Many studies foresee significant future growth in the number of mobile smart phone users, the phone's hardware and software features, and the broadband bandwidth. Therefore, a transformative area of research is to fully utilize this new platform for various tasks, among which the most promising is spatial crowdsourcing. Spatial crowdsourcing (SC) 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 shown to be useful when traditional means fail (e.g., due to disaster), are censored or do not scale in time and space.
Two major impediments to the success of spatial crowdsourcing in real-world applications are scalability and trust issues. Without scale considerations, it is impossible to develop a generic multi-campaign spatial crowdsourcing system (SC-system) that can efficiently and in real-time match many requesters' tasks to numerous workers. Without trust, the SC-system cannot evaluate the credibility of the contributed data, rendering it ineffective for replacing the traditional data collection means. In this paper, we survey and study both issues of scale and trust in spatial crowdsourcing.
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