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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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