The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order to identify truthful values from (crowd) workers’ noisy or even conflicting sensory data, truth discovery algorithms , which jointly estimate workers’ data quality and the underlying truths through quality-aware data aggregation, have drawn significant attention. However, the power of these algorithms could not be fully unleashed in MCS systems, unless workers’ strategic reduction of their sensing effort is properly tackled. To address this issue, in this paper, we propose a payment mechanism , named Theseus, that deals with workers’ such strategic behavior, and incentivizes high-effort sensing from workers. We ensure that, at the Bayesian Nash Equilibrium of the non-cooperative game induced by Theseus, all participating workers will spend their maximum possible effort on sensing, which improves their data quality. As a result, the aggregated results calculated subsequently by truth discovery algorithms based on workers’ data will be highly accurate. Additionally, Theseus bears other desirable properties, including individual rationality and budget feasibility . We validate the desirable properties of Theseus through theoretical analysis, as well as extensive simulations.

中文翻译：

---

数据驱动定价，用于移动人群传感系统中的传感努力诱导

近年来，随着人类携带的移动设备的激增，出现了移动人群感应（MCS）系统，该系统将感官数据收集外包给公众。为了从（人群）工人的嘈杂甚至冲突的感觉数据中识别出真实的价值，真相发现算法 通过感知质量的数据汇总来共同评估工人的数据质量和基本事实的数据，已经引起了广泛关注。但是，除非工人愿意，否则这些算法的功能无法在MCS系统中充分发挥。战略性地减少他们的感知工作解决得当。为了解决这个问题，在本文中，我们提出了付款机制 名为These修斯（Theusus）的公司，处理工人的这种战略行为，并激励工人的努力。我们确保在贝叶斯纳什均衡 的 非合作博弈 在These修斯的激励下，所有参与的工人将花费他们的 最大可能的努力感应，从而提高其数据质量。结果，由真相发现算法随后基于工人数据计算出的合计结果将非常准确。此外，These修斯具有其他理想的特性，包括个人理性 和 预算可行性 。我们通过理论分析和广泛的模拟，验证了These修斯的理想特性。