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Quality-aware Online Task Assignment in Mobile Crowdsourcing

机译：移动众包中的质量意识到线任务分配

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摘要

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.
机译：近年来，移动众包已成为一种强大的计算范例，以利用人类的权力来执行空间任务，例如收集实时交通信息和检查特定超市的产品价格。移动众包的根本问题是：当任务和人群工作者动态出现在平台中，如何为每个工人分配适当的任务集。大多数现有研究专注于基于二分图匹配的有效分配算法。然而，他们忽略了人群工人可能不可靠的重要事实。因此，他们的任务分配方案无法确保整体质量。在本文中，我们调查了移动众包中的质量意识的在线任务分配（QAOTA）问题。我们提出了一个概率模型来衡量任务的质量和搭便车的模型，以表征工人行为模式。我们将任务分配模拟为质量最大化问题，并导出多项式时间在线分配算法。通过严格的分析，我们证明了所提算法近似于竞争比例为10/7的离线最佳解决方案。最后，我们通过密集实验展示了我们解决方案的效率和有效性。

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  • 来源
    《ACM transactions on sensor networks》 |2020年第3期|30.1-30.21|共21页
  • 作者

    Miao Xin; Kang Yanrong; Ma Qiang; Liu Kebin; Chen Lei;

  • 作者单位

    Tsinghua Univ Sch Software 30 Shuangqing Rd Beijing 100084 Peoples R China;

    Tencent Inc Binhai Rd Shenzhen 518054 Peoples R China;

    Tsinghua Univ Sch Software 30 Shuangqing Rd Beijing 100084 Peoples R China;

    Tsinghua Univ Sch Software 30 Shuangqing Rd Beijing 100084 Peoples R China;

    Hong Kong Univ Sci & Technol CSE Dept Kowloon Clear Water Bay Hong Kong Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Crowdsourcing; task assignment;

    机译：众包;任务分配;

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