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
Part of the following topical collections:
  1. Special Issue on Mobile Crowdsourcing


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.


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



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.


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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

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