具有相互依赖估值的真实移动人群感知

Meng Zhang, Brian Swenson, Jianwei Huang, H. V. Poor
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引用次数: 2

摘要

移动人群感知(MCS)通过利用许多移动用户的“智慧”来实现广泛的资源发现应用。然而,在许多应用中,用户的估值取决于其他用户的感官数据,这就引入了相互依赖估值的问题。这一特性可能导致感官数据误报,从而使经济机制具有挑战性。虽然已经做了一些工作来解决这个问题,但私人公用事业信息和通信开销的问题仍然没有得到解决。在本研究中,我们为资源发现MCS系统建立了第一个相互依赖的评估模型,旨在获得真实的感官报告和效用信息,并最大化预期的社会福利。我们设计了一种基于代理函数的真实感知和竞标(T-SAB)机制,该机制通过只要求每个用户提交每个资源的一维信令来揭示边际效用信息。我们证明了代理函数和奖励函数可以限制用户的误报意愿,当用户的信息规模较小时,这是大规模MCS系统中的一个合理条件。因此,我们的T-SAB机制产生了一个具有有效分配结果、近似真实、个人理性和近似预算平衡的完美贝叶斯均衡(PBE)。为了说明T-SAB机制的有效性,我们对认知无线电网络进行了一个案例研究。我们证明,与基准相比,T-SAB机制的社会福利收益可达到20%的社会福利收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Truthful mobile crowd sensing with interdependent valuations
Mobile crowd sensing (MCS) has been used to enable a wide range of resource-discovery applications by exploiting the "wisdom" of many mobile users. However, in many applications, a user's valuation depends on other users' sensory data, which introduces the problem of interdependent valuations. This feature can encourage sensory data misreport, hence makes economic mechanisms challenging. While some work has been done to address this problem, the issues of private utility information and communication overheads remain unsolved. In this study, we formulate the first interdependent-valuation model for the resource-discovery MCS systems, aiming to elicit truthful sensory reports and utility information and to maximize expected social welfare. We design a Truthful Sense-And-Bid (T-SAB) Mechanism based on surrogate functions, which can reveal marginal utility information by only requiring each user to submit one-dimensional signaling per resource. We show that the surrogate function and a reward function can limit users' willingness to misreport, when users have small informational sizes, a reasonable condition in large-scale MCS systems. Consequently, our T-SAB Mechanism yields a Perfect Bayesian Equilibrium (PBE) with the efficient allocation outcome, approximate truthfulness, individual rationality, and approximate budget balance. To illustrate the effectiveness of the T-SAB Mechanism, we perform a case study of a cognitive radio network. We demonstrate that the social welfare gain of the T-SAB Mechanism can achieve up to 20% social welfare gain comparing with a benchmark.
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