Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing

Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring and spectrum sensing). In this study, a socially-aware crowdsensing system is advocated in which a cloud-based platform incentivizes mobile users to participate in sensing tasks by leveraging social trust among users, upon receiving sensing requests. For this system, social trust assisted reciprocity (STAR), a synergistic marriage of social trust and reciprocity, is exploited to design an incentive mechanism that stimulates users' participation. Given the social trust structure among users, the efficacy of STAR for satisfying users' sensing requests is thoroughly investigated. Specifically, it is first shown that all requests can be satisfied if and only if sufficient social credit can be 'transferred' from users who request more sensing service than they can provide to users who can provide more than they request. Then utility maximization for sensing services under STAR is investigated, and it is shown that it reduces to maximizing the utility of a circulation flow in the combined social graph and request graph. Accordingly, an algorithm that iteratively cancels a cycle of positive weight in the residual graph is developed, which computes the optimal solution efficiently, for both cases of divisible and indivisible sensing service. Extensive simulation results corroborate that STAR can significantly outperform the mechanisms using social trust only or reciprocity only.