Cargando…
Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters
The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect a...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828067/ https://www.ncbi.nlm.nih.gov/pubmed/33445540 http://dx.doi.org/10.3390/s21020487 |
_version_ | 1783640919007821824 |
---|---|
author | Elsisi, Mahmoud Mahmoud, Karar Lehtonen, Matti Darwish, Mohamed M. F. |
author_facet | Elsisi, Mahmoud Mahmoud, Karar Lehtonen, Matti Darwish, Mohamed M. F. |
author_sort | Elsisi, Mahmoud |
collection | PubMed |
description | The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0. |
format | Online Article Text |
id | pubmed-7828067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78280672021-01-25 Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters Elsisi, Mahmoud Mahmoud, Karar Lehtonen, Matti Darwish, Mohamed M. F. Sensors (Basel) Article The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0. MDPI 2021-01-12 /pmc/articles/PMC7828067/ /pubmed/33445540 http://dx.doi.org/10.3390/s21020487 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Elsisi, Mahmoud Mahmoud, Karar Lehtonen, Matti Darwish, Mohamed M. F. Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title | Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title_full | Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title_fullStr | Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title_full_unstemmed | Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title_short | Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters |
title_sort | reliable industry 4.0 based on machine learning and iot for analyzing, monitoring, and securing smart meters |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7828067/ https://www.ncbi.nlm.nih.gov/pubmed/33445540 http://dx.doi.org/10.3390/s21020487 |
work_keys_str_mv | AT elsisimahmoud reliableindustry40basedonmachinelearningandiotforanalyzingmonitoringandsecuringsmartmeters AT mahmoudkarar reliableindustry40basedonmachinelearningandiotforanalyzingmonitoringandsecuringsmartmeters AT lehtonenmatti reliableindustry40basedonmachinelearningandiotforanalyzingmonitoringandsecuringsmartmeters AT darwishmohamedmf reliableindustry40basedonmachinelearningandiotforanalyzingmonitoringandsecuringsmartmeters |