In the age of the development of artificial intelligence, we face the challenge on how to obtain high-quality data set for learning systems effectively and efficiently. Crowdsensing is a new powerful tool which will divide tasks between the data contributors to achieve an outcome cumulatively. However, it arouses several new challenges, such as incentivization. Incentive mechanisms are significant to the crowdsensing applications, since a good incentive mechanism will attract more workers to participate. However, existing mechanisms failed to consider situations where the crowdsourcer has to hire capacitated workers or workers from multiregions. We design two objectives for the proposed multiregion scenario, namely, weighted mean and maximin. The proposed mechanisms maximize the utility of services provided by a selected data contributor under both constraints approximately. Also, extensive simulations are conducted to verify the effectiveness of our proposed methods.

中文翻译：

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多区域移动人群感知的真实机制设计

在人工智能发展的时代，我们面临着如何有效和高效地为学习系统获取高质量数据集的挑战。众包是一个新的强大工具，它将数据提供者之间的任务划分，以累积实现结果。但是，它引起了一些新的挑战，例如激励。激励机制对众筹应用很重要，因为良好的激励机制将吸引更多的工人参与。但是，现有的机制未能考虑众包方必须雇用有能力的工人或多地区工人的情况。我们针对提出的多区域方案设计了两个目标，即加权均值和极大值。所提出的机制在大约两个约束条件下最大化了由选定数据提供者提供的服务的效用。此外，进行了广泛的仿真，以验证我们提出的方法的有效性。