Crowdsensing is a popular method that leverages a crowd of sensor users to collect data. For many crowdsensing applications, the collected raw data need to be preprocessed before further analysis, and the preprocessing work is mainly done by the crowdsourcer. However, as the amount of collected data increases, this type of preprocessing approach has many disadvantages. In this article, we construct monetary-based incentive mechanisms to motivate users to preprocess the collected raw data for the crowdsourcer. For two common crowdsensing scenarios, we propose two system models, which are the single-task-multiple-participants (STMP) model and the multiple-tasks-multiple-participants (MTMP) model. In the STMP model, we design an incentive mechanism based on game theory and prove that there is a Nash equilibrium. In the MTMP model, we develop an incentive mechanism based on an auction and demonstrate that the incentive mechanism has the desirable properties of truthfulness, individual rationality, profitability, and computational efficiency. Furthermore, the utility maximization problems of the crowdsourcer and users are simultaneously considered in our incentive mechanisms. Through theoretical analysis and extensive experiments, we evaluate the performance of our incentive mechanisms.

中文翻译：

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众感的激励机制

人群感应是一种流行的方法，它利用大量传感器用户来收集数据。对于很多众测应用来说，采集到的原始数据需要在进一步分析之前进行预处理，而预处理工作主要由众包方完成。然而，随着收集数据量的增加，这种类型的预处理方法有很多缺点。在本文中，我们构建了基于货币的激励机制来激励用户为众包者预处理收集到的原始数据。对于两种常见的众感场景，我们提出了两种系统模型，即单任务多参与者（STMP）模型和多任务多参与者（MTMP）模型。在STMP模型中，我们设计了一种基于博弈论的激励机制，证明存在纳什均衡。在 MTMP 模型中，我们开发了一种基于拍卖的激励机制，并证明该激励机制具有真实性、个体理性、盈利能力和计算效率等理想属性。此外，我们的激励机制同时考虑了众包者和用户的效用最大化问题。通过理论分析和广泛的实验，我们评估了我们激励机制的表现。