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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/79616
Ver los metadatos del registro completo
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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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1842269641576022016 |
score |
13.13397 |