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Found. Trends Mach. Learn. 5(1), 1–122 (2012)

    Article  Google Scholar 

  56. Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

    Youqi Li, Fan Li, Song Yang & Chuan Zhang

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  1. Youqi Li
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  2. Fan Li
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  4. Chuan Zhang
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Li, Y., Li, F., Yang, S., Zhang, C. (2024). Fair Incentive Mechanism for Mobile Crowdsensing. In: Incentive Mechanism for Mobile Crowdsensing. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-99-6921-0_3

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  • DOI: https://doi.org/10.1007/978-981-99-6921-0_3

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