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Frontiers of Computer Science

, Volume 12, Issue 2, pp 231–244 | Cite as

Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective

  • Liang Wang
  • Zhiwen Yu
  • Bin Guo
  • Fei Yi
  • Fei Xiong
Research Article

Abstract

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing (MCS) using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users’ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy-based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on realworld open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.

Keywords

mobile crowd sensing task allocation mobility regularity pattern matching 

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Notes

Acknowledgements

This work was partially supported by the National Basic Research Program of China (2015CB352400), the National Natural Science Foundation of China (Grant Nos. 61402360, 61402369), the Foundation of Shaanxi Educational Committee (16JK1509). The authors are grateful to the anonymous referees for their helpful comments and suggestions.

Supplementary material

11704_2017_7024_MOESM1_ESM.ppt (228 kb)
Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Liang Wang
    • 1
    • 2
  • Zhiwen Yu
    • 1
  • Bin Guo
    • 1
  • Fei Yi
    • 1
  • Fei Xiong
    • 3
  1. 1.School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Electrical and Control EngineeringXi’an University of Science and TechnologyXi’anChina
  3. 3.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina

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