Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing

  • 326 Accesses

  • 10 Citations


Mobile crowd sensing has become an effective approach to meet the demand in large scale sensing applications. In mobile crowd sensing applications, incentive mechanisms are necessary to compensate the resource consumptions and manual efforts of smartphone users. In this paper, we focus on exploring budget feasible frameworks for a novel and practical mobile crowd sensing scenario, where the platform expects to maximize the continuous time interval coverage under budget constraint. We present the system model and formulate the budget feasible maximum continuous time duration problem for this scenario. We design two budget feasible frameworks: BFF-STI and BFF-BTI, and integrate MST as the truthful mechanism to maximize the social efficiency. Then we extend the budget feasible frameworks to the general case, in which each user can bid multiple time intervals simultaneously. We show the proposed budget feasible frameworks are computationally efficient, individually rational, truthful and budget feasible. Through extensive simulations, we demonstrate that our budget feasible frameworks are efficient with different parameter settings. The simulation results also show that BFF-STI has superiority in large scale mobile crowd sensing applications, while BFF-STI is more suitable for long-term sensing applications.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11


  1. 1.

    Ganti, R. K., Ye, F., & Lei, H. (2011). Mobile crowdsensing: Current state and future challenges. IEEE Communications Magazine, 49(11), 32–39.

  2. 2.

    Consolvo, S., et al. (2008). Activity sensing in the wild: A field trial of Ubifit Garden. In Proceedings of the 26th annual ACM SIGCHI conference on human factors in computer systems (pp. 1797–1806).

  3. 3.

    Miluzzo, E., et al. (2008). Sensing meets mobile social networks: The design, implementation, and evaluation of the CenceMe Application. In Proceedings of the 6th ACM SenSys (pp. 337–350).

  4. 4.

    Mun, M., et al. (2009). Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In Proceedings of 7th ACM MobiSys (pp. 55–68).

  5. 5.

    Thiagarajan, A., et al. (2009). VTrack: Accurate, energy-aware traffic delay estimation using mobile phones. In Proceedings of the 7th ACM SenSys (pp. 85–98).

  6. 6.

    UC Berkeley/Nokia/NAVTEQ, “Mobile Millennium”, http://traffic.berkeley.edu/.

  7. 7.

    Dong, W., Lepri, B., & Pentland, S. (2012). Tracking co-evolution of behavior and relationships with mobile phones. Tsinghua Science and Technology, 17(2), 136–151.

  8. 8.

    Koukoumidis, E., Peh, L., & Martonosi, M. (2011). SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory. In Proceedings of the ACM ninth international conference on mobile systems, applications, and services (MobiSys) (pp. 127–140).

  9. 9.

    Costa, C., Laoudias, C., Zeinalipour-Yazti, D., & Gunopulos, D. (2011). Smarttrace: Finding similar trajectories in smartphone networks without disclosing the traces. In Proceedings of the IEEE 27th international conference on data engineering (ICDE) (pp. 1288–1291).

  10. 10.

    Liu, Y., Zhao, Y., Chen, L., Pei, J., & Han, J. (2012). Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. IEEE Transactions on Parallel and Distributed Systems, 23(11), 2138–2149.

  11. 11.

    Yang, Z., Wu, C., & Liu, Y. (2012). Locating in fingerprint space: Wireless indoor localization with little human intervention. In Proceedings of the ACM MobiCom (pp. 269–280).

  12. 12.

    Yang, D., Xue, G., Fang, X., & Tang, J. (2012). Crowdsourcing to Smartphones: Incentive mechanism design for mobile phone sensing. In Proceedings of the ACM MobiCom (pp. 173–184).

  13. 13.

    Zhao, D., Li, X., & Ma, H. (2014). How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint. In Proceedings of the IEEE INFOCOM (pp. 1213–1221).

  14. 14.

    Singer, Y., & Mittal, M. (2013). Pricing mechanisms for crowdsourcing markets. In Proceedings of the 22nd international conference on World Wide Web (pp. 1157–1166).

  15. 15.

    Feng, Z., Zhu, Y., Zhang, Q., Ni, L. M., & Vasilakos, A.V. (2014). TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing. In Proceedings of the IEEE INFOCOM (pp. 1231–1239).

  16. 16.

    Subramanian, A., Kanth, G. S., Moharir, S., & Vaze, R. (2015). Online incentive mechanism design for smartphone crowd-sourcing. In Proceedings of the WiOpt (pp. 403–410).

