Sparse mobile crowdsensing is a practical paradigm for large sensing systems, which recruits a small number of users to sense data from only a few subareas and, then, infers the data of unsensed subareas. In order to provide high-quality sensing services under a budget constraint, we would like to select the most effective users to collect useful sensing data to achieve the highest inference accuracy. However, due to the variable user mobility and complicated data inference, it is really challenging to directly select the best user set which helps the most with data inference. From the user’s side, we can obtain the probabilistic coverage according to the users’ mobilities, while the probabilistic coverage cannot indicate the data inference accuracy directly. From the subarea’s side, we may identify some more useful subareas under the current states (e.g., the previous sensed subareas and the current expected coverage), while these useful subareas may not be covered by the users. Moreover, both the user mobility and data inference introduce a lot of uncertainty, which yields nonmonotonicity and thus nonsubmodularity in the user recruitment problem. Therefore, in this article, we study the user recruitment problem on both the user’s and subarea’s sides and propose a three-step strategy, including user selection, subarea selection, and user–subarea-cross (US-cross) selection. We first select some candidate user sets, which may cover the most subareas under the budget constraint (user selection), then estimate which subareas are more useful on data inference according to the selected candidates (subarea selection), which finally guides us to recruit the best user set (US-cross selection). Extensive experiments on two real-world data sets with four types of sensing tasks verify the effectiveness of our proposed user recruitment algorithms, which can effectively enhance the data inference accuracy under a budget constraint.

中文翻译：

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在稀疏移动人群感知中提高数据推断准确性的用户招募

稀疏的移动人群感测是大型感测系统的一种实用范例，该系统招募了少量用户以仅从几个子区域感测数据，然后推断出未感测的子区域的数据。为了在预算限制下提供高质量的传感服务，我们希望选择最有效的用户来收集有用的传感数据，以实现最高的推理精度。但是，由于可变的用户移动性和复杂的数据推断，直接选择最能帮助数据推断最大的最佳用户群确实具有挑战性。从用户的角度出发，我们可以根据用户的移动性来获得概率覆盖度，而概率覆盖度不能直接表示数据推断的准确性。从分区的角度来看，我们可能会在当前状态下确定一些更有用的子区域（例如，先前感测到的子区域和当前的预期覆盖范围），而这些有用的子区域可能不会被用户覆盖。而且，用户移动性和数据推断都引入了很多不确定性，这导致了非单调性，从而在用户招募问题中产生了非亚模块性。因此，在本文中，我们研究了用户和子区域双方的用户招募问题，并提出了三步策略，包括用户选择，子区域选择和用户-苏打-跨（US-cross）选择。我们首先选择一些候选用户集，这些用户集可能会在预算约束下覆盖最多的子区域（用户选择），然后根据所选的候选者估计哪些子区域对数据推断更有用（古巴选择），最终引导我们招募最佳的用户群（美国跨地区选择）。在具有四种类型的传感任务的两个真实世界数据集上的大量实验证明了我们提出的用户募集算法的有效性，该算法可以有效地提高预算约束下的数据推断准确性。