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

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 487 ◽  
Author(s):  
Mahmoud Elsisi ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
Mohamed M. F. Darwish
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Learning Algorithm ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Smart Meters ◽  
Industrial Environment ◽  
Physical Systems ◽  
Smart Machines

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.

Adoption of Machine Learning With Adaptive Approach for Securing CPS

2020 ◽  
pp. 388-415
Author(s):  
Rama Mercy Sam Sigamani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Smart Grids ◽  
False Negative ◽  
False Negative Rate ◽  
Cyber Physical Systems ◽  
Detection Accuracy ◽  
Physical Systems ◽  
Positive Rate ◽  
Iot Devices

The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence algorithms have been proposed to detect and mitigate the cyber-attacks in cyber physical systems, smart grids, power systems. The various machine learning approaches towards securing CPS is observed based on the performance metrics like detection accuracy, average classification rate, false negative rate, false positive rate, processing time per packet. A unique feature of CPS is considered through structural adaptation which facilitates a self-healing CPS.

Information Processing in Work Environment 4.0 and the Beneficial Impact of Intentional Forgetting on Change Management

2020 ◽  
Vol 64 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Arnulf Sebastian Schüffler ◽  
Christof Thim ◽  
Jennifer Haase ◽  
Norbert Gronau ◽  
Annette Kluge
Keyword(s):  
Work Environment ◽  
Industry 4.0 ◽  
Experimental Approach ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
Intentional Forgetting ◽  
Beneficial Impact ◽  
The Internet Of Things

Abstract. Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment ( N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.

scholarly journals Study and Analysis of the Implementation of 4.0 Technologies in the Agri-Food Supply Chain: A State of the Art

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2526
Author(s):  
Paula Morella ◽  
María Pilar Lambán ◽  
Jesús Royo ◽  
Juan Carlos Sánchez
Keyword(s):  
Supply Chain ◽  
Performance Indicators ◽  
Food Supply ◽  
Industry 4.0 ◽  
State Of The Art ◽  
Cyber Physical Systems ◽  
Food Supply Chain ◽  
Industrial Environment ◽  
Physical Systems ◽  
Lack Of Information

Industry 4.0 is changing the industrial environment. Particularly, the emerging Industry 4.0 technologies can improve the agri-food supply chain throughout all its stages. This study aims to highlight the benefits of implementing Industry 4.0 in the agri-food supply chain. First, it presents how technologies enhance the agri-food supply chain development. Then, it identifies and highlights the most common challenges that Industry 4.0 implementation faces in agri-food’s environment. After that, it proposes key performance indicators to measure the advantages of this implementation. To achieve this, a systematic literature review was conducted. It combined conceptual and bibliometric analyses of 78 papers. As a result, the most suitable technologies were identified, e.g., Internet of Things, Big Data, blockchain and cyber physical systems. The most used indicators are proposed and the challenges of implementation were detected and classified in three groups, i.e., technical, educational and governmental. This paper highlights and exemplifies the benefits of implementing Industry 4.0 facing the lack of knowledge that exists nowadays. Moreover, it fulfils the gaps in literature, i.e., the lack of information about the implementation of technologies 4.0 or the description of the most relevant indicators for Industry 4.0 implementation.

scholarly journals Modern technologies in Industry 4.0 – cyber-physical systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Anna Smyshlyaeva ◽  
Kseniya Reznikova ◽  
Denis Savchenko
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
Level Data ◽  
The Internet Of Things ◽  
Modern Technologies