  17. 17.

    Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., & Li, X. (2014). Free market of crowdsourcing: incentive mechanism design for mobile sensing. IEEE Transactions on Parallel and Distributed Systems, 25(12), 3190–3200.

  18. 18.

    Singer, Y. (2010). Budget feasible mechanisms. In Proceedings of the IEEE FOCS (pp. 765–774).

  19. 19.

    Koutsopoulos, I. (2013). Optimal incentive-driven design of participatory sensing systems. In Proceedings of the IEEE INFOCOM (pp. 1402–1410).

  20. 20.

    Zhang, X., Xue, G., Yu, R., Yang, D., & Tang, J. (2015). Truthful incentive mechanisms for crowdsourcing. In Proceedings of the IEEE INFOCOM (pp. 2830–2838).

  21. 21.

    Lee, J., & Hoh, B. (2010). Sell your experiences: a market mechanism based incentive for participatory sensing. In Proceedings of the IEEE PerCom (pp. 60–68).

  22. 22.

    Zhou, P., Zheng Y., & Li, M. (2012). How long to wait?: Predicting bus arrival time with mobile phone based participatory sensing. In Proceedings of the ACM MobiSys (pp. 1228–1240).

  23. 23.

    Rana, R., Chou, C., Kanhere, S., Bulusu, N., & Hu, W. (2010). Earphone: “An end-to-end participatory urban noise mapping”. In Proceedings of the ACM/IEEE IPSN (pp. 105–116).

  24. 24.

    Carrapetta, J., Youdale, N., Chow, A., & Sivaraman, V. Haze watch project. http://www.hazewatch.unsw.edu.au/.

  25. 25.

    Xu, J., Xiang, J., & Yang, D. (2015). Incentive mechanisms for time window dependent tasks in mobile crowdsensing. IEEE Transactions on Wireless Communications, 14(11), 6353–6364.

  26. 26.

    Adams, C., Cain, P., Pinkas, D., & Zuccherato, R. (2001). Internet X.509 public key infrastructure time-stamp protocol (TSP). IETF RFC3161. http://tools.ietf.org/pdf/rfc3161.pdf.

  27. 27.

    Blumrosen, L., & Nisan, N. (2007). Combinatorial auctions (a survey). In N. Nisan, T. Roughgarden, E. Tardos, & V. Vazirani (Eds.), Algorithmic game theory (pp. 267–298). Cambridge: Cambridge University Press.

  28. 28.

    Cormen, T. (2009). Introduction to algorithms. Cambridge: MIT Press.

  29. 29.

    Amicia, R., Bonolaa, M., Bracciale, L., Rabuffi, A., Loretia, P., & Bianchi, G. (2014). Performance assessment of an epidemic protocol in VANET using real traces. Procedia Computer Science, 40, 92–99.

  30. 30.

    Peng, D., Wu, F., & Chen, G. (2015). Pay as how well you do: A quality based incentive mechanism for crowdsensing. In Proceedings of the ACM MobiHoc (pp. 177–186).

  31. 31.

    Han, K., Zhang, C., Luo, J., Hu, M., & Veeravalli, B. (2016). Truthful scheduling mechanisms for powering mobile crowdsensing. IEEE Transactions on Computers, 65(1), 294–307.

  32. 32.

    Zhang, Q., Wen, Y., & Tian, X., et al. (2015). Incentivize crowd labeling under budget constraint. In Proceedings of the IEEE INFOCOM (pp. 2812–2820).

Download references


This work is sponsored in part by NSFC (No. 61472193, 61472192, 61373139), The natural science foundation of Jiangsu Province (No. BK20141429, BK20130852), Scientific and Technological Support Project (Society) of Jiangsu Province (No. BE2013666), CCF-Tencent Open Research Fund (No. CCF-Tencent RAGR20150107), China Postdoctoral Science Foundation (No. 2014M562662), Jiangsu Postdoctoral Science Foundation (No. 1402223C), Independent Research Project of Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks (No. WSNLBZY201524), NUPTSF (No. NY215098) and the “1311” Talent Project of NJUPT.

Author information

Correspondence to Jia Xu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xu, J., Xiang, J. & Li, Y. Incentivize maximum continuous time interval coverage under budget constraint in mobile crowd sensing. Wireless Netw 23, 1549–1562 (2017). https://doi.org/10.1007/s11276-016-1244-9

Download citation


  • Mobile crowd sensing
  • Incentive mechanism
  • Auction
  • Budget feasible