{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:22:29Z","timestamp":1785410549355,"version":"3.56.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T00:00:00Z","timestamp":1610409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 data. The data monitoring is carried based on the industrial digital twins\u2019 platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.<\/jats:p>","DOI":"10.3390\/s21020487","type":"journal-article","created":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T20:11:31Z","timestamp":1610482291000},"page":"487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":117,"title":["Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0411-9637","authenticated-orcid":false,"given":"Mahmoud","family":"Elsisi","sequence":"first","affiliation":[{"name":"Industry 4.0 Implementation Center, Center for Cyber\u2013Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan"},{"name":"Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6729-6809","authenticated-orcid":false,"given":"Karar","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, Finland"},{"name":"Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9979-7333","authenticated-orcid":false,"given":"Matti","family":"Lehtonen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9782-8813","authenticated-orcid":false,"given":"Mohamed M. F.","family":"Darwish","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"},{"name":"Department of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, J.C.-W., and Yeh, K.-H. (2020). Security and Privacy Techniques in IoT Environment. Sensors, 21.","DOI":"10.3390\/s21010001"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MCOM.2016.1600647CM","article-title":"An Integrated IoT Architecture for Smart Metering","volume":"54","author":"Lloret","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alablani, I., and Alenazi, M. (2020). 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