World Wide Web

, Volume 21, Issue 3, pp 741–758 | Cite as

Participant selection for t-sweep k-coverage crowd sensing tasks

  • Zhiyong Yu
  • Jie Zhou
  • Wenzhong Guo
  • Longkun Guo
  • Zhiwen Yu
Article
  • 141 Downloads
Part of the following topical collections:
  1. Special Issue on Mobile Crowdsourcing

Abstract

With the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and submit ambient information to the server. The composition of participants greatly determines the quality and cost of the collected information. This paper aims to select fewest participants to achieve the quality required by a sensing task. The requirement namely “t-sweep k-coverage” means for a target location, every t time interval should at least k participants sense. The participant selection problem for “t-sweep k-coverage” crowd sensing tasks is NP-hard. Through delicate matrix stacking, linear programming can be adopted to solve the problem when it is in small size. We further propose a participant selection method based on greedy strategy. The two methods are evaluated through simulated experiments using users’ call detail records. The results show that for small problems, both the two methods can find a participant set meeting the requirement. The number of participants picked by the greedy based method is roughly twice of the linear programming based method. However, when problems become larger, the linear programming based method performs unstably, while the greedy based method can still output a reasonable solution.

Keywords

Crowd sensing t-sweep k-coverage Participant selection Linear programming Set covering 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.61300103, 61672159, 61332005), the Technology Innovation Platform Project of Fujian Province under Grant No. 2014H2005 and 2009 J1007,the Fujian Collaborative Innovation Center for Big Data Application in Governments.

