The dynamics of internet of things
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/41411 https://orcid.org/0000-0001-6497-1613 |
Resumo: | This thesis investigates the dynamical behavior of time series data from Internet of Things (IoT) sensors. Because of the growing number of IoT initiatives, with its impressive number of devices collecting a large amount of data from real-world phenomena, there is an imminent need for solutions that are adequate to their issues. For instance, an important part of the current IoT is the Collaborative Internet of Things (CoIoT), which is mainly composed by cheap components and managed by common users, affecting the generated data. Thus, solutions for IoT must consider improving the security of those devices, as well as the quality and reliability their data, but being efficient and robust to the issues from this novel scenario. A subject that has been successfully used for a deeper comprehension of several real-world phenomena is the study of dynamics, which aims to understand systems that evolve in time. An important tool with solid results concerning the analysis of time series dynamics is the ordinal patterns transformation. However, while the dynamics has the potential to be the basis for novel representation domains to the analysis of IoT data, there are issues on their transformations that must be handled for their proper applicability. This work aims to advance the state-of-the-art in the analysis of time series dynamics, to be adequate for the IoT issues, and to propose solutions based on dynamical behavior for a more reliable use of data from IoT. In order to advance the applicability of ordinal patterns transformations for challenging scenarios, such as IoT, we propose strategies in two main directions. A first strategy is aimed to provide minimum dependency on the selection of parameters by the transformation, by considering the multiscale behavior of a novel proposed metric, the probability of self-transitions, which are shown to be useful for the distinction of time series dynamics. The second strategy consists of a class separability index, which is a valuable method to estimate the most adequate parameters for the ordinal patterns transformations, in the context of time series classification problems. With respect to the application of the analysis of time series dynamics to IoT scenarios, we first give an enlightenment on the CoIoT. We provide a better understanding on the main characteristics and properties of data that are being generated by their sensors and its inherent problems. Then, we provide strategies for the classification of physical phenomena data collected by CoIoT sensors and a method to increase the security of IoT devices against botnet attacks, both considering their dynamical behavior. The proposed strategies were compared to related work and the results show their potentials on advancing the applicability of ordinal patterns transformations for the IoT scenarios. We show that the construction of this novel representation helps in the scalability, avoiding comparisons with a large number of data, and being robust to the problems of CoIoT data. Thus, by following these approaches, it is possible to develop solutions for IoT scenarios that can benefit from the unique aspects of dynamical systems. |
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Antonio Alfredo Ferreira Loureirohttp://lattes.cnpq.br/8886634592087842Heitor Soares Ramos FilhoAlejandro César Frery OrgambideOsvaldo Anibal RossoThaís Vasconcelos BatistaPedro Olmo Stancioli Vaz de MeloMario Sérgio Ferreira Alvim Júniorhttp://lattes.cnpq.br/3102308378811852João Batista Borges Neto2022-05-05T20:58:59Z2022-05-05T20:58:59Z2021-11-25http://hdl.handle.net/1843/41411https://orcid.org/0000-0001-6497-1613This thesis investigates the dynamical behavior of time series data from Internet of Things (IoT) sensors. Because of the growing number of IoT initiatives, with its impressive number of devices collecting a large amount of data from real-world phenomena, there is an imminent need for solutions that are adequate to their issues. For instance, an important part of the current IoT is the Collaborative Internet of Things (CoIoT), which is mainly composed by cheap components and managed by common users, affecting the generated data. Thus, solutions for IoT must consider improving the security of those devices, as well as the quality and reliability their data, but being efficient and robust to the issues from this novel scenario. A subject that has been successfully used for a deeper comprehension of several real-world phenomena is the study of dynamics, which aims to understand systems that evolve in time. An important tool with solid results concerning the analysis of time series dynamics is the ordinal patterns transformation. However, while the dynamics has the potential to be the basis for novel representation domains to the analysis of IoT data, there are issues on their transformations that must be handled for their proper applicability. This work aims to advance the state-of-the-art in the analysis of time series dynamics, to be adequate for the IoT issues, and to propose solutions based on dynamical behavior for a more reliable use of data from IoT. In order to advance the applicability of ordinal patterns transformations for challenging scenarios, such as IoT, we propose strategies in two main directions. A first strategy is aimed to provide minimum dependency on the selection of parameters by the transformation, by considering the multiscale behavior of a novel proposed metric, the probability of self-transitions, which are shown to be useful for the distinction of time series dynamics. The second strategy consists of a class separability index, which is a valuable method to estimate the most adequate parameters for the ordinal patterns transformations, in the context of time series classification problems. With