Habit: Leveraging Human Mobility and Social Network for Efficient Content Dissemination in MANETs

Digital content (e.g., music, photos, videos) has become very easy to create and disseminate, thanks to the widespread success of powerful, networked handheld devices. As the volume of content being produced is increasing dramatically, it becomes crucial to be able to filter irrelevant information as early as possible; this is to avoid both network congestion and end-user overload. In order to achieve these goals, this paper proposes Habit, a novel cross-layered approach to content dissemination for mobile ad-hoc networks that leverages information about nodes’ colocation (physical layer) and their social network (application layer). More precisely, the regularity of users’ colocation is learned based on historical colocation observations; also, the users’ social network (or ‘network of interest’) is dynamically propagated during periods of colocation; finally, these distinct pieces of information are locally combined and used to compute the paths that content should follow, in a way that maximises both precision (i.e., nodes receive only content they are interested in) and recall (i.e., all relevant content is received by interested nodes). We demonstrate the effectiveness of the approach by means of simulation using a large set of real world traces.