基于节俭视角的周期性移动群体感知激励机制研究
An Incentive Mechanism for Periodical Mobile Crowdsensing from a Frugality Perspective
摘要：
移动人群感知（MCS）由于其灵活和普遍的感知能力，近年来得到了广泛的研究。虽然已经建立了许多激励机制来吸引广泛的用户参与，但这些机制大多只关注独立任务场景，其中感知任务彼此独立。相反，我们专注于一个周期性的任务的情况下，每个用户定期参与相同类型的传感任务。在本文中，我们考虑了长期用户参与激励一般期刊MCS系统从节俭支付的角度。我们探讨的问题下半在线（期间内的互动过程是同步的，而期间间的互动过程是顺序和异步在每个时期）和在线用户到达模型（前两个互动过程是顺序和异步）。特别是，我们首先提出了一个半在线节俭的激励机制，通过引入李雅普诺夫方法。此外，我们还将其扩展到一个在线的节俭激励机制，它满足恒定的节俭。此外，这两种机制还能满足计算效率、渐近最优性、个体理性和真实性等要求。通过大量的模拟，我们评估的性能和验证我们的在线机制的理论属性。
英文摘要：
Mobile crowdsensing (MCS) has been intensively explored recently due to its flexible and pervasive sensing ability. Although many incentive mechanisms have been built to attract extensive user participation, Most of these mechanisms focus only on independent task scenarios, where the sensing tasks are independent of each other. On the contrary, we focus on a periodical task scenario, where each user participates in the same type of sensing tasks periodically. In this paper, we consider the long-term user participation incentive in a general periodical MCS system from a frugality payment perspective. We explore the issue under both semi-online (the intra-period interactive process is synchronous while the inter-period interactive process is sequential and asynchronous during each period) and online user arrival models (the previous two interactive processes are sequential and asynchronous). In particular, we first propose a semi-online frugal incentive mechanism by introducing a Lyapunov method. Moreover, we also extend it to an online frugal incentive mechanism, which satisfies the constant frugality. Besides, the two mechanisms can also satisfy computational efficiency, asymptotical optimality, individual rationality and truthfulness. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
