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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/34656
Ver los metadatos del registro completo
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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 |
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CONICET Digital (CONICET) |
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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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