In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks

Autores
Riva, Guillermo Gaston; Finochietto, Jorge Manuel
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Data collection in wireless sensor networks (WSNs) can become extremely expensive in terms of power consumption if all measurements have to be fetched. However, since multiple applications do not require data from all nodes but to compute a function over a smaller data set, much of the available data on the network can be considered irrelevant and not worthy of spending energy. In this context, in-network filtering schemes can be used to forward only relevant data towards a sink node for processing purposes. In this work, we propose and evaluate two schemes that can drive this filtering process. Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. We investigate the trade-off between communications costs, which are directly associated with power consumption, and error costs due to fetching not all relevant data. We show by simulation that communication costs can be significantly reduced with respect to traditional schemes while keeping the computation error bounded.
Fil: Riva, Guillermo Gaston. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Finochietto, Jorge Manuel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
WIRELESS SENSOR NETWORKS
ROUTING PROTOCOLS
IN-NETWORK PROCESSING
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:11336/34656

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network_name_str CONICET Digital (CONICET)
spelling In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor NetworksRiva, Guillermo GastonFinochietto, Jorge ManuelWIRELESS SENSOR NETWORKSROUTING PROTOCOLSIN-NETWORK PROCESSINGhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Data collection in wireless sensor networks (WSNs) can become extremely expensive in terms of power consumption if all measurements have to be fetched. However, since multiple applications do not require data from all nodes but to compute a function over a smaller data set, much of the available data on the network can be considered irrelevant and not worthy of spending energy. In this context, in-network filtering schemes can be used to forward only relevant data towards a sink node for processing purposes. In this work, we propose and evaluate two schemes that can drive this filtering process. Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. We investigate the trade-off between communications costs, which are directly associated with power consumption, and error costs due to fetching not all relevant data. We show by simulation that communication costs can be significantly reduced with respect to traditional schemes while keeping the computation error bounded.Fil: Riva, Guillermo Gaston. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Finochietto, Jorge Manuel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaHindawi Publishing Corporation2014-08info: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/11336/34656Riva, Guillermo Gaston; Finochietto, Jorge Manuel; In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks; Hindawi Publishing Corporation; International Journal of Distributed Sensor Networks; 10; 8; 8-2014; 1-20; 2459241550-13291550-1477CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1155/2014/245924info:eu-repo/semantics/altIdentifier/url/http://journals.sagepub.com/doi/10.1155/2014/245924info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:57Zoai:ri.conicet.gov.ar:11336/34656instacron: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:34982025-09-03 10:06:57.606CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
title In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
spellingShingle In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
Riva, Guillermo Gaston
WIRELESS SENSOR NETWORKS
ROUTING PROTOCOLS
IN-NETWORK PROCESSING
title_short In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
title_full In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
title_fullStr In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
title_full_unstemmed In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
title_sort In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks
dc.creator.none.fl_str_mv Riva, Guillermo Gaston
Finochietto, Jorge Manuel
author Riva, Guillermo Gaston
author_facet Riva, Guillermo Gaston
Finochietto, Jorge Manuel
author_role author
author2 Finochietto, Jorge Manuel
author2_role author
dc.subject.none.fl_str_mv WIRELESS SENSOR NETWORKS
ROUTING PROTOCOLS
IN-NETWORK PROCESSING
topic WIRELESS SENSOR NETWORKS
ROUTING PROTOCOLS
IN-NETWORK PROCESSING
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Data collection in wireless sensor networks (WSNs) can become extremely expensive in terms of power consumption if all measurements have to be fetched. However, since multiple applications do not require data from all nodes but to compute a function over a smaller data set, much of the available data on the network can be considered irrelevant and not worthy of spending energy. In this context, in-network filtering schemes can be used to forward only relevant data towards a sink node for processing purposes. In this work, we propose and evaluate two schemes that can drive this filtering process. Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. We investigate the trade-off between communications costs, which are directly associated with power consumption, and error costs due to fetching not all relevant data. We show by simulation that communication costs can be significantly reduced with respect to traditional schemes while keeping the computation error bounded.
Fil: Riva, Guillermo Gaston. Universidad Tecnológica Nacional. Facultad Regional Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Finochietto, Jorge Manuel. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Data collection in wireless sensor networks (WSNs) can become extremely expensive in terms of power consumption if all measurements have to be fetched. However, since multiple applications do not require data from all nodes but to compute a function over a smaller data set, much of the available data on the network can be considered irrelevant and not worthy of spending energy. In this context, in-network filtering schemes can be used to forward only relevant data towards a sink node for processing purposes. In this work, we propose and evaluate two schemes that can drive this filtering process. Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. We investigate the trade-off between communications costs, which are directly associated with power consumption, and error costs due to fetching not all relevant data. We show by simulation that communication costs can be significantly reduced with respect to traditional schemes while keeping the computation error bounded.
publishDate 2014
dc.date.none.fl_str_mv 2014-08
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/11336/34656
Riva, Guillermo Gaston; Finochietto, Jorge Manuel; In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks; Hindawi Publishing Corporation; International Journal of Distributed Sensor Networks; 10; 8; 8-2014; 1-20; 245924
1550-1329
1550-1477
CONICET Digital
CONICET
url http://hdl.handle.net/11336/34656
identifier_str_mv Riva, Guillermo Gaston; Finochietto, Jorge Manuel; In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks; Hindawi Publishing Corporation; International Journal of Distributed Sensor Networks; 10; 8; 8-2014; 1-20; 245924
1550-1329
1550-1477
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1155/2014/245924
info:eu-repo/semantics/altIdentifier/url/http://journals.sagepub.com/doi/10.1155/2014/245924
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 Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
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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