Mobile crowdsensing recruits a group of mobile users to cooperatively perform a common sensing job with their smart devices. As a special issue, photo crowdsensing allows users to utilize the built-in cameras of mobile devices to take photos for an event or a target. Then, the photos can be used in numerous application areas, such as target reconstruction, scenario reduction, and so on. Therefore, photo crowdsensing has attracted considerable attention recently due to the rich information that can be provided by images. In this paper, we focus on using the photos to make reconstructions for specific targets. Furthermore, we develop a user recruitment system for efficient photo collecting in mobile crowdsensing (RSMC), where the task requesters publish a sensing task to the users, and the map is gridded according to the locations of the sensing targets. Then, we use a semi-Markov model to calculate the user's utility for the sensing task. Finally, a user recruitment strategy is devised to recruit the optimal $k$ users for finishing the sensing task. We conduct extensive simulations based on three widely used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, RSMC takes the largest number of efficient photos for the sensing task.

中文翻译：

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移动人群中高效照片采集的用户招募系统

移动人群感知招募一组移动用户，用他们的智能设备合作执行共同的感知工作。作为一个特刊，照片众包允许用户利用移动设备的内置摄像头为事件或目标拍照。然后，照片可以用于众多应用领域，例如目标重建、场景缩减等。因此，由于图像可以提供丰富的信息，照片人群感知最近引起了相当大的关注。在本文中，我们专注于使用照片对特定目标进行重建。此外，我们开发了一个用户招募系统，用于在移动人群感知（RSMC）中高效收集照片，其中任务请求者向用户发布感知任务，并根据感​​应目标的位置对地图进行网格化。然后，我们使用半马尔可夫模型来计算用户对感知任务的效用。最后，设计了一个用户招募策略来招募最佳的 $k$ 用户来完成传感任务。我们基于三种广泛使用的现实世界轨迹进行了广泛的模拟：罗马/出租车、epfl 和 geolife。结果表明，与其他招聘策略相比，RSMC 为传感任务拍摄了最多的有效照片。