Extra-Budget Aware Task Assignment in Spatial Crowdsourcing

With the prevalence of sharing economy and mobile Internet, spatial crowdsourcing (SC) has been receiving increased attentions recently. A core issue in SC is task assignment, which aims to assign tasks to suitable workers. As workers need to reach the corresponding locations to complete the tasks, they prefer tasks nearby to save travel cost. Therefore, most of the existing solutions for task assignment give workers a fixed range constraint. However, those solutions do not consider the tasks that out of the range, which may make these remote tasks never been completed. Therefore, in this paper, we propose a new problem called extra-budget aware task assignment (EBATA) in spatial crowdsourcing, where extra budget is provided to subsidize the over cost of workers to ensure that the remote tasks have a chance to be accomplished. To address the EBATA problem, two baseline algorithms and two improved greedy algorithms are devised in the paper. The two improved greedy algorithms can heavily reduce the computational time and keep most of the number of matched pairs with the optimal one. Extensive experiments on real dataset verify the effectiveness and efficiency of the proposed methods.