With the rich sensing ability and extensive usage of various sensors, mobile crowdsensing (MCS) has become a new paradigm to collect sensing data for various sensing applications. In the modern urban environment, the multisource sensing information and the difference of mobile users make the sensing scenario more and more complex. To improve the applicability of different sensing scenarios, it is necessary to design a heterogeneous user recruitment mechanism for multiple heterogeneous tasks. However, most of the prior works focus on the recruitment of single-type users for homogeneous tasks without considering the heterogeneity of tasks (e.g., spatiotemporal characteristics, sensor requirements, etc.) and users (e.g., personal preferences, carrying sensors, etc). In this article, we propose the problem of heterogeneous user recruitment of multiple heterogeneous tasks (HURoTs) in MCS, with the goal of minimizing the total platform payment and maximizing the task coverage ratio. The HURoT problem is proved to be NP-hard, which is divided into multiple subproblems in different sensing cycles. Moreover, by introducing the user’s utility function, we propose three greedy-based user recruitment algorithms to obtain near-optimal solutions. Extensive experiments are conducted to validate the effectiveness of the proposed schemes.

中文翻译：

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移动群智感知中基于效用的异构用户招募

随着各种传感器的丰富感知能力和广泛使用，移动群智感知（MCS）已成为为各种感知应用收集感知数据的新范式。现代城市环境中，感知信息的多源性和移动用户的差异性使得感知场景越来越复杂。为了提高不同感知场景的适用性，需要针对多个异构任务设计异构用户招募机制。然而，以往的工作大多侧重于为同质任务招募单一类型的用户，而没有考虑任务（如时空特征、传感器需求等）和用户（如个人喜好、携带传感器等）的异质性。 . 在本文中，我们提出了 MCS 中多个异构任务 (HURoT) 的异构用户招募问题，目标是最小化平台总支付和最大化任务覆盖率。HURoT问题被证明是NP-hard问题，在不同的感知周期内被分解为多个子问题。此外，通过引入用户的效用函数，我们提出了三种基于贪心的用户招募算法以获得接近最优的解决方案。进行了广泛的实验以验证所提出方案的有效性。我们提出了三种基于贪心的用户招募算法以获得接近最优的解决方案。进行了广泛的实验以验证所提出方案的有效性。我们提出了三种基于贪心的用户招募算法以获得接近最优的解决方案。进行了广泛的实验以验证所提出方案的有效性。