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 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 multiscale characterisation of users (both global and local) represents a novel research direction and paves the way for novel policies on mobile crowdsensing systems.