A delay-tolerant network architecture for challenged internets
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
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Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs
IEEE Transactions on Mobile Computing
The ONE simulator for DTN protocol evaluation
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
MobiClique: middleware for mobile social networking
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HUBCODE: message forwarding using hub-based network coding in delay tolerant networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Peoplerank: social opportunistic forwarding
INFOCOM'10 Proceedings of the 29th conference on Information communications
BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks
IEEE Transactions on Mobile Computing
Bootstrapping opportunistic networks using social roles
WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
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IEEE Communications Magazine
Facencounter: Bridging the Gap between Offline and Online Social Networks
SITIS '12 Proceedings of the 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
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In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Most social-based routing protocols use social information extracted from real-world encounter networks to select an appropriate message relay. A protocol based on encounter history, however, takes time to build up a knowledge database from which to take routing decisions. An opportunistic routing protocol which extracts social information from multiple social networks, can be an alternative approach to avoid suboptimal paths due to partial information on encounters. While contact information changes constantly and it takes time to identify strong social ties, online social network ties remain rather stable and can be used to augment available partial contact information. In this paper, we propose a novel opportunistic routing approach, called ML-SOR (Multi-layer Social Network based Routing), which extracts social network information from multiple social contexts. To select an effective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. These metrics are computed by ML-SOR on different social network layers. Trace driven simulations show that ML-SOR, when compared to other schemes, is able to deliver messages with high probability while keeping overhead ratio very small.