Self-optimization of dense wireless sensor networks based on simulated annealing

Detalhes bibliográficos
Autor(a) principal: Pinto, A. R. [UNESP]
Data de Publicação: 2012
Outros Autores: Cansian, Adriano [UNESP], MacHado, José Marcio [UNESP], Montez, Carlos
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/LATW.2012.6261236
http://hdl.handle.net/11449/73652
Resumo: Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.
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spelling Self-optimization of dense wireless sensor networks based on simulated annealingSelf-OptimizationSimulated AnnealingWireless Sensor NetworksNon-intrusiveReal time requirementSelf-optimizationWireless communicationsNetwork managementSimulated annealingWireless sensor networksWireless telecommunication systemsSensor nodesWireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.DCCE UNESP Universidade Estadual Paulista, São José do Rio PretoUFSC Universidade Federal de Santa Catarina, FlorianópolisDCCE UNESP Universidade Estadual Paulista, São José do Rio PretoUniversidade Estadual Paulista (Unesp)Universidade Federal de Santa Catarina (UFSC)Pinto, A. R. [UNESP]Cansian, Adriano [UNESP]MacHado, José Marcio [UNESP]Montez, Carlos2014-05-27T11:27:06Z2014-05-27T11:27:06Z2012-10-05info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/LATW.2012.6261236LATW 2012 - 13th IEEE Latin American Test Workshop.http://hdl.handle.net/11449/7365210.1109/LATW.2012.62612362-s2.0-8486692498600959219433459740000-0003-4494-1454Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLATW 2012 - 13th IEEE Latin American Test Workshopinfo:eu-repo/semantics/openAccess2021-10-23T21:44:32Zoai:repositorio.unesp.br:11449/73652Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:04:46.107139Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Self-optimization of dense wireless sensor networks based on simulated annealing
title Self-optimization of dense wireless sensor networks based on simulated annealing
spellingShingle Self-optimization of dense wireless sensor networks based on simulated annealing
Pinto, A. R. [UNESP]
Self-Optimization
Simulated Annealing
Wireless Sensor Networks
Non-intrusive
Real time requirement
Self-optimization
Wireless communications
Network management
Simulated annealing
Wireless sensor networks
Wireless telecommunication systems
Sensor nodes
title_short Self-optimization of dense wireless sensor networks based on simulated annealing
title_full Self-optimization of dense wireless sensor networks based on simulated annealing
title_fullStr Self-optimization of dense wireless sensor networks based on simulated annealing
title_full_unstemmed Self-optimization of dense wireless sensor networks based on simulated annealing
title_sort Self-optimization of dense wireless sensor networks based on simulated annealing
author Pinto, A. R. [UNESP]
author_facet Pinto, A. R. [UNESP]
Cansian, Adriano [UNESP]
MacHado, José Marcio [UNESP]
Montez, Carlos
author_role author
author2 Cansian, Adriano [UNESP]
MacHado, José Marcio [UNESP]
Montez, Carlos
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal de Santa Catarina (UFSC)
dc.contributor.author.fl_str_mv Pinto, A. R. [UNESP]
Cansian, Adriano [UNESP]
MacHado, José Marcio [UNESP]
Montez, Carlos
dc.subject.por.fl_str_mv Self-Optimization
Simulated Annealing
Wireless Sensor Networks
Non-intrusive
Real time requirement
Self-optimization
Wireless communications
Network management
Simulated annealing
Wireless sensor networks
Wireless telecommunication systems
Sensor nodes
topic Self-Optimization
Simulated Annealing
Wireless Sensor Networks
Non-intrusive
Real time requirement
Self-optimization
Wireless communications
Network management
Simulated annealing
Wireless sensor networks
Wireless telecommunication systems
Sensor nodes
description Wireless sensor network (WSN) Is a technology that can be used to monitor and actuate on environments in a non-intrusive way. The main difference from WSN and traditional sensor networks is the low dependability of WSN nodes. In this way, WSN solutions are based on a huge number of cheap tiny nodes that can present faults in hardware, software and wireless communication. The deployment of hundreds of nodes can overcome the low dependability of individual nodes, however this strategy introduces a lot of challenges regarding network management, real-time requirements and self-optimization. In this paper we present a simulated annealing approach that self-optimize large scale WSN. Simulation results indicate that our approach can achieve self-optimization characteristics in a dynamic WSN. © 2012 IEEE.
publishDate 2012
dc.date.none.fl_str_mv 2012-10-05
2014-05-27T11:27:06Z
2014-05-27T11:27:06Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/LATW.2012.6261236
LATW 2012 - 13th IEEE Latin American Test Workshop.
http://hdl.handle.net/11449/73652
10.1109/LATW.2012.6261236
2-s2.0-84866924986
0095921943345974
0000-0003-4494-1454
url http://dx.doi.org/10.1109/LATW.2012.6261236
http://hdl.handle.net/11449/73652
identifier_str_mv LATW 2012 - 13th IEEE Latin American Test Workshop.
10.1109/LATW.2012.6261236
2-s2.0-84866924986
0095921943345974
0000-0003-4494-1454
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv LATW 2012 - 13th IEEE Latin American Test Workshop
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808128456104345600

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