iMac: Strategy-Proof Incentive Mechanism for Mobile Crowdsourcing

  • Zhenni Feng
  • Yanmin Zhu
  • Lionel M. Ni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7992)


Mobile crowdsourcing with smartphones advocates the cooperative effort of mobile smartphones to perform a joint distributed sensing task, which has gained growing importance for its potential to support a wide spectrum of large-scale sensing applications. Smartphone users in the real world are strategic and rational. Thus, one crucial problem in mobile crowdsourcing with smartphones is to stimulate cooperation from smartphone users. Several major challenges should be addressed. First, the actual cost incurred for a sensing task is private information and unknown to other users and the mobile crowdsourcing platform. Second, smartphone users are strategic, which suggest a user may deliberately misreport its cost (different from the real cost) in order to maximize its own utility. In this paper, we propose a strategy-proof incentive mechanism called iMac based on the Vickrey-Clarke-Groves (VCG) mechanism. The main idea of iMac is to stimulate smartphone users to truthfully disclose their real costs in spite of strategic behavior of the users. iMac introduces two main components. The first component determines the allocation of a sensing task to smartphone users given the user costs. And the second component decides the payment to each user. We prove that iMac can successfully produce a unique Nash equilibrium at which each user truthfully discloses the cost. Meanwhile, the minimization of the social cost is achieved. Simulation results demonstrate iMac achieves the desired design objectives and the overpayment is modest.


Strategy-Proof Mobile Crowdsourcing Incentive Mechanism 


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  1. 1.
    Nath, S.: Ace: exploiting correlation for energy-efficient and continuous context sensing. In: Proc. ACM MobiSys. (2012)Google Scholar
  2. 2.
    Ganti, R., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine 49(11), 32–39 (2011)CrossRefGoogle Scholar
  3. 3.
    Ra, M.R., Liu, B., La Porta, T.F., Govindan, R.: Medusa: a programming framework for crowd-sensing applications. In: Proc. ACM MobiSys. (2012)Google Scholar
  4. 4.
    Chon, Y., Lane, N.D., Li, F., Cha, H., Zhao, F.: Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proc. of the 2012 ACM Conference on Ubiquitous Computing (2012)Google Scholar
  5. 5.
    Rai, A., Chintalapudi, K.K., Padmanabhan, V.N., Sen, R.: Zee: zero-effort crowdsourcing for indoor localization. In: Proc. ACM MOBICOM (2012)Google Scholar
  6. 6.
    Yang, D., Xue, G., Fang, X., Tang, J.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proc. ACM MOBICOM (2012)Google Scholar
  7. 7.
    Anderegg, L., Eidenbenz, S.: Ad hoc-vcg: A truthful and cost-efficient routing protocol for mobile ad hoc networks with selfish agents (2003)Google Scholar
  8. 8.
    Wang, W., Li, X.Y., Wang, Y.: Truthful multicast routing in selfish wireless networks. In: Proc. ACM MOBICOM (2004)Google Scholar
  9. 9.
    Dang, T., Chi Feng, W., Bulusu, N.: Zoom: A multi-resolution tasking framework for crowdsourced geo-spatial sensing. In: Proc. IEEE INFOCOM (2011)Google Scholar
  10. 10.
    Tamilin, A., Carreras, I., Ssebaggala, E., Opira, A., Conci, N.: Context-aware mobile crowdsourcing. In: Proc. of the 2012 ACM Conference on Ubiquitous Computing (2012)Google Scholar
  11. 11.
    Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic game theory. Cambridge University Press (2007)Google Scholar
  12. 12.
    Jayaraman, P., Sinha, A., Sherchan, W., Krishnaswamy, S., Zaslavsky, A., Haghighi, P.D., Loke, S., Do, M.T.: Here-n-now: A framework for context-aware mobile crowdsensing. In: Proc. of the Tenth International Conference on Pervasive Computing (2012)Google Scholar
  13. 13.
    Xiao, Y., Simoens, P., Pillai, P., Ha, K., Satyanarayanan, M.: Lowering the barriers to large-scale mobile crowdsensing. In: Proc. of the 14th Workshop on Mobile Computing Systems and Applications (2013)Google Scholar
  14. 14.
    Sherchan, W., Jayaraman, P.P., Krishnaswamy, S., Zaslavsky, A., Loke, S., Sinha, A.: Using on-the-move mining for mobile crowdsensing. In: Proc. of the 13th IEEE International Conference on Mobile Data Management, MDM 2012 (2012)Google Scholar
  15. 15.
    Fudenberg, D., Tirole, J.: Game theory. MIT Press (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhenni Feng
    • 2
  • Yanmin Zhu
    • 2
    • 1
  • Lionel M. Ni
    • 3
  1. 1.Shanghai Key Lab of Scalable Computing and SystemsShanghai Jiao Tong UniversityChina
  2. 2.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityChina
  3. 3.Hong Kong University of Science and TechnologyHong Kong

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