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before running the Vicinity package, we advise the reader that he should either read Tiphaine Phe-Neau's PhD manuscript (available here) that explains most of the Vicinity packages potential analyses and its functionning, or the short summary proposed here (12 pages). Both of these documents explain the theoretical background needed to make to most of the Vicinity package for opportunistic networks.

The Vicinity package requires that your system has: Python 2.7 and Numpy >= 1.7. The Vicinity package also relies on the NetworkX >= 1.7 library but, we integrated its installation in the archive we provide. Therefore, you do not need to install it yourself. However, you must check that you do not have previous versions of NetworkX in your PYTHONPATH.

Using a connectivity dataset under the ONE format, the Vicinity package enables the generation of:

  1. all pairwise timelines,
  2. average dataset and all pairwise Vicinity Motion values: average intervals durations and transitional probabilities,
  3. synthetic pairwise timelines with TiGeR,
  4. pairwise distance predictions using our Vicinity Motion-based heuristic.

In the given archive, you will find the code as well as a short synthetic datasets that you can use to test the Vicinity package functioning.


= Website: - Implemented during my PhD Thesis @ UPMC Sorbonne Universites - LIP6 - NPA
= Work in collaboration with Marcelo Dias de Amorim (CNRS, France), Miguel Elias M. Campista (UFRJ, Brazil),
and Vania Conan (Thales Communications & Security, France).

      python <ONE scenario> <number of nodes> <max distance> <tiger timeline duration> <step forward value> 

To obtain the sources, please send us a mail at:
-> tiphaine.phe-neau[at]lip6[dot]fr or tiphaine[at]phe[hyphen]neau[dot]com.

To use the Vicinity package, you must first extract the archive in the 'vicinity' folder, and type the following commands in a terminal:

cd vicinity
python <ONE scenario> <number of nodes> <max distance> <tiger timeline duration> <step forward value>

The main command works as follows:

python <ONE scenario> <number of nodes> <max distance> <tiger timeline duration> <step forward value>

  • <ONE scenario>: the connectivity trace in the ONE format,
  • <number of nodes>: the number of traces in the connectivity trace,
  • <max distance>: the max K value to be analyzed for the K-vicinity,
  • <tiger timeline duration>: the expected timeline duration for TiGeR,
  • <step forward value>: for the Vicinity Motion-based heuristic, the number of future steps to be considered.

To test the Vicinity package's abilities, you can use the following commands on a 300 seconds long we provide. The chosen K value is 7 and we generate TiGeR synthetic timeliens of length 1500 seconds. We also issue prediction for the next 10 following steps.

python 61 7 1500 10

Details on each module of the Vicinity Package:

For the reviewers (restricted access)