In recent years, mobile crowdsourcing has emerged as a powerful computation paradigm to harness human power to perform spatial tasks such as collecting real-time traffic information and checking product prices in a specific supermarket. A fundamental problem of mobile crowdsourcing is: When both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each worker. Most existing studies focus on efficient assignment algorithms based on bipartite graph matching. However, they overlook an important fact that crowd workers might be unreliable. Thus, their task assignment schemes cannot ensure the overall quality. In this article, we investigate the Quality-aware Online Task Assignment (QAOTA) problem in mobile crowdsourcing. We propose a probabilistic model to measure the quality of tasks and a hitchhiking model to characterize workers’ behavior patterns. We model task assignment as a quality maximization problem and derive a polynomial-time online assignment algorithm. Through rigorous analysis, we prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Finally, we demonstrate the efficiency and effectiveness of our solution through intensive experiments.

中文翻译：

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移动众包中的质量感知在线任务分配

近年来，移动众包已经成为一种强大的计算范式，可以利用人力来执行空间任务，例如收集实时交通信息和检查特定超市的产品价格。移动众包的一个基本问题是：当任务和众包工作者都动态出现在平台上时，如何为每个工作者分配一组合适的任务。大多数现有研究都集中在基于二分图匹配的高效分配算法上。然而，他们忽略了一个重要的事实，即众包工作者可能不可靠。因此，他们的任务分配方案不能保证整体质量。在本文中，我们研究了移动众包中的质量感知在线任务分配 (QAOTA) 问题。我们提出了一个概率模型来衡量任务的质量，并提出一个搭便车模型来描述工人的行为模式。我们将任务分配建模为质量最大化问题并推导出多项式时间在线的分配算法。通过严格的分析，我们证明了所提出的算法近似于离线最优具有 10/7 的竞争力的解决方案。最后，我们通过密集的实验证明了我们的解决方案的效率和有效性。