Cargando…

A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †

Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and...

Descripción completa

Detalles Bibliográficos
Autores principales: Hayashi, Victor Takashi, Ruggiero, Wilson Vicente, Estrella, Júlio Cezar, Filho, Artino Quintino, Pita, Matheus Ancelmo, Arakaki, Reginaldo, Ribeiro, Cairo, Trazzi, Bruno, Bulla, Romeo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739385/
https://www.ncbi.nlm.nih.gov/pubmed/36502200
http://dx.doi.org/10.3390/s22239498
_version_ 1784847792193142784
author Hayashi, Victor Takashi
Ruggiero, Wilson Vicente
Estrella, Júlio Cezar
Filho, Artino Quintino
Pita, Matheus Ancelmo
Arakaki, Reginaldo
Ribeiro, Cairo
Trazzi, Bruno
Bulla, Romeo
author_facet Hayashi, Victor Takashi
Ruggiero, Wilson Vicente
Estrella, Júlio Cezar
Filho, Artino Quintino
Pita, Matheus Ancelmo
Arakaki, Reginaldo
Ribeiro, Cairo
Trazzi, Bruno
Bulla, Romeo
author_sort Hayashi, Victor Takashi
collection PubMed
description Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors’ knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license.
format Online
Article
Text
id pubmed-9739385
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97393852022-12-11 A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning † Hayashi, Victor Takashi Ruggiero, Wilson Vicente Estrella, Júlio Cezar Filho, Artino Quintino Pita, Matheus Ancelmo Arakaki, Reginaldo Ribeiro, Cairo Trazzi, Bruno Bulla, Romeo Sensors (Basel) Article Robust, fault tolerant, and available systems are fundamental for the adoption of Internet of Things (IoT) in critical domains, such as finance, health, and safety. The IoT infrastructure is often used to collect a large amount of data to meet the business demands of Smart Cities, Industry 4.0, and Smart Home, but there is a opportunity to use these data to intrinsically monitor an IoT system in an autonomous way. A Test Driven Development (TDD) approach for automatic module assessment for ESP32 and ESP8266 IoT development devices based on unsupervised Machine Learning (ML) is proposed to monitor IoT device status. A framework consisting of business drivers, non-functional requirements, engineering view, dynamic system evaluation, and recommendations phases is proposed to be used with the TDD development tool. The proposal is evaluated in academic and smart home study cases with 25 devices, consisting of 15 different firmware versions collected in one week, with a total of over 550,000 IoT status readings. The K-Means algorithm was applied to free memory available, internal temperature, and Wi-Fi level metrics to automatically monitor the IoT devices under development to identify device constraints violation and provide insights for monitoring frequency configuration of different firmware versions. To the best of the authors’ knowledge, it is the first TDD approach for IoT module automatic assessment which uses machine learning based on the real testbed data. The IoT status monitoring and the Python scripts for model training and inference with K-Means algorithm are available under a Creative Commons license. MDPI 2022-12-05 /pmc/articles/PMC9739385/ /pubmed/36502200 http://dx.doi.org/10.3390/s22239498 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hayashi, Victor Takashi
Ruggiero, Wilson Vicente
Estrella, Júlio Cezar
Filho, Artino Quintino
Pita, Matheus Ancelmo
Arakaki, Reginaldo
Ribeiro, Cairo
Trazzi, Bruno
Bulla, Romeo
A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title_full A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title_fullStr A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title_full_unstemmed A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title_short A TDD Framework for Automated Monitoring in Internet of Things with Machine Learning †
title_sort tdd framework for automated monitoring in internet of things with machine learning †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739385/
https://www.ncbi.nlm.nih.gov/pubmed/36502200
http://dx.doi.org/10.3390/s22239498
work_keys_str_mv AT hayashivictortakashi atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT ruggierowilsonvicente atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT estrellajuliocezar atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT filhoartinoquintino atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT pitamatheusancelmo atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT arakakireginaldo atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT ribeirocairo atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT trazzibruno atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT bullaromeo atddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT hayashivictortakashi tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT ruggierowilsonvicente tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT estrellajuliocezar tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT filhoartinoquintino tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT pitamatheusancelmo tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT arakakireginaldo tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT ribeirocairo tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT trazzibruno tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning
AT bullaromeo tddframeworkforautomatedmonitoringininternetofthingswithmachinelearning