Identifying the Most Valuable Workers in Fog-Assisted Spatial Crowdsourcing

Abstract

In this paper, we study worker selection in spatial crowdsourcing, which is the recruitment of human workers in a specific location to collect geographical data. To achieve better performance, spatial crowdsourcing task relies on both worker's effort and skill. Therefore, to maximize the long-term platform utility, we exploit fog platform as a service to identify valuable workers through learning their performance information. Worker's historical performance data are recorded at local fog server, based on which valuable workers are identified and selected to perform the tasks. During worker selection, we aim at balancing the exploration and exploitation, and propose an online algorithm that promotes workers who are not fully explored. With budget constraint, the proposed algorithm is able to maximize the long-term platform utility. Theoretical analysis indicates that the proposed learning algorithm achieves asymptotically diminishing regret. Finally, extensive simulations on real-world dataset are conducted, which demonstrate the advantage of our algorithm over other methods.