Skip to main content
SpringerLink
Log in

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

  • Published:
World Wide Web Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  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)

  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)

  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)

  4. Chen, A., Kumar, S., Lai, T.H.: Local barrier coverage in wireless sensor networks. IEEE Trans. Mob. Comput. 9(4), 491–504 (2010)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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. 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)

  12. Huang, C., Tseng, Y.: The coverage problem in a wireless sensor network. Mob. Netw. Appl. 10(4), 519–528 (2005)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

  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)

  16. Kumar, S., Lai, T.H., Arora, A.: Barrier coverage with wireless sensors. Wirel. Netw. 13(6), 817–834 (2007)

    Article  Google Scholar 

  17. Li, M., Cheng, W., Liu, K., et al.: Sweep coverage with mobile sensors. IEEE Trans. Mob. Comput. 10(11), 1534–1545 (2011)

    Article  Google Scholar 

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  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)

  26. Stevens, M., D’Hondt, E.: Crowdsourcing of Pollution Data using Smartphones. Proceedings of the Workshop on Ubiquitous Crowdsourcing. Copenhagen Denmark,1–4 (2010)

  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)

  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. Wang, L., Zhang, D., Wang, Y., et al.: Sparse mobile crowdsensing: challenges and opportunities. IEEE Commun. Mag. 54(7), 161–167 (2016)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 

Download references

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.

Author information

Authors and Affiliations

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China

    Zhiyong Yu, Jie Zhou, Wenzhong Guo & Longkun Guo

  2. Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, China

    Zhiyong Yu, Jie Zhou & Wenzhong Guo

  3. Key Laboratory of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou, 350003, China

    Wenzhong Guo

  4. College of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Zhiwen Yu

Authors
  1. Zhiyong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhong Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Longkun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiwen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenzhong Guo.

Additional information

This article belongs to the Topical Collection: Special Issue on Mobile Crowdsourcing

Guest Editors: Bin Guo, Xing Xie, Raghu K. Ganti, Daqing Zhang, and Zhu Wang

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, Z., Zhou, J., Guo, W. et al. Participant selection for t-sweep k-coverage crowd sensing tasks. World Wide Web 21, 741–758 (2018). https://doi.org/10.1007/s11280-017-0481-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-017-0481-x

Keywords

Search

Navigation