@inproceedings{sattari_multiple_2009,
 abstract = {In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. We combine and extend two ideas that have been developed independently. On one hand, network coding introduces topology-dependent correlation, which can then be exploited at the receivers to infer the topology. On the other hand, it has been shown that a traditional (i.e., without network coding) multiple source, multiple receiver tomography problem can be decomposed into multiple two source, two receiver subproblems. Our first contribution is to show that, when intermediate nodes perform network coding, topological information contained in network coded packets allows to accurately distinguish among all different 2-by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internet-like topology, and is robust to the presence of delay variability and packet loss.},
 type={4},
 author = {Sattari, P. and Markopoulou, Athina and Fragouli, C.},
 booktitle = {Network {Coding}, {Theory}, and {Applications}, 2009. {NetCod} '09. {Workshop} on},
 doi = {10.1109/netcod.2009.5191391},
 keywords = {network coding, encoding, multiple source multiple destination topology inference, network coded packets, network topology, receivers, routing protocols, telecommunication network topologycorrelation, tomographic techniques},
 month = {June},
 pages = {36--41},
 tags = {network_coding},
 title = {Multiple source multiple destination topology inference using network coding},
 year = {2009}
}

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On the other hand, it has been shown that a traditional (i.e., without network coding) multiple source, multiple receiver tomography problem can be decomposed into multiple two source, two receiver subproblems. Our first contribution is to show that, when intermediate nodes perform network coding, topological information contained in network coded packets allows to accurately distinguish among all different 2-by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. 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Workshop on","doi":"10.1109/netcod.2009.5191391","keywords":"network coding, encoding, multiple source multiple destination topology inference, network coded packets, network topology, receivers, routing protocols, telecommunication network topologycorrelation, tomographic techniques","month":"June","pages":"36–41","tags":"network_coding","title":"Multiple source multiple destination topology inference using network coding","year":"2009","bibtex":"@inproceedings{sattari_multiple_2009,\n abstract = {In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. We combine and extend two ideas that have been developed independently. On one hand, network coding introduces topology-dependent correlation, which can then be exploited at the receivers to infer the topology. On the other hand, it has been shown that a traditional (i.e., without network coding) multiple source, multiple receiver tomography problem can be decomposed into multiple two source, two receiver subproblems. Our first contribution is to show that, when intermediate nodes perform network coding, topological information contained in network coded packets allows to accurately distinguish among all different 2-by-2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. 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