Spatial crowdsourcing: challenges, techniques, and applications

Crowdsourcing is a new computing paradigm where humans are actively enrolled to participate in the procedure of computing, especially for tasks that are intrinsically easier for humans than for computers. The popularity of mobile computing and sharing economy has extended conventional web-based crowdsourcing to spatial crowdsourcing (SC), where spatial data such as location, mobility and the associated contextual information, plays a central role. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including Citizen Sensing (Waze), P2P ride-sharing (Uber) and Real-time Online-To-Offline (O2O) services (Instacart and Postmates). In this tutorial, we review the paradigm shift from web-based crowdsourcing to spatial crowdsourcing. We dive deep into the challenges and techniques brought by the unique spatio-temporal characteristics of spatial crowdsourcing. Particularly, we survey new designs in task assignment, quality control, incentive mechanism design and privacy protection on spatial crowdsourcing platforms, as well as the new trend to incorporate crowdsourcing to enhance existing spatial data processing techniques. We also discuss case studies of representative spatial crowdsourcing systems and raise open questions and current challenges for the audience to easily comprehend the tutorial and to advance this important research area.