A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber–Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

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Models of decision support in socio-cyber-physical systems

Information and Control Systems ◽

10.31799/1684-8853-2019-3-55-70 ◽

2019 ◽

pp. 55-70 ◽

Cited By ~ 1

Author(s):

A. V. Smirnov ◽

T. V. Levashova

Keyword(s):

Decision Making ◽

Decision Support ◽

Situation Awareness ◽

Cyber Physical Systems ◽

Emergent Behavior ◽

Emergent Properties ◽

Decision Making Process ◽

Physical Systems ◽

Decision Support Models ◽

Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

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Cyber-Physical Systems, Internet of Things, and Big Data in Industry 4.0: Digital Manufacturing Technologies, Business Process Optimization, and Sustainable Organizational Performance

Economics Management and Financial Markets ◽

10.22381/emfm14320193 ◽

2019 ◽

Vol 14 (3) ◽

pp. 23

Keyword(s):

Big Data ◽

Internet Of Things ◽

Organizational Performance ◽

Process Optimization ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Digital Manufacturing ◽

Physical Systems ◽

Manufacturing Technologies ◽

Business Process Optimization

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The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06572-4 ◽

2021 ◽

Vol 113 (7-8) ◽

pp. 2395-2412

Author(s):

Baudouin Dafflon ◽

Nejib Moalla ◽

Yacine Ouzrout

Keyword(s):

Internet Of Things ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Review Literature ◽

Comprehensive Understanding ◽

Physical Systems ◽

Fine Grained ◽

Key Technologies ◽

Granularity Level ◽

Human Component

AbstractThis work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.

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The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽

10.3390/j4020012 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Antonio Royo ◽

Juan Carlos Sánchez

Keyword(s):

Decision Making ◽

Data Acquisition ◽

Real Time ◽

Failure Detection ◽

Cyber Physical Systems ◽

Future Research ◽

Time Data ◽

Physical Systems ◽

The Real ◽

Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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Artificial Intelligence and the Internet of Things in Industry 4.0

CCF Transactions on Pervasive Computing and Interaction ◽

10.1007/s42486-021-00057-3 ◽

2021 ◽

Author(s):

Petar Radanliev ◽

David De Roure ◽

Razvan Nicolescu ◽

Michael Huth ◽

Omar Santos

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Industry 4.0 ◽

Cyber Physical Systems ◽

Physical Systems ◽

Leading Industry ◽

Key Technologies ◽

The Internet Of Things ◽

State Of Art ◽

Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

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

Sensors ◽

10.3390/s21020487 ◽

2021 ◽

Vol 21 (2) ◽

pp. 487 ◽

Cited By ~ 2

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.

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Introducing Gamification in the AR-Enhanced Order Picking Process: A Proposed Approach

Logistics ◽

10.3390/logistics5010014 ◽

2021 ◽

Vol 5 (1) ◽

pp. 14

Author(s):

Athina G. Bright ◽

Stavros T. Ponis

Keyword(s):

Industry 4.0 ◽

Business Processes ◽

Cyber Physical Systems ◽

Order Picking ◽

Physical Systems ◽

The Core ◽

Complex Process ◽

Core Elements ◽

Game Elements ◽

Innovative Industry

In the last decade, the Industry 4.0 concept has introduced automation and cyber-physical systems as the core elements of future logistics, supported by an array of technologies, such as augmented reality (AR) providing the necessary support for the digital transformation of manufacturing and logistics and the smartification and digital refinement of traditional pre-Industry 4.0 processes. This paper studies the influence and the potential of gamification techniques in supporting innovative Industry 4.0-enhanced processes in the contemporary warehouse work ecosystem. Gamification in the workplace aims to motivate the employees and increase their involvement in an activity, while at the same time creating a sense of an everyday different experience rather than a set of repetitive and monotonous tasks. Since the design of such a system is a complex process, the most widespread design frameworks are studied, and the emphasis is on the principal game elements and their connection to mobilization mechanisms. Finally, an initial proposal of a gamification framework to support the AR-enhanced order picking process in contemporary logistics centers is provided with an emphasis on the mechanics of a fair and functional reward system. The proposed approach aims to showcase the potential alignment of business processes to human motivation, respecting the differences between tasks and the workers’ cognitive workload.

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