Pheromone-based In-Network Processing for wireless sensor network monitoring systems

Autores
Riva, Guillermo Gaston; Finochietto, Jorge Manuel
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.
Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; Argentina
Materia
BIO-INSPIRED NETWORKING
COMPUTATIONAL INTELLIGENCE
IN-NETWORK FILTERING
MONITORING SYSTEMS
ROUTING ALGORITHMS AND PROTOCOLS
SWARM INTELLIGENCE
WIRELESS SENSOR NETWORKS
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:11336/79616

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network_name_str CONICET Digital (CONICET)
spelling Pheromone-based In-Network Processing for wireless sensor network monitoring systemsRiva, Guillermo GastonFinochietto, Jorge ManuelBIO-INSPIRED NETWORKINGCOMPUTATIONAL INTELLIGENCEIN-NETWORK FILTERINGMONITORING SYSTEMSROUTING ALGORITHMS AND PROTOCOLSSWARM INTELLIGENCEWIRELESS SENSOR NETWORKShttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; ArgentinaMacrothink Institute2012-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/79616Riva, Guillermo Gaston; Finochietto, Jorge Manuel; Pheromone-based In-Network Processing for wireless sensor network monitoring systems; Macrothink Institute; Network Protocols and Algorithms; 4; 4; 12-2012; 156-1731943-3581CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.5296/npa.v4i4.2206info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:00:29Zoai:ri.conicet.gov.ar:11336/79616instacron: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 10:00:29.99CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Pheromone-based In-Network Processing for wireless sensor network monitoring systems
title Pheromone-based In-Network Processing for wireless sensor network monitoring systems
spellingShingle Pheromone-based In-Network Processing for wireless sensor network monitoring systems
Riva, Guillermo Gaston
BIO-INSPIRED NETWORKING
COMPUTATIONAL INTELLIGENCE
IN-NETWORK FILTERING
MONITORING SYSTEMS
ROUTING ALGORITHMS AND PROTOCOLS
SWARM INTELLIGENCE
WIRELESS SENSOR NETWORKS
title_short Pheromone-based In-Network Processing for wireless sensor network monitoring systems
title_full Pheromone-based In-Network Processing for wireless sensor network monitoring systems
title_fullStr Pheromone-based In-Network Processing for wireless sensor network monitoring systems
title_full_unstemmed Pheromone-based In-Network Processing for wireless sensor network monitoring systems
title_sort Pheromone-based In-Network Processing for wireless sensor network monitoring systems
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 BIO-INSPIRED NETWORKING
COMPUTATIONAL INTELLIGENCE
IN-NETWORK FILTERING
MONITORING SYSTEMS
ROUTING ALGORITHMS AND PROTOCOLS
SWARM INTELLIGENCE
WIRELESS SENSOR NETWORKS
topic BIO-INSPIRED NETWORKING
COMPUTATIONAL INTELLIGENCE
IN-NETWORK FILTERING
MONITORING SYSTEMS
ROUTING ALGORITHMS AND PROTOCOLS
SWARM INTELLIGENCE
WIRELESS SENSOR NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.
Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; Argentina
Fil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; Argentina
description Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.
publishDate 2012
dc.date.none.fl_str_mv 2012-12
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/11336/79616
Riva, Guillermo Gaston; Finochietto, Jorge Manuel; Pheromone-based In-Network Processing for wireless sensor network monitoring systems; Macrothink Institute; Network Protocols and Algorithms; 4; 4; 12-2012; 156-173
1943-3581
CONICET Digital
CONICET
url http://hdl.handle.net/11336/79616
identifier_str_mv Riva, Guillermo Gaston; Finochietto, Jorge Manuel; Pheromone-based In-Network Processing for wireless sensor network monitoring systems; Macrothink Institute; Network Protocols and Algorithms; 4; 4; 12-2012; 156-173
1943-3581
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.5296/npa.v4i4.2206
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
dc.publisher.none.fl_str_mv Macrothink Institute
publisher.none.fl_str_mv Macrothink Institute
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 13.13397