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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/2/487 |
| _version_ | 1797412712890761216 |
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| author | Mahmoud Elsisi Karar Mahmoud Matti Lehtonen Mohamed M. F. Darwish |
| author_facet | Mahmoud Elsisi Karar Mahmoud Matti Lehtonen Mohamed M. F. Darwish |
| author_sort | Mahmoud Elsisi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T05:07:09Z |
| format | Article |
| id | doaj.art-903bf886941845cc9812ce4dfe0610a2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:07:09Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-903bf886941845cc9812ce4dfe0610a22023-12-03T12:53:37ZengMDPI AGSensors1424-82202021-01-0121248710.3390/s21020487Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart MetersMahmoud Elsisi0Karar Mahmoud1Matti Lehtonen2Mohamed M. F. Darwish3Industry 4.0 Implementation Center, Center for Cyber–Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, FinlandDepartment of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, FinlandDepartment of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, EgyptThe 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.https://www.mdpi.com/1424-8220/21/2/487smart systemsindustry 4.0internet of thingsmachine learning |
| spellingShingle | Mahmoud Elsisi Karar Mahmoud Matti Lehtonen Mohamed M. F. Darwish Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters Sensors smart systems industry 4.0 internet of things machine learning |
| 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 | smart systems industry 4.0 internet of things machine learning |
| url | https://www.mdpi.com/1424-8220/21/2/487 |
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