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Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

Authors
  • Elsisi, Mahmoud1, 2
  • Mahmoud, Karar3, 4
  • Lehtonen, Matti3
  • Darwish, Mohamed M. F.2, 3
  • 1 or
  • 2 Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt
  • 3 (M.L.)
  • 4 Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Jan 12, 2021
Volume
21
Issue
2
Identifiers
DOI: 10.3390/s21020487
PMID: 33445540
PMCID: PMC7828067
Source
PubMed Central
Keywords
License
Green

Abstract

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.

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