Source Localization and Network Topology Discovery in Infection Networks

Bibliographic Details
Main Author: Hao, He
Publication Date: 2018
Other Authors: Silvestre, Daniel, Silvestre, Carlos
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/11144/3923
Summary: Determining the network topology is typically a challenging problem due to the number of nodes and connection between them. Complexity is added whenever this identification problem relies solely on a subset of the outputs of some dynamical system or distributed algorithm running on those nodes. In this paper, we focus on both the source identification and network topology discovery problems in the context of infection networks where a subset of the nodes are elected as observers. The solution consists in writing the binary constraints associated with the problem. Convex relaxations are also proposed and investigated through simulations where a pattern emerges that placing observers in high-degree nodes increases the accuracy of the method.
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spelling Source Localization and Network Topology Discovery in Infection Networkscomputer networkstelecommunication network topologyhigh-degree nodessource localizationnetwork topology discoveryinfection networksidentification problemsource identificationObserversStandardsOptimization;TopologyMathematical modelObservabilityNetwork topologyDetermining the network topology is typically a challenging problem due to the number of nodes and connection between them. Complexity is added whenever this identification problem relies solely on a subset of the outputs of some dynamical system or distributed algorithm running on those nodes. In this paper, we focus on both the source identification and network topology discovery problems in the context of infection networks where a subset of the nodes are elected as observers. The solution consists in writing the binary constraints associated with the problem. Convex relaxations are also proposed and investigated through simulations where a pattern emerges that placing observers in high-degree nodes increases the accuracy of the method.IEEE2018-11-08T17:58:18Z2018-07-01T00:00:00Z2018-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11144/3923eng1934-176810.23919/ChiCC.2018.8482274Hao, HeSilvestre, DanielSilvestre, Carlosinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-08-01T01:59:20Zoai:repositorio.ual.pt:11144/3923Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:39:23.188795Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Source Localization and Network Topology Discovery in Infection Networks
title Source Localization and Network Topology Discovery in Infection Networks
spellingShingle Source Localization and Network Topology Discovery in Infection Networks
Hao, He
computer networks
telecommunication network topology
high-degree nodes
source localization
network topology discovery
infection networks
identification problem
source identification
Observers
Standards
Optimization;
Topology
Mathematical model
Observability
Network topology
title_short Source Localization and Network Topology Discovery in Infection Networks
title_full Source Localization and Network Topology Discovery in Infection Networks
title_fullStr Source Localization and Network Topology Discovery in Infection Networks
title_full_unstemmed Source Localization and Network Topology Discovery in Infection Networks
title_sort Source Localization and Network Topology Discovery in Infection Networks
author Hao, He
author_facet Hao, He
Silvestre, Daniel
Silvestre, Carlos
author_role author
author2 Silvestre, Daniel
Silvestre, Carlos
author2_role author
author
dc.contributor.author.fl_str_mv Hao, He
Silvestre, Daniel
Silvestre, Carlos
dc.subject.por.fl_str_mv computer networks
telecommunication network topology
high-degree nodes
source localization
network topology discovery
infection networks
identification problem
source identification
Observers
Standards
Optimization;
Topology
Mathematical model
Observability
Network topology
topic computer networks
telecommunication network topology
high-degree nodes
source localization
network topology discovery
infection networks
identification problem
source identification
Observers
Standards
Optimization;
Topology
Mathematical model
Observability
Network topology
description Determining the network topology is typically a challenging problem due to the number of nodes and connection between them. Complexity is added whenever this identification problem relies solely on a subset of the outputs of some dynamical system or distributed algorithm running on those nodes. In this paper, we focus on both the source identification and network topology discovery problems in the context of infection networks where a subset of the nodes are elected as observers. The solution consists in writing the binary constraints associated with the problem. Convex relaxations are also proposed and investigated through simulations where a pattern emerges that placing observers in high-degree nodes increases the accuracy of the method.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-08T17:58:18Z
2018-07-01T00:00:00Z
2018-07
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/11144/3923
url http://hdl.handle.net/11144/3923
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1934-1768
10.23919/ChiCC.2018.8482274
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833597571366387712

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