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prob.fwding

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https://github.com/vinkumbr/prob.fwding

Science Score: 26.0%

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  • Host: GitHub
  • Owner: vinkumbr
  • Language: Python
  • Default Branch: master
  • Size: 267 MB
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Created about 6 years ago · Last pushed over 3 years ago
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Readme Citation

README.md

Prob_fwding

Required packages 1. numpy 2. mpi 3. random

This repository has four subfolders corresponding to trees, grids, random geometric graphs (RGG) and other general graphs (Graphs). In each subfolder, the graph is specified using M - Reduced adjacency matrix for a graph: The i-th roww of the matrix contains the neighburs of vertex i. Vertex 0 is often taken to be the source.

The main code which implements probabilistic forwarding to find the minimum forwarding probability is probfwdingparallel.py. To adapt it to different graph structures the matrix M has to be changed. To find the expected total number of transmissions, use transfrompkndeltaparallel.py with the probability values obtained using the corresponding probfwding_parallel.py code.

Use the command mpirun -n 100 python filename.py to run a file.

References: 1. B. R. Vinay Kumar and N. Kashyap, Probabilistic forwarding of coded packets on networks - IEEE/ACM Transactions on Networking, vol. 29, no. 1, pp. 234-247, 2021. 2. B. R. Vinay Kumar, N. Kashyap and D. Yogeshwaran, An analysis of probabilistic forwarding of coded packets on random geometric graphs, accepted at WiOpt 2021, Philadelphia, October 18-21, 2021. 3. B. R. Vinay Kumar and N. Kashyap, Probabilistic forwarding of coded packets on networks, in Proc. 2019 International Symposium on Information Theory (ISIT 2019), Paris, Jul 7-12, 2019.
4. B. R. Vinay Kumar, R. Antony and N. Kashyap, The Effect of Introducing Redundancy in a Probabilistic Forwarding Protocol, in Proc. 2018 National Conference on Communications (NCC 2018), IIT- Hyderabad, Feb 25-28, 2018

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