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Spatial crowdsourcing: a survey

  • Yongxin Tong
  • Zimu Zhou
  • Yuxiang Zeng
  • Lei ChenEmail author
  • Cyrus Shahabi
Special Issue Paper
  • 128 Downloads

Abstract

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique characteristics of spatial crowdsourcing. Particularly, we identify four core algorithmic issues in spatial crowdsourcing: (1) task assignment, (2) quality control, (3) incentive mechanism design, and (4) privacy protection. We conduct a comprehensive and systematic review of existing research on the aforementioned four issues. We also analyze representative spatial crowdsourcing applications and explain how they are enabled by these four technical issues. Finally, we discuss open questions that need to be addressed for future spatial crowdsourcing research and applications.

Keywords

Spatial crowdsourcing Task assignment Quality control Incentive mechanism Privacy protection 

Notes

Acknowledgements

We are grateful to anonymous reviewers for their constructive comments. Yongxin Tong’s work is partially supported by the National Science Foundation of China (NSFC) under Grant Nos. 61822201, U1811463 and 71531001, Science and Technology Major Project of Beijing under Grant Nos. Z171100005117001 and Didi Gaia Collborative Research Funds for Young Scholars. Yuxiang Zeng and Lei Chen’s works are partially supported by the Hong Kong RGC CRF C6030-18G Project, the National Science Foundation of China (NSFC) under Grant No. 61729201, Science and Technology Planning Project of Guangdong Province, China, No. 2015B010110006, Hong Kong ITC ITF Grants ITS/212/16FP and ITS/470/18FX, Didi-HKUST joint research lab project, Microsoft Research Asia Collaborative Research Grant and Wechat Research Grant. Cyrus Shahabi’s work has been funded in part by NSF Grants IIS1320149 and CNS-1461963, the USC Integrated Media Systems Center (IMSC), and unrestricted cash gifts from Google and Oracle. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of any of the sponsors such as the National Science Foundation.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development Environment, Beijing Advanced Innovation Center for Big Data and Brain Computing and International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina
  2. 2.Computer Engineering and Networks LaboratoryETH ZurichZurichSwitzerland
  3. 3.Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyKowloonChina
  4. 4.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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