Personalized Content Sharing via Mobile Crowdsensing

Abstract

Personalized content sharing will inevitably become one of the core applications of mobile Internet of Things. However, the existing strategies for content sharing are far from effective content personalization, since they either fail to protect the diversity of shared content or harm the enthusiasm of users to participate in cooperation. How to optimize the tradeoff between content personalization and sharing efficiency thus becomes an extremely challenging problem. To circumvent this dilemma, we propose a social-aware personalized content-sharing strategy based on mobile crowdsensing (MCS), which specially introduces positive network externalities derived from MCS and the social network. Specifically, we design a two-stage pricing-participation game to model the interactions between mobile users and a profit-making service provider. By solving the subgame-perfect Nash equilibrium (NE) of the proposed game, an efficient participation mechanism and an optimal-pricing strategy are developed. First, users' decision selection of whether to join MCS is modeled as a social-aware MCS participation game (SA-MPG), and two algorithms for solving the Pareto-optimal NE of SA-MPG are designed. Subsequently, the pricing issue for network operators is investigated by exploiting the supermodularity of SA-MPG. Stochastic network model and real-world data set-based simulations corroborate the significant gain of our proposed strategy.