Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.

中文翻译：

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移动人群感知中的博弈论：一项综合调查。

移动人群感应（MCS）是智能城市和物联网（IoT）数据的分布式采集中的新兴范例。MCS要求大量用户能够访问其移动设备中的内置传感器并共享感测数据，以确保大感测数据的高价值和高准确性。提高用户对MCS广告系列的参与度需要有效地增加用户，这是MCS平台成功的关键。由于MCS建立在非专用传感器上，因此无法保证数据的可信赖性，因为每个用户都有一种从参与中受益的个性化策略。同时，MCS平台致力于以较低的成本获取高度可靠的人群感知数据。这种现象在形成参与者池的用户之间以及参与者池与MCS平台之间引入了游戏。对各种博弈论方法的研究旨在为该问题提供稳定的解决方案。本文对不同的博弈论解决方案进行了全面的综述，这些解决方案解决了MCS中的以下问题，例如感知成本，数据质量，数据请求者与提供者之间的最优价格确定以及激励措施。我们提出了一种基于游戏理论的MCS平台解决方案分类法，其中主要基于Stackelberg，贝叶斯和演化博弈来制定问题。我们介绍了每个游戏达到平衡所使用的方法，在该问题上，问题的解决方案确保了游戏的每个参与者对其效用都感到满意，而无需改变其策略。对MCS的游戏理论解决方案进行分类的初始标准是基于参与者之间的合作和信息，而参与者可以是请求者或提供者。在对所调查方法进行彻底的定性比较之后，我们提供了有关这一活跃研究领域中开放区域和可能方向的见解。