Recently, crowdsensing revolutionizes sensing paradigm in Internet of Things (IoTs). However, a practical incentive mechanism which works for time-varying scenario and fairly incentivizes users to participate in crowdsensing is less studied. In this paper, we propose an incentive mechanism for crowdsensing under continuous and time-varying scenario using three-stage Stackelberg game. In such a scenario, different requesters generate sensing tasks with payments to the platform at each time slot. The platform makes pricing decision to determine rewards for tasks without complete information, and then notifies task-price pairs to online users in Stage I. In Stage II, users select optimal tasks as their interests under certain constraints and report back to the platform. The platform fairly selects users as workers in order to ensure users’ long-term participation in Stage III. We use Lyapunov optimization to address online decision problems for the platform in Stage I and III where there are no prior knowledge and future information available. We propose an FPTAS for users to derive their interests of tasks based on their mobile devices’ computing capabilities in Stage II. Numerical results in simulations validate the significance and superiority of our proposed incentive mechanism.

中文翻译：

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移动人群感知的三阶段 Stackelberg 长期激励机制和货币化：一种在线学习方法

最近，群体感应彻底改变了物联网 (IoT) 中的感应范式。然而，一种适用于时变场景并公平激励用户参与人群感知的实用激励机制研究较少。在本文中，我们提出了一种使用三阶段 Stackelberg 博弈在连续和时变场景下进行人群感知的激励机制。在这种情况下，不同的请求者会在每个时隙生成向平台付款的感知任务。平台对没有完整信息的任务进行定价决策，确定奖励，然后在第一阶段将任务-价格对通知在线用户。在第二阶段，用户在一定的约束条件下选择最佳任务作为自己的兴趣并反馈给平台。平台公平地选择用户作为工人，以确保用户长期参与第三阶段。我们使用 Lyapunov 优化来解决第一阶段和第三阶段平台的在线决策问题，这些问题没有可用的先验知识和未来信息。我们提出了一个 FPTAS，让用户根据他们在第二阶段的移动设备的计算能力来获得他们对任务的兴趣。模拟中的数值结果验证了我们提出的激励机制的重要性和优越性。