respect to the application of the analysis of time series dynamics to IoT scenarios, we first give an enlightenment on the CoIoT. We provide a better understanding on the main characteristics and properties of data that are being generated by their sensors and its inherent problems. Then, we provide strategies for the classification of physical phenomena data collected by CoIoT sensors and a method to increase the security of IoT devices against botnet attacks, both considering their dynamical behavior. The proposed strategies were compared to related work and the results show their potentials on advancing the applicability of ordinal patterns transformations for the IoT scenarios. We show that the construction of this novel representation helps in the scalability, avoiding comparisons with a large number of data, and being robust to the problems of CoIoT data. Thus, by following these approaches, it is possible to develop solutions for IoT scenarios that can benefit from the unique aspects of dynamical systems.Este trabalho investiga o comportamento dinâmico dos dados de sensores na Internet das Coisas (IoT, do inglês Internet of Things). Devido ao crescente número de iniciativas na IoT, com seu impressionante número de dispositivos coletando um grande volume de dados de fenômenos do mundo real, há uma iminente necessidade de soluções adequadas aos seus desafios. Uma parte importante da atual IoT é a Internet das Coisas Colaborativa (CoIoT, do inglês Collaborative IoT), que é composta, principalmente, por componentes baratos e mantidos por usuários comuns, afetando os dados gerados. Assim, soluções para a IoT devem considerar o aprimoramento da segurança de seus dispositivos, bem como a qualidade e confiabilidade dos seus dados, mas sendo a eficiência e robusto aos desafios deste novo cenário. Um tópico que vem sendo usado com sucesso para compreender mais profundamente fenômenos do mundo real é o estudo da dinâmica, que visa entender como sistemas evoluem com o tempo. Uma importante ferramenta com sólidos resultados na análise da dinâmica de séries temporais é a transformação de padrões ordinais. Contudo, embora a dinâmica tenha o potencial de servir de base para novos domínios de representação para a análise de dados na IoT, há questões em suas transformações que devem ser tratadas para sua aplicação adequada.a Este trabalho tem como objetivos avançar o estado da arte na análise da dinâmica de séries temporais, em sua adequação para os desafios da IoT, e propor soluções baseadas em comportamentos dinâmicos para o uso mais confiável dos dados da IoT. Para avançar na aplicabilidade das transformações de padrões ordinais para cenários desafiadores, como é o caso da IoT, são propostas estratégias em duas principais direções. Uma primeira estratégia tem como objetivo prover a mínima dependência na seleção de parâmetros na transformação, considerando o comportamento multiescala de uma nova métrica proposta, a probabilidade de auto transições, que se mostraram úteis na distinção de dinâmicas de séries temporais. A segunda estratégia consiste em um índice de separabilidade de classes, que é um valioso método para estimar os parâmetros mais adequados para as transformações de padrões ordinais, no contexto da classificação de séries temporais. Em respeito à aplicação da análise da dinâmica de séries temporais para os cenários de IoT, primeiramente são dados esclarecimentos quanto ao contexto da CoIoT. Nós provemos um melhor entendimento sobre as principais características e propriedades dos dados gerados por seus sensores e seus principais problemas. Em seguida, são propostas estratégias para a classificação de dados de fenômenos físicos coletados pelos sensores da CoIoT e um método para incrementar a segurança dos dispositivos da IoT contra ataques de botnet, ambos considerando seus comportamentos dinâmicos. As estratégias propostas foram comparadas com trabalhos relacionados e os resultados demonstraram seus potenciais no avanço da aplicabilidade das transformações de padrões ordinais para os cenários da IoT. Nós mostramos que a construção desta nova representação auxilia na escalabilidade, evitando comparações com uma grande quantidade de dados, sendo robusta para os problemas dos dados da CoIoT. Assim, por meio dessas abordagens, é possível desenvolver soluções para a IoT que podem se beneficiar dos aspectos únicos de sistemas dinâmicos.engUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em Ciência da ComputaçãoUFMGBrasilICEX - INSTITUTO DE CIÊNCIAS EXATAShttp://creativecommons.org/licenses/by-nc-nd/3.0/pt/info:eu-repo/semantics/openAccessComputação - TesesInternet das coisas - TesesSistemas colaborativos - TesesAnálise de séries temporais - TeseInternet of ThingsCollaborative sensingTime series dynamicsOrdinal patterns transformationsThe dynamics of internet of thingsA dinâmica da internet das coisasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALJoao_Borges-Tese-PPGCC-DCC_PDFA.pdfJoao_Borges-Tese-PPGCC-DCC_PDFA.pdfVersão em PDFAapplication/pdf8991545https://repositorio.ufmg.br/bitstream/1843/41411/2/Joao_Borges-Tese-PPGCC-DCC_PDFA.pdf4b5e44fb65b50a407bc267417bb7c06dMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufmg.br/bitstream/1843/41411/3/license_rdfcfd6801dba008cb6adbd9838b81582abMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-82118https://repositorio.ufmg.br/bitstream/1843/41411/4/license.txtcda590c95a0b51b4d15f60c9642ca272MD541843/414112022-05-05 17:59:00.004oai:repositorio.ufmg.br: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ório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-05-05T20:59Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
The dynamics of internet of things |
dc.title.alternative.pt_BR.fl_str_mv |
A dinâmica da internet das coisas |
title |
The dynamics of internet of things |
spellingShingle |
