CAF: Community aware framework for large scale mobile opportunistic networks

  • Authors:

  • Abderrahmen Mtibaa;Khaled A. Harras

  • Affiliations:

  • Department of Computer Science, Carnegie Mellon University, Qatar;Department of Computer Science, Carnegie Mellon University, Qatar

  • Venue:
  • Computer Communications
  • Year:
  • 2013

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Abstract

The fundamental challenge in opportunistic networking is when and how to forward a message. Rank-based forwarding, one of the most promising methods for addressing this challenge, ranks nodes based on their social profiles or contact history in order to identify the most suitable forwarders. While these forwarding techniques have demonstrated great performance trends, we observe that they fail to efficiently forward messages in large scale networks. In this paper, we demonstrate using real mobility traces, the weakness of existing rank-based forwarding algorithms in large scale communities. We propose strategies for partitioning large communities into sub-communities based on geographic locality or social interests. We also propose exploiting particular nodes, named MultiHomed nodes, in order to disseminate messages across these sub-communities. We introduce CAF, a Community Aware Forwarding framework, which is designed to be integrated with state-of-the-art rank-based 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 delivery success rate increase of up to 40%, along with 5% to 30% improved success delivery rates compared to state-of-the-art rank-based forwarding algorithms; these results are obtained while incurring a marginal increase in cost which is less than 10%. We finally propose an extension of the original framework called Community Destination Aware Framework (CDAF). Assuming that the source node can determine the destination's community, CDAF further reduces the cost of CAF by a factor of 2 while maintaining similar success rates.