Finding the Right Tree: Topology Inference Despite Spatial Dependences

Bowden, R; Veitch, D

Finding the Right Tree: Topology Inference Despite Spatial Dependences

Bowden, R Veitch, D

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Information Theory, 2018, 64 (6), pp. 4594 - 4609
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Bowden, R	en_US
dc.contributor.author	Veitch, D https://orcid.org/0000-0001-8163-3464	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	IEEE Transactions on Information Theory, 2018, 64 (6), pp. 4594 - 4609	en_US
dc.identifier.issn	0018-9448	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122332
dc.description.abstract	© 1963-2012 IEEE. Network tomographic techniques have almost exclusively been built on a strong assumption of mutual independence of link processes. We introduce model classes for link loss processes with non-Trivial spatial dependencies, for which the tree topology is nonetheless identifiable from leaf measurements using multicast probing. We show that these classes are large in a well-defined sense, and we provide an algorithm, SLTD, capable of returning the correct topology with certainty in the limit of infinite data.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102834
dc.relation.ispartof	IEEE Transactions on Information Theory	en_US
dc.relation.isbasedon	10.1109/TIT.2017.2739779	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Finding the Right Tree: Topology Inference Despite Spatial Dependences	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	64	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	64	en_US

Abstract:

© 1963-2012 IEEE. Network tomographic techniques have almost exclusively been built on a strong assumption of mutual independence of link processes. We introduce model classes for link loss processes with non-Trivial spatial dependencies, for which the tree topology is nonetheless identifiable from leaf measurements using multicast probing. We show that these classes are large in a well-defined sense, and we provide an algorithm, SLTD, capable of returning the correct topology with certainty in the limit of infinite data.

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http://hdl.handle.net/10453/122332