A Novel Methodology for designing Policies in Mobile Crowdsensing Systems.

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 modeled 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 in our structured population by including the concepts of homophily and heterogeneity. We have analytically defined and measured novel statistical measures of QoI and user reputation scores based on the evolutionary dynamics. Measures are distinct for the different configurations and higher for the most cooperative ones. Through the proposed methodology we have defined the core of a DSS for designing novel incentive mechanisms by operating on the policies in terms of QoI and user reputation scores.