Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2020
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 (SC) 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
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
VLDB Journal
Volume
29
Issue
1
First Page
217
Last Page
250
ISSN
1066-8888
Identifier
10.1007/s00778-019-00568-7
Publisher
Springer Verlag (Germany)
Citation
TONG, Yongxin; ZHOU, Zimu; ZENG, Yuxiang; CHEN, Lei; and SHAHABI, Cyrus.
A survey of spatial crowdsourcing. (2020). VLDB Journal. 29, (1), 217-250.
Available at: https://ink.library.smu.edu.sg/sis_research/4935
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00778-019-00568-7