From social trust assisted reciprocity (STAR) to utility-optimal mobile crowdsensing

Crowdsensing has been widely recognized as a promising paradigm for numerous applications in mobile networks. To realize the full benefit of crowdsensing, one fundamental challenge is to incentivize users to participate. In this paper, we leverage social trust assisted reciprocity (STAR), a synergistic marriage of social trust and reciprocity, to develop an incentive mechanism in order to stimulate users´ participation. We investigate thoroughly the efficacy of STAR for satisfying users´ sensing requests, for a given social tie structure among users. Specifically, we first show that all requests can be satisfied if and only if sufficient social credit can be transferred from users who request more sensing services than what they can provide to users who can provide more than what they request. Then we investigate utility maximization for sensing services, and show that it boils down to maximizing the utility of a circulation flow in the combined graph of the social graph and request graph. Accordingly, we develop an algorithm that iteratively cancels cycles of positive weights in the residual graph, and thereby finds the optimal solution efficiently.