›› 2017, Vol. 32 ›› Issue (5): 890-904.doi: 10.1007/s11390-017-1771-6

Special Issue: Data Management and Data Mining

• Special Section on Crowdsourced Data Management • Previous Articles     Next Articles

Budget-aware Dynamic Incentive Mechanism in Spatial Crowdsourcing

Jia-Xu Liu1,2, Member, CCF, Yu-Dian Ji3, Wei-Feng Lv1,*, Ke Xu1   

  1. 1 State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China;
    2 School of Software, Liaoning Technical University, Huludao 125105, China;
    3 Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
  • Received:2017-03-02 Revised:2017-07-13 Online:2017-09-05 Published:2017-09-05
  • Contact: Wei-Feng Lv,lwf@nlsde.buaa.edu.cn E-mail:lwf@nlsde.buaa.edu.cn
  • About author:Jia-Xu Liu is currently a Ph.D. candidate at the School of Computer Science and Engineering, Beihang University, Beijing. He received his B.E. and M.S. degrees in computer science, both from Liaoning Technical University, Huludao, in 2002 and 2010, respectively. His research interests include spatial crowdsourcing, spatio-temporal database, and network security.
  • Supported by:

    This work is supported in part by the National Basic Research 973 Program of China under Grant No. 2014CB340300, the National Natural Science Foundation of China under Grant Nos. 61421003 and 71531001, and the State Key Laboratory of Software Development Environment of China under Grant No. SKLSDE-2016ZX-13.

The ubiquitous deployment of GPS-equipped devices and mobile networks has spurred the popularity of spatial crowdsourcing. Many spatial crowdsourcing tasks require crowd workers to collect data from different locations. Since workers tend to select locations nearby or align to their routines, data collected by workers are usually unevenly distributed across the region. To encourage workers to choose remote locations so as to avoid imbalanced data collection, we investigate the incentive mechanisms in spatial crowdsourcing. We propose a price adjustment function and two algorithms, namely DFBA and DABA, which utilize price leverage to mitigate the imbalanced data collection problem. Extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed incentive mechanisms are able to effectively balance the popularity of different locations.

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