Utility-Aware Participant Selection with Budget Constraints for Mobile Crowd Sensing

  • Shanila Azhar
  • Shan ChangEmail author
  • Ye Liu
  • Yuting Tao
  • Guohua Liu
  • Donghong Sun
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 300)


Mobile Crowd Sensing is an emerging paradigm, which engages ordinary mobile device users to efficiently collect data and share sensed information using mobile applications. The data collection of participants consumes computing, storage and communication resources; thus, it is necessary to give rewards to users who contribute their private data for sensing tasks. Furthermore, since the budget of the sensing task is limited, the Service Provider (SP) needs to select a set of participants such that the total utility of their sensing data can be maximized, and their bid price for sensing data can be satisfied without exceeding the total budget. In this paper, firstly, we claim that the total data utility of a set of participants within a certain area should be calculated according to the data quality of each participant and the location coverage of the sensing data. Secondly, a participant selection scheme has been proposed, which determines a set of participants with maximum total data utility under the budget constraint, and shows that it is a Quadratic Integer Programming problem. Simulations have been conducted to solve the selection problem. The Simulation results demonstrate the effectiveness of the proposed scheme.


Mobile Crowd Sensing Utility Budget Data quality Incentive 



This work was supported in part by the National Natural Science Foundation of China (Grant No. 61972081, 61672151, 61772340, 61420106010), National Key Research and Development Project (Grant No. 2016QY12Z2103-2), Shanghai Rising-Star Program (Grant No.17QA1400100), National Key R&D Program of China (Grant No. 2018YFC1900700), Shanghai Municipal Natural Science Foundation (Grant No. 18ZR1401200), the Fundamental Research Funds for the Central Universities (Grant No. EG2018028), DHU Distinguished Young Professor Program and 2017 CCF-IFAA Research Fund.


  1. 1.
    Ericsson: Ericsson Mobility Report, pp. 1–3, August 2019Google Scholar
  2. 2.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)CrossRefGoogle Scholar
  3. 3.
    Wang, X., et al.: A city-wide real-time traffic management system: enabling crowdsensing in social internet of vehicles. IEEE Commun. Mag. 56(9), 19–25 (2018)CrossRefGoogle Scholar
  4. 4.
    Kalogiros, L.A., Lagouvardos, K., Nikoletseas, S., Papadopoulos, N., Tzamalis, P.: Allergymap: a hybrid mHealth mobile crowdsensing system for allergic diseases epidemiology: a multidisciplinary case study. In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 597–602. IEEE, March 2018Google Scholar
  5. 5.
    Roitman, H., Mamou, J., Mehta, S., Satt, A., Subramaniam, L.V.: Harnessing the crowds for smart city sensing. In: Proceedings of the 1st International Workshop on Multimodal Crowd Sensing, pp. 17–18. ACM, November 2012Google Scholar
  6. 6.
    Schobel, J., Pryss, R., Reichert, M.: Using smart mobile devices for collecting structured data in clinical trials: results from a large-scale case study. In: 2015 IEEE 28th International Symposium on Computer-Based Medical Systems, pp. 13–18. IEEE, June, 2015Google Scholar
  7. 7.
    Yang, D., Xue, G., Fang, X., Tang, J.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, pp. 173–184. ACM, August 2012Google Scholar
  8. 8.
    Jaimes, L., Vergara-Laurens, I., Labrador, M.A.: A location-based incentive mechanism for participatory sensing systems with budget constraints. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2012), pp. 103–108, March 2012Google Scholar
  9. 9.
    Mendez, D., Labrador, M., Ramachandran, K.: Data interpolation for participatory sensing systems. Pervasive Mob. Comput. 9(1), 132–148 (2013)CrossRefGoogle Scholar
  10. 10.
    Reddy, S., Estrin, D., Srivastava, M.: Recruitment framework for participatory sensing data collections. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 138–155. Springer, Heidelberg (2010). Scholar
  11. 11.
    Chon, Y., Lane, N.D., Kim, Y., Zhao, F., Cha, H.: Understanding the coverage and scalability of place-centric crowdsensing. In: Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–12. ACM, September 2013Google Scholar
  12. 12.
    Zhao, D., Ma, H., Liu, L.: Energy-efficient opportunistic coverage for people-centric urban sensing. Wirel. Netw. 20(6), 1461–1476 (2014)CrossRefGoogle Scholar
  13. 13.
    Xiong, H., Zhang, D., Wang, L., Chaouchi, H.: EMC 3: energy-efficient data transfer in mobile crowdsensing under full coverage constraint. IEEE Trans. Mob. Comput. 14(7), 1355–1368 (2014)CrossRefGoogle Scholar
  14. 14.
    Sun, J., Pei, Y., Hou, F., Ma, S.: Reputation-aware incentive mechanism for participatory sensing. IET Commun. 11(13), 1985–1991 (2017)CrossRefGoogle Scholar
  15. 15.
    Lee, J.S., Hoh, B.: Sell your experiences: a market mechanism based incentive for participatory sensing. In: 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 60–68. IEEE, March 2010Google Scholar
  16. 16.
    Chen, C., Wang, Y.: SPARC: strategy-proof double auction for mobile participatory sensing. In: 2013 International Conference on Cloud Computing and Big Data, pp. 133–140. IEEE, December 2013Google Scholar
  17. 17.
    Jaimes, L.G., Vergara-Laurens, I., Labrador, M.A.: A location-based incentive mechanism for participatory sensing systems with budget constraints. In: 2012 IEEE International Conference on Pervasive Computing and Communications, pp. 103–108. IEEE, March 2012Google Scholar
  18. 18.
    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, April 2014Google Scholar
  19. 19.
    He, S., Shin, D.H., Zhang, J., Chen, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: IEEE INFOCOM 2014-IEEE Conference on Computer Communications, pp. 745–753. IEEE, April 2014Google Scholar
  20. 20.
    Billionnet, A., Elloumi, S., Plateau, M.C.: Quadratic 0–1 programming: tightening linear or quadratic convex reformulation by use of relaxations. RAIRO-Oper. Res. 42(2), 103–121 (2008)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Körner, F.: Integer quadratic optimization. Eur. J. Oper. Res. 19(2), 268–273 (1985)MathSciNetCrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020

Authors and Affiliations

  • Shanila Azhar
    • 1
  • Shan Chang
    • 1
    Email author
  • Ye Liu
    • 1
  • Yuting Tao
    • 1
  • Guohua Liu
    • 1
  • Donghong Sun
    • 2
  1. 1.Computer Science and TechnologyDonghua UniversityShanghaiChina
  2. 2.Institute for Network Sciences and CyberspaceTsinghua UniversityBeijingChina

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