Spatial Crowdsourcing

Abstract

Spatial crowdsourcing engages individuals, groups, and communities in the act of collecting, analyzing, and disseminating urban, social, and other spatiotemporal information. This new paradigm of data collection has been shown to be useful when traditional means fail (e.g., due to disaster), are censored, or do not scale in time and space. The wide applicability of spatial crowdsourcing primarily became possible due to the broad availability of mobile devices. With spatial crowdsourcing, the goal is to efficiently outsource a set of spatiotemporal tasks (i.e., tasks related to time and location) to a set of workers, which requires the workers to perform the tasks by physically traveling to those locations. Hence, spatial crowdsourcing strategies must be designed to take advantage of large populations of human workers for ad hoc spatiotemporal tasks – they must consider the environment's dynamism (i.e., tasks and workers come and go) and scale as well as user considerations such as trust (i.e., not all workers are trustworthy) and privacy (i.e., not all workers want to share their location information). Here efficient spatial crowdsourcing task assignment strategies considering both trust and privacy are discussed.