Mobile crowdsensing is a people-centric sensing system based on users’ contributions and incentive mechanisms aim at stimulating them. In our work, we have rethought the design of incentive mechanisms through a game-theoretic methodology. Thus, we have introduced a multi-layer social sensing framework, where humans as social sensors interact on multiple social layers and various services. We have proposed to weigh these dynamic interactions by including the concept of homophily, that is a human-related factor related to the similarity and frequency of interactions on the multiplex network. We have modelled the evolutionary dynamics of sensing behaviours by defining a mathematical framework based on multiplex EGT, quantifying the impact of homophily, network heterogeneity and various social dilemmas. We have detected the configurations of social dilemmas and network structures that lead to the emergence and sustainability of human cooperation. Moreover, we have defined and evaluated local and global Nash equilibrium points by including the concepts of homophily and heterogeneity. Therefore, we have analytically defined and measured novel statistical measures of social honesty, QoI and users’ behavioural reputation scores based on the evolutionary dynamics. Through the proposed methodology we have defined the Decision Support System (DSS) and a novel incentive mechanism by operating on the policies in terms of users’ reputation scores, that also incorporate users’ behaviours other than quality and quantity of contributions. To evaluate our methodology experimentally, we consider a real dataset on vehicular traffic monitoring crowdsensing application, Waze, and we have derived the disbursement of incentives by also comparing our method with baselines. Experimental results demonstrate that our methodology, based on both quality and quantity of reports and the local or microscopic spatio-temporal distribution of behaviours, is able to better discriminate users’ behaviours. This multi-scale characterisation of users (both global and local) represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems.

移动人群感知是一种以用户为中心的传感系统，它基于用户的贡献和旨在激励用户的激励机制。在我们的工作中，我们通过博弈论方法重新考虑了激励机制的设计。因此，我们引入了多层社会感知框架，其中人类作为社会传感器在多个社会层和各种服务上进行交互。我们已经提议通过包括同构概念来权衡这些动态交互，同构是与复用网络上交互的相似性和频率相关的人为因素。我们通过定义基于多重EGT的数学框架，量化同构性，网络异质性和各种社会困境的影响，对传感行为的演化动力学建模。我们已经发现了导致人们合作的出现和可持续性的社会困境和网络结构的配置。此外，我们通过包括同构和异质性的概念来定义和评估局部和全局Nash平衡点。因此，我们已经基于进化动力学分析地定义和测量了新的社会诚实度，QoI和用户行为声誉得分的统计量度。通过提出的方法，我们通过根据用户的信誉得分对政策进行操作，定义了决策支持系统（DSS）和一种新颖的激励机制，该机制还结合了用户的行为，而不是贡献的质量和数量。为了通过实验评估我们的方法，我们考虑了有关车辆交通监控人群感知应用程序Waze的真实数据集，并且还通过将我们的方法与基准进行了比较，得出了激励措施的支出。实验结果表明，基于报告的质量和数量以及行为的局部或微观时空分布，我们的方法能够更好地区分用户的行为。用户（全球和本地）的多尺度表征代表了一种新颖的研究方向，并为移动人群感知系统上的新政策铺平了道路。基于报告的质量和数量以及行为的本地或微观时空分布，可以更好地区分用户的行为。用户（全球和本地）的多尺度表征代表了一种新颖的研究方向，并为移动人群感知系统上的新政策铺平了道路。基于报告的质量和数量以及行为的本地或微观时空分布，可以更好地区分用户的行为。用户（全球和本地）的多尺度表征代表了一种新颖的研究方向，并为移动人群感知系统上的新政策铺平了道路。