Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas

Detalhes bibliográficos
Autor(a) principal: Alencar, Brenno de Mello
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Institucional da UFBA
Texto Completo: http://repositorio.ufba.br/ri/handle/ri/33634
Resumo: The Internet of Things (IoT) has produced infrastructures and applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, this data is generally processed and analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.
id UFBA-2_f11ecd034bcfe183ccea68178f5f6e5d
oai_identifier_str oai:repositorio.ufba.br:ri/33634
network_acronym_str UFBA-2
network_name_str Repositório Institucional da UFBA
repository_id_str 1932
spelling Alencar, Brenno de MelloAlencar, Brenno de MelloPrazeres, Cássio Vinícius SerafimRios, Ricardo AraújoMendonça Neto, Manoel Gomes deDelicato, Flavia Coimbra2021-06-25T20:07:16Z2021-06-25T20:07:16Z2021-06-252020-09-26http://repositorio.ufba.br/ri/handle/ri/33634The Internet of Things (IoT) has produced infrastructures and applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, this data is generally processed and analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.The Internet of Things (IoT) has produced infrastructures and applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing and provisioning of resources, this data is generally processed and analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.Submitted by Brenno de Mello Alencar (brennodemello.bm@gmail.com) on 2021-04-19T22:31:24Z No. of bitstreams: 1 Dissertao-Brenno-Mello-correcoes.pdf: 3490579 bytes, checksum: 1cf679d20de4bb82a9d14336807275c0 (MD5)Approved for entry into archive by Solange Rocha (soluny@gmail.com) on 2021-06-25T20:07:16Z (GMT) No. of bitstreams: 1 Dissertao-Brenno-Mello-correcoes.pdf: 3490579 bytes, checksum: 1cf679d20de4bb82a9d14336807275c0 (MD5)Made available in DSpace on 2021-06-25T20:07:16Z (GMT). No. of bitstreams: 1 Dissertao-Brenno-Mello-correcoes.pdf: 3490579 bytes, checksum: 1cf679d20de4bb82a9d14336807275c0 (MD5)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Ciências Exatas e da TerraCiência da ComputaçãoSistemas de ComputaçãoInternet das CoisasMineração de dados (Computação)Concept DriftWavelets (Matemática)Computação em NévoaNévoa das CoisasRedes neurais artificiaisFluxo de dados (Computadores)Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisUniversidade Federal da BahiaInstituto de Matemática e Estatísticaem Ciência da ComputaçãoUFBAbrasilinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFBAinstname:Universidade Federal da Bahia (UFBA)instacron:UFBAORIGINALDissertao-Brenno-Mello-correcoes.pdfDissertao-Brenno-Mello-correcoes.pdfapplication/pdf3490579https://repositorio.ufba.br/bitstream/ri/33634/1/Dissertao-Brenno-Mello-correcoes.pdf1cf679d20de4bb82a9d14336807275c0MD51LICENSElicense.txtlicense.txttext/plain1442https://repositorio.ufba.br/bitstream/ri/33634/2/license.txt817035eff4c4c7dda1d546e170ee2a1aMD52TEXTDissertao-Brenno-Mello-correcoes.pdf.txtDissertao-Brenno-Mello-correcoes.pdf.txtExtracted texttext/plain213404https://repositorio.ufba.br/bitstream/ri/33634/3/Dissertao-Brenno-Mello-correcoes.pdf.txt8f7c4eca2e5f758071fd0fa09ecde90eMD53ri/336342022-07-05 14:04:30.368oai:repositorio.ufba.br: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Repositório InstitucionalPUBhttp://192.188.11.11:8080/oai/requestopendoar:19322022-07-05T17:04:30Repositório Institucional da UFBA - Universidade Federal da Bahia (UFBA)false
dc.title.pt_BR.fl_str_mv Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
title Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
spellingShingle Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
Alencar, Brenno de Mello
Ciências Exatas e da Terra
Ciência da Computação
Sistemas de Computação
Internet das Coisas
Mineração de dados (Computação)
Concept Drift
Wavelets (Matemática)
Computação em Névoa
Névoa das Coisas
Redes neurais artificiais
Fluxo de dados (Computadores)
title_short Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
title_full Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
title_fullStr Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
title_full_unstemmed Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
title_sort Aprendizado de máquina para redução do tráfego de dados e da latência na névoa das coisas
author Alencar, Brenno de Mello
