Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.

中文翻译：

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机会约束稳健的人群感知的公布定价

人群感知已被公认为是实现大规模城市数据收集的有前途的方法。在典型的人群感知系统中，任务所有者通常需要向用户（例如参与者）提供激励以鼓励他们的参与。在现有的激励机制中，明示定价在保证真实性和公平性的同时，易于实施，因此被广泛采用。任务所有者面临的一个关键挑战是设置正确的发布价格以招募总支付额较小且感知质量合理的人群，即稳健人群感知的发布定价问题。然而，到目前为止，这个基本问题在很大程度上仍然悬而未决。在本文中，我们以一种优雅的方式将感知数据质量的鲁棒性要求建模为机会约束，并研究人群感知系统中的一系列机会约束张贴定价问题。尽管文献中已经应用了一些机会约束优化技术，但它们不能为其解决方案提供任何性能保证。在这项工作中，我们提出了一种基于二分搜索的算法，并表明使用该算法可以让我们对其性能建立理论保证。大量的数值模拟证明了我们提出的算法的有效性。