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 and 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. We have
analytically defined and measured novel statistical measures of social honesty,
QoI and users' behavioural reputation scores based on the evolutionary
dynamics. We have defined the Decision Support System 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. Experimentally, we have considered the Waze dataset on vehicular
traffic monitoring application and derived the disbursement of incentives
comparing our method with baselines. Results demonstrate that our methodology,
which also includes the local (microscopic) spatio-temporal distribution of
behaviours, is able to better discriminate users' behaviours. This multi-scale
characterisation of users represents a novel research direction and paves the
way for novel policies on mobile crowdsensing systems