A survey of spatial crowdsourcing

• Main Authors: TONG, Yongxin, ZHOU, Zimu, ZENG, Yuxiang, CHEN, Lei, SHAHABI, Cyrus
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Spatial crowdsourcing; Task assignment; Quality control; Incentive mechanism; Privacy protection; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4935; https://ink.library.smu.edu.sg/context/sis_research/article/5938/viewcontent/vldbj20_tong.pdf
• Institution: Singapore Management University

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.