Social Forwarding in Large Scale Networks: Insights Based on Real Trace Analysis

Social forwarding, recently a hot topic in mobile opportunistic networking, faces extreme challenges from potentially large numbers of mobile nodes, vast areas, and limited communication resources. Such conditions render forwarding more challenging in large-scale networks. We observe that forwarding techniques based on social popularity fail to efficiently forward messages in large scale networks. The social popularity of nodes might not scale with the network size in a way that necessarily correlates with the contact opportunities and mobility patterns of these nodes. In this paper, we demonstrate, based on real mobility traces, the weakness of existing social forwarding algorithms in large scale communities. We address this weakness by proposing strategies for partitioning these large scale communities into sub-communities based on geographic locality or social interests. We also examine exploiting particular nodes, named MultiHomed nodes, in order to disseminate messages across these sub- communities. Finally, we introduce CAF, a Community Aware Forwarding framework, which can easily be integrated with the state-of-the-art social forwarding algorithms in order to improve their performance in large scale networks. We use real mobility traces to evaluate our proposed techniques. Our results empirically show a performance increase of around 40% and 5% to 30% better success delivery rates compared to state-of- the-art social forwarding algorithms, while incurring a marginal increase in cost.