The Vicinity Package for Opportunistic Networks

Tiphaine Phe-Neau 1
1 NPA - Networks and Performance Analysis
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Opportunistic networks have grown into a major trend in the wireless network research field. In our work, we relaxed the traditional definition of contact and intercontact times by bringing the notion of vicinity into the game. We first propose to analyze opportunistic/disruption-tolerant networks (DTN) using a node-centered vicinity point of view i.e. we consider that nodes are in k-contact when they remain within a few hops from each other and in k-intercontact otherwise (where k is the maximum number of hops characterizing the vicinity). Second, we focused on the inner vicinity behaviors namely Vicinity Motion and on how we could reproduce these patterns to generate dataset-based synthetic pairwise vicinity movements. Finally, since the main use of opportunistic networks is to transmit data, we investigated the use of vicinity knowledge into pairwise shortest distance prediction and found out a very interesting heuristic relying on Vicinity Motion. We packaged most of our contributions concerning vicinity knowledge for opportunistic networks in the Vicinity Package written using Python 2.7 and libraries such as NetworkX and NumPy. This paper reviews the theoretical knowledge required to use the Vicinity Package.
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Tiphaine Phe-Neau. The Vicinity Package for Opportunistic Networks. 2014. ⟨hal-01052728⟩

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