With the rapid popularization and application of smart sensing devices, mobile crowd sensing (MCS) has made rapid development. MCS mobilizes personnel with various sensing devices to collect data. Task distribution as the key point and difficulty in the field of MCS has attracted wide attention from scholars. However, the current research on participant selection methods whose main goal is data quality is not deep enough. Different from most of these previous studies, this paper studies the participant selection scheme on the multitask condition in MCS. According to the tasks completed by the participants in the past, the accumulated reputation and willingness of participants are used to construct a quality of service model (QoS). On the basis of maximizing QoS, two heuristic greedy algorithms are used to solve participation; two options are proposed: task-centric and user-centric. The distance constraint factor, integrity constraint factor, and reputation constraint factor are introduced into our algorithms. The purpose is to select the most suitable set of participants on the premise of ensuring the QoS, as far as possible to improve the platform’s final revenue and the benefits of participants. We used a real data set and generated a simulation data set to evaluate the feasibility and effectiveness of the two algorithms. Detailedly compared our algorithms with the existing algorithms in terms of the number of participants selected, moving distance, and data quality. During the experiment, we established a step data pricing model to quantitatively compare the quality of data uploaded by participants. Experimental results show that two algorithms proposed in this paper have achieved better results in task quality than existing algorithms.

中文翻译：

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针对移动人群感知服务质量的参与者招募方法

随着智能感应设备的迅速普及和应用，移动人群感应（MCS）取得了长足的发展。MCS动员人员使用各种传感设备来收集数据。任务分配作为MCS领域的重点和难点已经引起了学者的广泛关注。然而，目前对以数据质量为主要目标的参与者选择方法的研究还不够深入。与以往的大多数研究不同，本文研究了MCS中多任务条件下的参与者选择方案。根据参与者过去完成的任务，使用参与者积累的声誉和意愿来构建服务质量模型（QoS）。在最大化QoS的基础上，使用了两种启发式贪婪算法来解决参与问题。提出了两种选择：以任务为中心和以用户为中心。距离约束因子，完整性约束因子和声誉约束因子被引入到我们的算法中。目的是在确保QoS的前提下选择最合适的参与者集，以尽可能提高平台的最终收入和参与者的利益。我们使用了一个真实的数据集并生成了一个仿真数据集，以评估这两种算法的可行性和有效性。在选择的参与者数量，移动距离和数据质量方面，将我们的算法与现有算法进行了详细的比较。在实验过程中，我们建立了分步数据定价模型，以定量比较参与者上传的数据的质量。