A Novel Task Assignment Adjustment Method in Spatial-Temporal Crowdsourcing

With the rapid development of mobile networks and the ubiquity of mobile devices, spatial-temporal crowdsourcing, which refers to assigning spatial-temporal tasks to moving workers, has drawn increasing attention. Many researchers aim at various task assignment methods in spatial-temporal crowdsourcing. However, unexpected situations reduce the reliability of the original assignment, such as the absence of reserved workers. To solve the problem, we propose a novel task assignment adjustment method in spatial-temporal crowdsourcing. We design a multi-objective optimization algorithm to minimize the adjustment and maximize the total matching degree in the reassignment process. The experimental results on three real data sets show that the proposed method can improve the total matching degree by about 10% while minimizing the adjustment compared with the baselines.