Graph-Theoretic Approach for Increasing Participation in Social Sensing

Participatory sensing enables individuals, each with limited sensing capability, to share measurements and contribute towards developing a complete knowledge of their environment. The success of a participatory sensing application is often measured in terms of the number of users participating. In most cases, an individual's eagerness to participate depends on the group of users who already participate. For instance, when users share data with their peers in a social network, the engagement of an individual depends on its peers. Such engagement rules have been studied in the context of social networks using the concept of k-core, which assumes that participation is determined solely by network topology. However, in participatory sensing, engagement rules must also consider user heterogeneity, such as differences in sensing capabilities and physical location. To account for heterogeneity, we introduce the concept of (r, s)-core to model the set of participating users. We formulate the problem of maximizing the size of the (r, s)-core using 1) anchor users, who are incentivized to participate regardless of their peers, and by 2) assigning capabilities to users. Since these problems are computationally challenging, we study heuristic algorithms for solving them. Based on real-world social networks as well as random graphs, we provide numerical results showing significant improvement compared to random selection of anchor nodes and label assignments.