Impact of Human Mobility on Opportunistic Forwarding Algorithms
IEEE Transactions on Mobile Computing
CTG: a connectivity trace generator for testing the performance of opportunistic mobile systems
Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering
Designing mobility models based on social network theory
ACM SIGMOBILE Mobile Computing and Communications Review
Characterizing pairwise inter-contact patterns in delay tolerant networks
Proceedings of the 1st international conference on Autonomic computing and communication systems
Fast track article: From encounters to plausible mobility
Pervasive and Mobile Computing
Characterising aggregate inter-contact times in heterogeneous opportunistic networks
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part II
Density-Aware Routing in Highly Dynamic DTNs: The RollerNet Case
IEEE Transactions on Mobile Computing
Vicinity-based DTN characterization
Proceedings of the third ACM international workshop on Mobile Opportunistic Networks
Fine-grained intercontact characterization in disruption-tolerant networks
ISCC '11 Proceedings of the 2011 IEEE Symposium on Computers and Communications
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Modeling the dynamics of opportunistic networks generally relies on the dual notion of contacts and intercontacts between nodes. We advocate the use of an extended view in which nodes track their vicinity (within a few hops) and not only their direct neighbors. Contrary to existing approaches in the literature in which contact patterns are derived from the spatial mobility of nodes, we directly address the topological properties avoiding any intermediate steps. To the best of our knowledge, this paper presents the first study to ever focus on vicinity motion. We apply our method to several real-world and synthetic datasets to extract interesting patterns of vicinity. We provide an original workflow and intuitive modeling to understand a node's surroundings. Then, we highlight two main vicinity chains behaviors representing all the datasets we observed. Finally, we identify three main types of movements (birth, death, and sequential). These patterns represent up to 87% of all observed vicinity movements.