With the advent of the Industry 4.0 concept, the approach to production automation has fundamentally changed. The manufacturing industry is based on such modern technologies as the Internet of Things, Big Data, cloud computing, artificial intelligence and cyber-physical systems. These technologies have proven themselves not only in industry, but also in various other branches of life. In this paper, the authors consider the concept of cyber-physical systems – systems based on the interaction of physical processes with computational ones. The article presents a conceptual model of cyber-physical systems that displays its elements and their interaction. In cyber-physical systems, it represents five levels: physical, network, data storage, processing and analytics level, application level. Cyber-physical systems carry out their work using a basic set of technologies: the Internet of things, big data and cloud computing. Additional technologies are used depending on the purpose of the system. At the physical level, data is collected from physical devices. With the help of the Internet of Things at the network level, data is transferred to a data warehouse for further processing or processed almost immediately thanks to cloud computing. The amount of data in cyber-physical systems is enormous, so it is necessary to use big data technology and effective methods for processing and analyzing this data. The main feature of this technological complex is real-time operation. Despite the improvement in the quality of production and human life, cyber-physical systems have a number of disadvantages. The authors highlight the main problems of cyber-physical systems and promising areas of research for their development. Having solved the listed problems, cyber-physical systems will reach a qualitatively new level of utility. The paper also provides examples of the implementation of concepts such as a smart city, smart grid, smart manufacturing, smart house. These concepts are based on the principle of cyber-physical systems.

scholarly journals Przemysł 4.0 i sztuczna inteligencja: szansa czy zagrożenie dla realizacji koncepcji zrównoważonego i trwałego rozwoju?

2021 ◽  
Vol 65 (1) ◽  
pp. 7-26
Author(s):  
Barbara Siuta-Tokarska ◽  
Keyword(s):  
Artificial Intelligence ◽  
Sustainable Development ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Holistic Approach ◽  
Cyber Physical Systems ◽  
The Internet ◽  
Physical Systems ◽  
The Internet Of Things ◽  
Industrial Revolutions

This paper discusses the problems connected with visible changes in industry in the context of the consequent four industrial revolutions. The last one is associated with “industry 4.0”, which in turn manifests in the presence of the following constitutive parts (systems): cyber physical systems, the Internet of Things, the Internet of Services and intelligent factories. Another important factor of the ongoing changes is the appearance of a new branch, which tries to comprise in its theoretical divagations the problems discussed in IT, mathematics, neurophysiology, electronics, psychology, anthropology and philosophy. In the experimental area this realm, in turn, is treated as a branch of IT. All these constituents can be defined as artificial intelligence. The aim of this research is an attempt to answer the question posed in the title of the article, taking into consideration the potentially most holistic approach to these problems in the context of sustainable development of the constituent capitals taking into consideration not only the increasing of opportunities but maximizing the benefits in the natural, social and economic spheres.

scholarly journals A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8171
Author(s):  
Asfandyar Khan ◽  
Arif Iqbal Umar ◽  
Arslan Munir ◽  
Syed Hamad Shirazi ◽  
Muazzam A. Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Smart Grids ◽  
Numerical Results ◽  
Hierarchical Architecture ◽  
The Internet ◽  
Smart Meters ◽  
Web Interfaces ◽  
Scheduling Scheme ◽  
First In First Out

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

scholarly journals Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Dinithi Jayaratne ◽  
Daswin De Silva ◽  
Damminda Alahakoon ◽  
Xinghuo Yu
Keyword(s):  
Machine Learning ◽  
Data Streams ◽  
Learning Algorithm ◽  
Concept Drift ◽  
Cyber Physical Systems ◽  
Industrial Applications ◽  
Physical Systems ◽  
Concept Drift Detection ◽  
Continuous Concept ◽  
Time Critical

AbstractThe embedded, computational and cloud elements of industrial cyber physical systems (CPS) generate large volumes of data at high velocity to support the operations and functions of corresponding time-critical and mission-critical physical entities. Given the non-deterministic nature of these entities, the generated data streams are susceptible to dynamic and abrupt changes. Such changes, which are formally defined as concept drifts, leads to a decline in the accuracy and robustness of predicted CPS behaviors. Most existing work in concept drift detection are classifier dependent and require labeled data. However, CPS data streams are unlabeled, unstructured and change over time. In this paper, we propose an unsupervised machine learning algorithm for continuous concept drift detection in industrial CPS. This algorithm demonstrates three types of unsupervised learning, online, incremental and decremental. Furthermore, it distinguishes between abrupt and reoccurring drifts. We conducted experiments on SEA, a widely cited synthetic dataset of concept drift detection, and two industrial applications of CPS, task tracking in factory settings and smart energy consumption. The results of these experiments successfully validate the key features of the proposed algorithm and its utility of detecting change in non-deterministic CPS environments.

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