INDIGO: Interest-Driven Data Dissemination Framework for Mobile Networks.

In this paper, we present INDIGO, an interest-driven data dissemination framework that enables to predict the performance of data dissemination. INDIGO computes a tight upper bound of data dissemination time under (i) heterogeneous mobility patterns of people and, (ii) interests-driven dissemination strategy. INDIGO automatically learns the interests of people from their browsing history and also captures their heterogeneous mobility patterns. We model data dissemination with a cut-off point based approach that mimics real-world data process by utilizing the long tail behavior of inter-contact time distribution. We validate INDIGO through our real-world traces and achieve a tight upper bound of data dissemination, within 2-18% of error. We further identify a long tail behavior of users' interest distribution, and use this finding to validate the scalability on large traces with synthetic interest. INDIGO can empower local businesses or publishers to assess the performance of their localized dissemination based services in advance and with high accuracy.