author_facet Alencar, Brenno de Mello
author_role author
dc.contributor.author.fl_str_mv Alencar, Brenno de Mello
Alencar, Brenno de Mello
dc.contributor.advisor1.fl_str_mv Prazeres, Cássio Vinícius Serafim
dc.contributor.advisor-co1.fl_str_mv Rios, Ricardo Araújo
dc.contributor.referee1.fl_str_mv Mendonça Neto, Manoel Gomes de
Delicato, Flavia Coimbra
contributor_str_mv Prazeres, Cássio Vinícius Serafim
Rios, Ricardo Araújo
Mendonça Neto, Manoel Gomes de
Delicato, Flavia Coimbra
dc.subject.cnpq.fl_str_mv Ciências Exatas e da Terra
Ciência da Computação
Sistemas de Computação
topic Ciências Exatas e da Terra
Ciência da Computação
Sistemas de Computação
Internet das Coisas
Mineração de dados (Computação)
Concept Drift
Wavelets (Matemática)
Computação em Névoa
Névoa das Coisas
Redes neurais artificiais
Fluxo de dados (Computadores)
dc.subject.por.fl_str_mv Internet das Coisas
Mineração de dados (Computação)
Concept Drift
Wavelets (Matemática)
Computação em Névoa
Névoa das Coisas
Redes neurais artificiais
Fluxo de dados (Computadores)
description The Internet of Things (IoT) has produced infrastructures and applications that generate large amounts of data. These data are usually data streams, that have the characteristic of being continuous and infinite and also have the peculiarity of modifying their behavior over time. Due to the large capacity of storage, data processing, and provisioning of resources, this data is generally processed and analyzed in cloud computing environments. Although Cloud Computing provides the IoT infrastructure with adequate scalability and resource centric features, the distance between devices and the cloud can impose limitations to achieve low latency in data traffic. In order to maintain scalability, achieve low latency and reduce data traffic between the IoT devices and the Cloud, the Fog Computing was proposed. Although the Fog Computing paradigm establishes resource availability at the edge of the network, the technologies and techniques currently used for IoT data processing and analysis may not be sufficient to support the continuous and unlimited data stream that IoT platforms produce. In this way, this work presents an approach for processing and analyzing data stream from the Internet of Things in real time in Fog. The main advantage of using our approach is the possibility of reducing the amount of data transmitted on the network infrastructure, which allows, as a consequence, to perform an online data modeling, by detecting changes in data behavior, and a reduction of the Internet usage. In addition, the proposed platform does not require a constant Internet connection. Finally, we evaluate the proposal from the perspective of performance in a scenario of intelligent objects at the edge of the network.
publishDate 2020
dc.date.submitted.none.fl_str_mv 2020-09-26
dc.date.accessioned.fl_str_mv 2021-06-25T20:07:16Z
dc.date.available.fl_str_mv 2021-06-25T20:07:16Z
dc.date.issued.fl_str_mv 2021-06-25
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.ufba.br/ri/handle/ri/33634
url http://repositorio.ufba.br/ri/handle/ri/33634
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal da Bahia
Instituto de Matemática e Estatística
dc.publisher.program.fl_str_mv em Ciência da Computação
dc.publisher.initials.fl_str_mv UFBA
dc.publisher.country.fl_str_mv brasil
publisher.none.fl_str_mv Universidade Federal da Bahia
Instituto de Matemática e Estatística
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFBA
instname:Universidade Federal da Bahia (UFBA)
instacron:UFBA
instname_str Universidade Federal da Bahia (UFBA)
instacron_str UFBA
institution UFBA
reponame_str Repositório Institucional da UFBA
collection Repositório Institucional da UFBA
bitstream.url.fl_str_mv https://repositorio.ufba.br/bitstream/ri/33634/1/Dissertao-Brenno-Mello-correcoes.pdf
https://repositorio.ufba.br/bitstream/ri/33634/2/license.txt
https://repositorio.ufba.br/bitstream/ri/33634/3/Dissertao-Brenno-Mello-correcoes.pdf.txt
bitstream.checksum.fl_str_mv 1cf679d20de4bb82a9d14336807275c0
817035eff4c4c7dda1d546e170ee2a1a
8f7c4eca2e5f758071fd0fa09ecde90e
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
repository.name.fl_str_mv Repositório Institucional da UFBA - Universidade Federal da Bahia (UFBA)
repository.mail.fl_str_mv
_version_ 1808459626504519680