空间众包下移动工人的跨区域在线多任务分配

Cross-regional Online Multi-task Assignment of Mobile Workers in Spatial Crowdsourcing

空间众包中现有的任务分配方案主要集中在点任务上,对区域任务分配研究较少,并且多数研究以离线任务分配为基础.然而想要实现区域任务的真正应用,在线分配方案的提出更为重要.本文为区域任务提供了一个有效的在线任务分配方案,该方案在预算和时间约束下,可以最大化区域任务的总体质量得分.首先提出一种预分配算法(PA)通过历史数据对工人和任务进行预分配.然后提出了基于移动工人的在线跨区域分配算法(CRMW),并设计多轮分配机制提高任务分配的成功率.该算法分不同轮次在同一区域和所有区域之间进行分配,并采取基于原始质量比的激励机制,从而进一步提高分配算法的命中率.最后提出区域任务分解算法(RTDA)将任务进行子任务分解,并通过优化粒子群算法为子区域任务选择合适工人.本文通过在真实数据集上进行对比实验,从质量分数和运行时间两方面进行比较,并表明了本文算法对质量分数的提高具有一定的有效性.

The existing task assignment schemes in spatial crowdsourcing mainly focus on point tasks,and there is less research on regional task assignment,and most of the research is based on offline task assignment.However,in order to realize the real application of regional tasks,the proposal of online distribution scheme is more important.This paper provides an effective online task assignment scheme for regional tasks,which can maximize the overall quality score of regional tasks under budget and time constraints.First,a pre-allocation algorithm(PA)is proposed to pre-allocate workers and tasks through historical data.Then,an online cross-regional assignment algorithm(CRMW)based on mobile workers is proposed,and a multi-round allocation mechanism is designed to improve the success rate of task allocation.The algorithm distributes between the same area and all areas in different rounds,and adopts an incentive mechanism based on the original quality ratio,thereby further improving the hit rate of the allocation algorithm.Finally,the regional task decomposition algorithm(RTDA)is proposed to decompose the tasks into sub-tasks,and the appropriate workers are selected for the sub-region tasks by optimizing the particle swarm algorithm.This paper compares the quality score and running time through comparative experiments on real data sets,and shows that the algorithm in this paper is effective in improving the quality score.