Time Critical Content Delivery Using Predictable Patterns in Mobile Social Networks

In mobile social networks (MSN) individuals with similar interests or commonalities connect to each other using the mobile phones. MSN are special kind of ad-hoc networks in which wireless connectivity (i.e. encounters and re-encounters) with social peers is predictable. Most of the recent research proposes routing framework to exploit these predictable patterns to identify the best information carriers. The best information carriers are selected based on the high probability of encounter with potential information recipients. In these approaches an important variable is ignored i.e. time of encounter. Considering encounter time in the protocol gives time assurance of message delivery for time critical application. Therefore in this paper we address the research question, how to exploit people´s predictable social patterns to improve the content delivery performance and lower end-to-end delay in time critical applications? Our assumption is that the people follow similar mobility patterns daily (i.e. Monday to Friday). In this paper we verify our research question and assumption using real trace data of 100 users carrying Nokia 6600 smart phones over the course of nine months. Furthermore we model, analyze and propose algorithms for social encounter based content delivery system for time critical applications. The simple heuristics and initial study presented in this paper achieve a timely content delivery using predictable patterns while lowering system wide traffic flooding.