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

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.

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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.

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Correspondence to Liang Wang.

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Liang Wang received the PhD degree in computer science from Chinese Academy of Sciences, China. He is currently a postdoctoral researcher in Northwestern Polytechnical University, China. His research interests include mobile crowd sensing and intelligent systems.

Zhiwen Yu is currently a professor in Northwestern Polytechnical University, China. He was an Alexander Von Humboldt Fellow with Mannheim University, Germany from November 2009 to October 2010. His research interests include ubiquitous computing and HCI.

Bin Guo received the PhD degree in computer science from Keio University, Japan in 2009, He is currently a professor with Northwestern Polytechnical University, China. His research interests include ubiquitous computing, mobile crowd sensing, and HCI.

Fei Yi is currently working toward the doctoral degree at the School of Computer Science and Technology, Northwestern Polytechnical University, China. His research interests include mobile crowd sensing and intelligent systems.

Fei Xiong received the PhD degree from Beijing Jiaotong University (BJTU), China. He is currently an associate professor with BJTU. His research interests include complex networks and complex systems.

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Wang, L., Yu, Z., Guo, B. et al. Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective. Front. Comput. Sci. 12, 231–244 (2018). https://doi.org/10.1007/s11704-017-7024-6

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Keywords

  • mobile crowd sensing
  • task allocation
  • mobility regularity
  • pattern matching