References

  1. 1.
    Ahmed, A., Yasumoto, K., Yamauchi, Y., et al.: Distance and time based node selection for probabilistic coverage in people-centric sensing. The 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad-Hoc Communications and Networks. Utah 134–142 (2011)Google Scholar
  2. 2.
    Balister, P., Bollobas, B., Sarkar, A., et al.: Reliable Density Estimates for Achieving Coverage and Connectivity in Thin Strips of Finite Length. Proceedings of the 13th annual ACM international conference on Mobile computing and networking (MobiCom'07). Montreal, Quebec,, 75–86 (2007)Google Scholar
  3. 3.
    Chen, A. Kumar, S.: Designing localized algorithms for barrier coverage. Proceedings of the 13th annual ACM international conference on Mobile computing and networking (MobiCom'07). Montreal, Quebec, 63–74 (2007)Google Scholar
  4. 4.
    Chen, A., Kumar, S., Lai, T.H.: Local barrier coverage in wireless sensor networks. IEEE Trans. Mob. Comput. 9(4), 491–504 (2010)CrossRefGoogle Scholar
  5. 5.
    Chen, H., Guo, B., Yu, Z., et al.: A generic framework for constraint-driven data selection in mobile crowd photographing. IEEE Internet Things J. 4(1), 284–296 (2017)Google Scholar
  6. 6.
    Giuseppe, C., Luca, F., Paolo, B., et al.: Fostering participation in smart cities: a geo-social crowdsensing platform. IEEE Commun. Mag. 51(6), 112–119 (2013)CrossRefGoogle Scholar
  7. 7.
    Guo, B., Wang, Z., Yu, Z., et al.: Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm. ACM Comput. Surv. 48(1), 7 (2015)CrossRefGoogle Scholar
  8. 8.
    Guo, B., Chen, H., Yu, Z., et al.: FlierMeet: a mobile Crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans. Mob. Comput. 14(10), 2020–2033 (2015)CrossRefGoogle Scholar
  9. 9.
    Guo, B., Liu, Y., Wu, W., et al.: ActiveCrowd: a Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems. IEEE Trans. Hum.-Mach. Syst. PP(99):1-12 (2016)Google Scholar
  10. 10.
    Guo, B., Chen, H., Han, Q., et al.: Worker-contributed data utility measurement for visual Crowdsensing systems. IEEE Trans. Mob. Comput. 99, 1–1 (2016)Google Scholar
  11. 11.
    Hachem, S., Pathak, A., Issarny, V.: Probabilistic registration for large-scale mobile participatory sensing. IEEE International Conference on Pervasive Computing and Communications (PerCom). San Diego, 132–140 (2013)Google Scholar
  12. 12.
    Huang, C., Tseng, Y.: The coverage problem in a wireless sensor network. Mob. Netw. Appl. 10(4), 519–528 (2005)CrossRefGoogle Scholar
  13. 13.
    Karp, R.M.: Reducibility among combinatorial problem. In: Miller, R.A., Thatcher, J.W. (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York (1972)CrossRefGoogle Scholar
  14. 14.
    Krause, A., Horvitz, E., Kansal, A., et al.: Toward community sensing. In Proc. of ACM Sensor Networks. (IPSN). St. Louis, USA, 481–492 (2008)Google Scholar
  15. 15.
    Kumar, S., Lai, T.H., Balogh, J.: On K-coverage in a mostly sleeping sensor network. Proceedings of the 10th annual international conference on Mobile computing and networking (MobiCom'04). Philadelphia, 144–158 (2004)Google Scholar
  16. 16.
    Kumar, S., Lai, T.H., Arora, A.: Barrier coverage with wireless sensors. Wirel. Netw. 13(6), 817–834 (2007)CrossRefGoogle Scholar
  17. 17.
    Li, M., Cheng, W., Liu, K., et al.: Sweep coverage with mobile sensors. IEEE Trans. Mob. Comput. 10(11), 1534–1545 (2011)CrossRefGoogle Scholar
  18. 18.
    Lin, L., Lee, H.: Distributed algorithms for dynamic coverage in sensor networks. Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing (PODC'07). Portland, Oregon, 392–393 (2007)Google Scholar
  19. 19.
    Meguerdichian, S., Koushanfar, F., Potkonjak, M., et al.: Coverage problems in wireless ad-hoc sensor networks. Proceedings IEEE INFOCOM 2001, The Conference on Computer Communications, Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Alaska, 3(4):1380–1387 (2001)Google Scholar
  20. 20.
    Mendez, D., Labrador, M.A.: Density Maps: Determining Where to Sample in Participatory Sensing Systems. Proceedings of the 2012 Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing (MUSIC'12). Fukuoka, 35–40 (2012)Google Scholar
  21. 21.
    Mohan, P., Padmanabhan, V.N., Ranjee, R. Nericell: Rich monitoring of road and traffic conditions using mobile smart-phones. Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. Raleigh, 323–336 (2008)Google Scholar
  22. 22.
    Mun, M., Reddy, S., Shilton, K., et al.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. Proceedings of the 7th International Conference on Mobile Systems, Applications and Services. Krakow, 55–68 (2009)Google Scholar
  23. 23.
    Reddy, S., Samanta, V., Shilton, K., et al.: Mobisense, mobile network services for coordinated participatory sensing. Proceedings of International Symposium Autonomous Decentralized Systems (ISADS'09). Athens, 1–6 (2009)Google Scholar
  24. 24.
    Reddy, S., Shilton, K., Burke, J., et al.: Using context annotated mobility profles to recruit data collectors in participatory sensing. Proceedings of the 4th International Symposium on Location and Context Awareness. Tokyo, 52–69 (2009)Google Scholar
  25. 25.
    Reddy, S., Estrin, D., Srivastava, M.: Recruitment framework for participatory sensing data collections. Proceedings of the 8th international conference on Pervasive Computing (Pervasive'10). Helsinki, 138–155 (2010)Google Scholar
  26. 26.
    Stevens, M., D’Hondt, E.: Crowdsourcing of Pollution Data using Smartphones. Proceedings of the Workshop on Ubiquitous Crowdsourcing. Copenhagen Denmark,1–4 (2010)Google Scholar
  27. 27.
    Thiagarajan, A., Ravindranath, L., LaCurts, K., et al.: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones. Proceedings of the 7th ACM Conference on Embedded Network Sensor Systems. Berkeley 85–98 (2009)Google Scholar
  28. 28.
    Wang, J., Wang, Y., Helal, S., et al.: A context-driven worker selection framework for crowd-sensing. Int. J. Distrib. Sens. Netw. 2016(3), 1–16 (2016)Google Scholar
  29. 29.
    Wang, L., Zhang, D., Wang, Y., et al.: Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun. Mag. 54(7), 161–167 (2016)CrossRefGoogle Scholar
  30. 30.
    Xi, M., Wu, K., Qi, Y., et al.: Run to Potential: Sweep Coverage in Wireless Sensor Networks. International Conference on Parallel Processing. Vienna, 50–57 (2009)Google Scholar
  31. 31.
    Xiong, H., Zhang, D., Chen, G., et al.: iCrowd: near-optimal task allocation for piggyback Crowdsensing. IEEE Trans. Mob. Comput. 15(8), 2010–2022 (2016)CrossRefGoogle Scholar
  32. 32.
    Yu, Z., Zhang, D., Yu, Z., et al.: Participant Selection for Offline Event Marketing Leveraging Location Based Social Networks. IEEE Trans. Syst. Man Cybern. Syst. 45(6), 853–864 (2015)CrossRefGoogle Scholar
  33. 33.
    Yu, Z., Xu, H., Yang, Z., et al.: Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints. IEEE Trans. Hum.-Mach. Syst. 46(1), 151–158 (2016)CrossRefGoogle Scholar
  34. 34.
    Zhang, X., Yang, Z., Gong, Y., et al.: SpatialRecruiter: maximizing sensing coverage in selecting Workers for Spatial Crowdsourcing. IEEE Trans. Veh. Technol. 99, 1–1 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Zhiyong Yu
    • 1
    • 2
  • Jie Zhou
    • 1
    • 2
  • Wenzhong Guo
    • 1
    • 2
    • 3
  • Longkun Guo
    • 1
  • Zhiwen Yu
    • 4
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information ProcessingFuzhou UniversityFuzhouChina
  3. 3.Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of EducationFuzhouChina
  4. 4.College of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina

Personalised recommendations