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Information Dissemination using Opportunistic Networks in Scenarios with People Renewal

Abstract:

The diffusion of information using Opportunistic Networks relies mainly on users mobility. In fact, mobility creates the opportunities for contacts and therefore for data forwarding. The evaluation of this process in crowded spaces with people renewal, i.e. with users that can either enter or leave the evaluated scenario, is still basically unexplored, despite being one of the most suitable areas of application for these networks. In this paper, we focus on evaluating the dissemination of information in a subway station. We obtained real data from a local subway station through observation. These data were used in a pedestrian mobility simulator to generate people mobility traces to be used in an Opportunistic Network simulator. The analysis of the temporal and spatial characteristics of these traces reflects the realism of the generated scenario. In the experiments, we compare the direct delivery and epidemic diffusion protocols. As expected, epidemic diffusion compared to direct delivery increases drastically the number of users that receive the message. Furthermore, we propose an improvement of this protocol, called EpidemicX2 that is particularly effective for large messages.