The dynamics of internet of things João Batista Borges Neto Internet of Things Collaborative sensing Time series dynamics Ordinal patterns transformations Computação - Teses Internet das coisas - Teses Sistemas colaborativos - Teses Análise de séries temporais - Tese |
title_short |
The dynamics of internet of things |
title_full |
The dynamics of internet of things |
title_fullStr |
The dynamics of internet of things |
title_full_unstemmed |
The dynamics of internet of things |
title_sort |
The dynamics of internet of things |
author |
João Batista Borges Neto |
author_facet |
João Batista Borges Neto |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Antonio Alfredo Ferreira Loureiro |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8886634592087842 |
dc.contributor.advisor-co1.fl_str_mv |
Heitor Soares Ramos Filho |
dc.contributor.referee1.fl_str_mv |
Alejandro César Frery Orgambide |
dc.contributor.referee2.fl_str_mv |
Osvaldo Anibal Rosso |
dc.contributor.referee3.fl_str_mv |
Thaís Vasconcelos Batista |
dc.contributor.referee4.fl_str_mv |
Pedro Olmo Stancioli Vaz de Melo |
dc.contributor.referee5.fl_str_mv |
Mario Sérgio Ferreira Alvim Júnior |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3102308378811852 |
dc.contributor.author.fl_str_mv |
João Batista Borges Neto |
contributor_str_mv |
Antonio Alfredo Ferreira Loureiro Heitor Soares Ramos Filho Alejandro César Frery Orgambide Osvaldo Anibal Rosso Thaís Vasconcelos Batista Pedro Olmo Stancioli Vaz de Melo Mario Sérgio Ferreira Alvim Júnior |
dc.subject.por.fl_str_mv |
Internet of Things Collaborative sensing Time series dynamics Ordinal patterns transformations |
topic |
Internet of Things Collaborative sensing Time series dynamics Ordinal patterns transformations Computação - Teses Internet das coisas - Teses Sistemas colaborativos - Teses Análise de séries temporais - Tese |
dc.subject.other.pt_BR.fl_str_mv |
Computação - Teses Internet das coisas - Teses Sistemas colaborativos - Teses Análise de séries temporais - Tese |
description |
This thesis investigates the dynamical behavior of time series data from Internet of Things (IoT) sensors. Because of the growing number of IoT initiatives, with its impressive number of devices collecting a large amount of data from real-world phenomena, there is an imminent need for solutions that are adequate to their issues. For instance, an important part of the current IoT is the Collaborative Internet of Things (CoIoT), which is mainly composed by cheap components and managed by common users, affecting the generated data. Thus, solutions for IoT must consider improving the security of those devices, as well as the quality and reliability their data, but being efficient and robust to the issues from this novel scenario. A subject that has been successfully used for a deeper comprehension of several real-world phenomena is the study of dynamics, which aims to understand systems that evolve in time. An important tool with solid results concerning the analysis of time series dynamics is the ordinal patterns transformation. However, while the dynamics has the potential to be the basis for novel representation domains to the analysis of IoT data, there are issues on their transformations that must be handled for their proper applicability. This work aims to advance the state-of-the-art in the analysis of time series dynamics, to be adequate for the IoT issues, and to propose solutions based on dynamical behavior for a more reliable use of data from IoT. In order to advance the applicability of ordinal patterns transformations for challenging scenarios, such as IoT, we propose strategies in two main directions. A first strategy is aimed to provide minimum dependency on the selection of parameters by the transformation, by considering the multiscale behavior of a novel proposed metric, the probability of self-transitions, which are shown to be useful for the distinction of time series dynamics. The second strategy consists of a class separability index, which is a valuable method to estimate the most adequate parameters for the ordinal patterns transformations, in the context of time series classification problems. With respect to the application of the analysis of time series dynamics to IoT scenarios, we first give an enlightenment on the CoIoT. We provide a better understanding on the main characteristics and properties of data that are being generated by their sensors and its inherent problems. Then, we provide strategies for the classification of physical phenomena data collected by CoIoT sensors and a method to increase the security of IoT devices against botnet attacks, both considering their dynamical behavior. The proposed strategies were compared to related work and the results show their potentials on advancing the applicability of ordinal patterns transformations for the IoT scenarios. We show that the construction of this novel representation helps in the scalability, avoiding comparisons with a large number of data, and being robust to the problems of CoIoT data. Thus, by following these approaches, it is possible to develop solutions for IoT scenarios that can benefit from the unique aspects of dynamical systems. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-11-25 |
dc.date.accessioned.fl_str_mv |
2022-05-05T20:58:59Z |
dc.date.available.fl_str_mv |
2022-05-05T20:58:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/41411 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0001-6497-1613 |
url |
http://hdl.handle.net/1843/41411 https://orcid.org/0000-0001-6497-1613 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/pt/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/pt/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Ciência da Computação |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
ICEX - INSTITUTO DE CIÊNCIAS EXATAS |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
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UFMG |
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