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Analysis on binary loss tree classification with hop count for multicast topology discovery

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dc.contributor.authorTian, H.
dc.contributor.authorShen, H.
dc.contributor.conferenceIEEE Consumer Communications and Networking Conference (1st : 2004 : Las Vegas, Nevada)
dc.date.issued2004
dc.descriptionCopyright © 2004 IEEE
dc.description.abstractThe use of multicast inference on end-to-end measurement has recently been proposed as a means of obtaining the underlying multicast topology. We analyze the algorithm of binary loss tree classification with hop count (HBLT). We compare it with the binary loss tree classification algorithm (BLT) and show that the probability of misclassification of HBLT decreases more quickly than that of BLT as the number of probing packets increases. The inference accuracy of HBLT is always 1 (the inferred tree is identical to the physical tree) in the case of correct classification, whereas that of BLT is dependent on the shape of the physical tree and inversely proportional to the number of internal nodes with a single child. Our analytical result shows that HBLT is superior to BLT, not only on time complexity, but also on misclassification probability and inference accuracy.
dc.description.statementofresponsibilityHui Tian, Hong Shen
dc.identifier.citationCCNC2004 : 2004 1st IEEE Consumer Communications and Networking Conference : Consumer networking : closing the digital divide : proceedings : Caesar's Palace, Las Vegas, Nevada USA, 5-8 January 2004 / [Robert S. Fish, general chair], pp.164-168
dc.identifier.doi10.1109/CCNC.2004.1286852
dc.identifier.isbn0780381459
dc.identifier.orcidShen, H. [0000-0002-3663-6591] [0000-0003-0649-0648]
dc.identifier.urihttp://hdl.handle.net/2440/36945
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeOnline
dc.source.urihttp://dx.doi.org/10.1109/ccnc.2004.1286852
dc.titleAnalysis on binary loss tree classification with hop count for multicast topology discovery
dc.typeConference paper
pubs.publication-statusPublished

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