On Participant Selection for Minimum Cost Participatory Urban Sensing with Guaranteed Quality of Information

  • Hong YaoEmail author
  • Changkai Zhang
  • Chao Liu
  • Qingzhong Liang
  • Xuesong Yan
  • Chengyu Hu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 163)


Exploring vehicles to conduct participatory urban sensing has become an economic and efficient sensing paradigm to pursue the smart city vision. Intuitively, having more vehicles participate in one sensing task, higher quality-of-information (QoI) can be achieved. However, more participation also implies a higher sensing cost, which include the cost pay to participated vehicles and 3G traffic cost. This paper introduces an interesting problem on how to select an appropriate set of vehicles to minimize the sensing cost while guaranteeing the required QoI. In this paper, we define a new QoI metric called coverage ratio satisfaction (CRS) with the consideration of coverage from both temporary and spatial aspects. Based on the CRS definition, we formulate the minimum cost CRS guaranteeing problem as an integer linear problem and propose a participant selection strategy called Vehicles Participant Selection (VPS). The high efficiency of VPS is extensively validated by real trace based experiments.


Vehicular sensor network Quality-of-Information Coverage Ratio Satisfaction Vehicles Participant Selection 



This research was supported in part by the NSF of China (Grant No. 61402425, 61272470, 61305087,61440060),the China Postdoctoral Science Foundation funded project(2014M562086), the Fundamental Research Funds for National University, China University of Geosciences (Wuhan) (Grant No. CUG14065, CUGL150830, CUG120114).


  1. 1.
    Burke, J.A., Estrin, D., Hansen, M., Parker, A.: Participatory sensing. Center for Embedded Network Sensing (2006)Google Scholar
  2. 2.
    Zeng, D., Li, P., Guo, S., Miyazaki, T., Hu, J., Xiang, Y.: Energy minimization in multi-task software-defined sensor networks. IEEE Trans. Comput. 64(11), 3128–3139 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Song, Z., Liu, C.H., Wu, J., Ma, J., Wang, W.: QoI-Aware multitask-oriented dynamic participant selection with budget constraints. IEEE Trans. Veh. Technol. 63(9), 4618–4632 (2014)CrossRefGoogle Scholar
  4. 4.
    Xiong, H., Zhang, D., Chen, G., Wang, L., Gauthier, V.: Crowdtasker: Maximizing coverage quality in piggyback crowdsensing under budget constraint. In: Proceedings of the 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2015)Google Scholar
  5. 5.
    Sheng, X., Tang, J.: Energy-efficient collaborative sensing with mobile phones. In: INFOCOM, Proceedings IEEE, pp. 1916–1924. IEEE (2012)Google Scholar
  6. 6.
    Zhao, Q., Zhu, Y., Zhu, H., Li, B.: Fair energy-efficient sensing task allocation in participatory sensing with smartphones. In: INFOCOM, Proceedings IEEE, pp. 1366–1374. IEEE (2014)Google Scholar
  7. 7.
    Wang, L., Zhang, D.: Effsense: energy-efficient and cost-effective data uploading in mobile crowdsensing. In: Proceedings of the ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 1075–1086. ACM (2013)Google Scholar
  8. 8.
    Zeng, D., Guo, S., Barnawi, A., Yu, S., Stojmenovic, I.: An improved stochastic modeling of opportunistic routing in vehicular CPS. IEEE Trans. Comput. 64(7), 1819–1829 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zeng, D., Guo, S., Hu, J.: Reliable bulk-data dissemination in delay tolerant networks. IEEE Trans. Parall. Distrib. Syst. 25(8), 2180–2189 (2014)CrossRefGoogle Scholar
  10. 10.
    Yao, H., Zeng, D., Huang, H., Guo, S., Barnawi, A., Stojmenovic, I.: Opportunistic offloading of deadline-constrained bulk cellular traffic in vehicular DTNs. IEEE Trans. Comput. 64(12), 3515–3527 (2015)CrossRefGoogle Scholar
  11. 11.
    Devarakonda, S., Sevusu, P., Liu, H., Liu, R: Real-time air quality monitoring through mobile sensing in metropolitan areas. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, pp. 15. ACM (2013)Google Scholar
  12. 12.
    Du, R., Chen, C., Yang, B., Lu, N., Guan, X., Shen, X.: Effective urban traffic monitoring by vehicular sensor networks. IEEE Trans. Veh. Technol. 64(1), 273–286 (2015)CrossRefGoogle Scholar
  13. 13.
    Bruno, R., Nurchis, M.: Robust and efficient data collection schemes for vehicular multimedia sensor networks. In: IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–10. IEEE (2013)Google Scholar
  14. 14.
    Li, Z., Liu, Y., Li, M., Wang, J.: Exploiting ubiquitous data collection for mobile users in wireless sensor networks. IEEE Trans. Parall. Distrib. Syst. 24(2), 312–326 (2013)CrossRefGoogle Scholar
  15. 15.
    Palazzi, C.E., Pezzoni, F., Ruiz, P.M.: Delay-bounded data gathering in urban vehicular sensor networks. Pervasive Mob. Comput. 8(2), 180–193 (2012)CrossRefGoogle Scholar
  16. 16.
    Center, S.G.C.: Shanghai taxi trace data (2012).

Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Hong Yao
    • 1
    Email author
  • Changkai Zhang
    • 1
  • Chao Liu
    • 1
  • Qingzhong Liang
    • 1
  • Xuesong Yan
    • 1
  • Chengyu Hu
    • 1
  1. 1.School of Computer Science and TechnologyChina University of GeosciencesWuhanChina

Personalised recommendations