Tiphaine Phe-Neau Senior Data Scientist, Engineer, PhD in Computer Science. Let's have fun!

Vicinity Motion for Opportunistic Networks

Following our presentation at the 16th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (ACM MSWiM 2013), we provide the Vicinity Motion implementation to the research community. Vicinity Motion uses the notion of k-vicinity for opportunistic networks that we defined in a companion paper (ACM MobiOpp 2012). Vicinity Motion models the k-vicinity using a chain and observes nodes pairwise evolution in this k-vicinity. Our implementation takes network dynamics as input (contact traces) and provides a vicinity behavior dynamics capture.

vicinity motion workflow - PHE-NEAU

We present an example in the following figure. This chain states represent pairwise shortest distances for a given k-vicinity and edges show transitional probabilities between states within this k-vicinity. ∞ indicates the lack of end-to-end shortest path between two nodes, 1 indicates that two nodes are at a 1-hop shortest distance and so on. For the sake of clarity, we do not display all transitional probabilities here. In reality, the sum of all outgoing edges is equal to 1.

vicinity_motion_example-PHE-NEAU_Tiphaine-UPMC Sorbonne Universites


More details about Vicinity Motion in the corresponding paper or the commented poster linked below.
Do not hesitate to drop me an email about it!
→ tiphaine[dot]phe-neau[at]lip6[dot]fr

  • ♦ PaperPaper as a PDF
  • ♦ Poster (with audio commentary – to come)
  • ♦ Code (to come)

Work done in collaboration with Miguel Elias M. Campista (Universidade Federal do Rio de Janeiro, Brazil), Marcelo Dias de Amorim (CNRS & UPMC Sorbonne Universités, France), and Vania Conan (Thales Communications & Security, France). This work was partially funded by the European Community’s Seventh Framework Programme under grant agreement no. FP7-317959 MOTO. Miguel Elias M. Campista would like to thank CNPq, Faperj, CAPES, and FINEP for their financial support.