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.
Highlights
In recent years, updating market necessities and developing autonomous technologies, e.g., the internet of things (IoT), is shifting traditional power systems towards promising smart grids [1,2,3]
We focus on the security part and data loss in the infrastructure of industry 4.0 and apply this topology in the smart meters, which can be applied in other smart sensors in the future, not the total industry 4.0 system for managing and securing smart meters
This paper introduces an efficient infrastructure based on machine learning to analyze and monitor the data of smart meters
Summary
In recent years, updating market necessities and developing autonomous technologies, e.g., the internet of things (IoT), is shifting traditional power systems towards promising smart grids [1,2,3]. To avoid human involvement in the process of billing, an automatic smart system for reading and transfer data of meters should be applied [15,16,17] This technique is employed in the countries that can only monitor and measure the energy consumption of electricity, but do not permit remote access. The merit of the proposed infrastructure is the consideration of the cyber-physical systems that are the basis of the fourth industrial revolution (labeled industry 4.0) Such adopted industry 4.0 involves the IoT and smart sensors (smart energy meters). The advantage of utilizing the IoT framework under industry 4.0 for smart meters is that it enables an enterprise resource planning system, which can promote the manufactured electric industry in several features, including forecasting, real-time visibility, remote monitoring, cyber-physical security, and alerts and notifications
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