Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs

Autores
Maya, Juan Augusto; Rey Vega, Leonardo Javier; Galarza, Cecilia Gabriela
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.
Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Galarza, Cecilia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
Materia
DISTRIBUTED DETECTION
MAC
ENERGY
BANDWIDTH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/253936
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNsMaya, Juan AugustoRey Vega, Leonardo JavierGalarza, Cecilia GabrielaDISTRIBUTED DETECTIONMACENERGYBANDWIDTHhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; ArgentinaFil: Galarza, Cecilia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; ArgentinaInstitute of Electrical and Electronics Engineers2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/253936Maya, Juan Augusto; Rey Vega, Leonardo Javier; Galarza, Cecilia Gabriela; Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs; Institute of Electrical and Electronics Engineers; IEEE Transactions On Signal Processing; 63; 8; 4-2015; 2057-20691053-587XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7050369info:eu-repo/semantics/altIdentifier/doi/10.1109/TSP.2015.2407323info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:41:13Zoai:ri.conicet.gov.ar:11336/253936instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:41:13.565CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
title Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
spellingShingle Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
Maya, Juan Augusto
DISTRIBUTED DETECTION
MAC
ENERGY
BANDWIDTH
title_short Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
title_full Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
title_fullStr Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
title_full_unstemmed Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
title_sort Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs
dc.creator.none.fl_str_mv Maya, Juan Augusto
Rey Vega, Leonardo Javier
Galarza, Cecilia Gabriela
author Maya, Juan Augusto
author_facet Maya, Juan Augusto
Rey Vega, Leonardo Javier
Galarza, Cecilia Gabriela
author_role author
author2 Rey Vega, Leonardo Javier
Galarza, Cecilia Gabriela
author2_role author
author
dc.subject.none.fl_str_mv DISTRIBUTED DETECTION
MAC
ENERGY
BANDWIDTH
topic DISTRIBUTED DETECTION
MAC
ENERGY
BANDWIDTH
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.
Fil: Maya, Juan Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina
Fil: Rey Vega, Leonardo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Electronica; Argentina
Fil: Galarza, Cecilia Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Simulación Computacional para Aplicaciones Tecnológicas; Argentina
description We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN) for detecting a stationary random process distributed both in space and time with a circularly-symmetric complex Gaussian distribution under the Neyman-Pearson (NP) framework. Using an analog scheme, the sensors transmit different linear combinations of their measurements through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. Considering an energy constraint on each node transmission and a limited amount of channel uses, we compute the miss error exponent of the proposed scheme using Large Deviation Theory (LDT) and show that the proposed strategy is asymptotically optimal (when the number of sensors approaches infinity) among linear orthogonal schemes. We also show that the proposed scheme obtains meaningful energy saving in the low signal-to-noise ratio regime, which is the typical scenario of WSNs. Finally, a Monte Carlo simulation of a 2-dimensional process in space validates the analytical results.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/253936
Maya, Juan Augusto; Rey Vega, Leonardo Javier; Galarza, Cecilia Gabriela; Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs; Institute of Electrical and Electronics Engineers; IEEE Transactions On Signal Processing; 63; 8; 4-2015; 2057-2069
1053-587X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/253936
identifier_str_mv Maya, Juan Augusto; Rey Vega, Leonardo Javier; Galarza, Cecilia Gabriela; Optimal Resource Allocation for Detection of a Gaussian Process Using a MAC in WSNs; Institute of Electrical and Electronics Engineers; IEEE Transactions On Signal Processing; 63; 8; 4-2015; 2057-2069
1053-587X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ieeexplore.ieee.org/document/7050369
info:eu-repo/semantics/altIdentifier/doi/10.1109/TSP.2015.2407323
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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