Quality and Budget Aware Task Allocation for Spatial Crowdsourcing

A major research challenge for spatial crowdsourcing is to improve the expected quality of the results. However, existing research in this field mostly focuses on achieving this objective in volunteer-based spatial crowdsourcing. In this paper, we introduce the budget limitations into the above problem and consider realistic cases where workers are paid unequally based on their trustworthiness. We propose a novel quality and budget aware spatial task allocation approach which jointly considers the workers' reputation and proximity to the task locations to maximize the expected quality of the results while staying within a limited budget.