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Emerging Paradigms and Architectures for Industry 4.0 Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (15 March 2021) | Viewed by 41655

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Special Issue Editors

Prof. Dr. Paula Fraga-Lamas

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Department of Computer Engineering, University of A Coruña (UDC), 15071 A Coruña, Spain
Interests: blockchain; distributed ledger technology (DLT); fog computing; internet of things; IIoT; cyber-physical systems; industry 4.0; defense and public safety; cybersecurity; wearables; industrial augmented reality; traceability
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Dr. Tiago M. Fernández-Caramés

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Department Computer Engineering, Faculty of Computer Science, Universidade da Coruña, 15071 A Coruña, Spain
Interests: blockchain; intelligent transportation systems; wireless sensor networks; fog computing; edge computing; industrial internet of things (IIoT); RFID; wireless communications; cybersecurity; augmented reality; industry 4.0; traceability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Sérgio Ivan Lopes

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ADiT-LAB, Applied Digital Transformation Laboratory, Polytechnic Institute of Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, n.º 34, 4900-347 Viana do Castelo, Portugal
Interests: cyber-physical systems; IoT; wireless sensor networks; signal processing; smart sensing; data analytics; visual analytics; IAQ monitoring; radon risk management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution (4IR), called “Industry 4.0” in Europe, “Industrial Internet” in the U.S. or “Made in China 2025” in China, blurs the boundaries between the physical, digital and biological worlds in order to improve manufacturing processes.

4IR-enabling technologies such as the Industrial Internet of Things (IIoT), Industrial Cyber–Physical Systems (ICPS), novel computing paradigms (fog, mist and edge computing), digital twin, augmented/mixed reality, and Distributed Ledger Technologies (DLT) like blockchain or advanced wireless sensor networks, can enable novel cyber-secure, resilient, collaborative and human-centric computing and communications architectures to improve manufacturing processes in diverse aspects related to data collection, data communications, storage, authentication, reliability, scalability, communications latency, energy efficiency, standardization, interoperability, mobility or security.

This Special Issue aims to report the latest advances in architectures, paradigms and applications in the ever-increasing complex ecosystem of smart manufacturing. Potential topics include but are not limited to the following:

Challenges, visions and concepts for Industry 4.0;
Novel architectures for the Industrial Internet of Things (IIoT);
Industrial applications of the Internet of Things (IoT) such as green manufacturing, agile manufacturing, predictive maintenance and zero-defect production;
Novel network infrastructures for IIoT;
IIoT data analytics, data aggregation, data abstraction and event detection;
Cybersecurity in IIoT environments;
Cognitive IIoT;
Cloud, fog, mist, edge and mobile edge computing architectures for industrial scenarios;
Industrial Cyber–Physical Systems (ICPS);
Advances in the application of Distributed Ledger Technologies (DLT) (e.g., Blockchain, IOTA) to industrial scenarios;
Industrial Wireless Sensor Networks;
Low-Power Wide-Area Network (LPWAN) technologies (e.g., LoRa, SigFox, NB-IoT) for Industry 4.0 applications;
Localization and tracking technologies for Industry 4.0;
Energy harvesting techniques for industrial scenarios;
Advanced sensors for Industry 4.0 applications;
Novel sensing strategies for process monitoring and product traceability;
Human–machine interfaces and interactions in human-centric smart industrial systems;
Advanced machine learning and artificial intelligence for Industry 4.0;
Digital twins.

Dr. Paula Fraga-Lamas
Prof. Dr. Tiago M. Fernández-Caramés
Prof. Dr. Sérgio I. Lopes
Guest Editors

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Keywords

Industry 4.0
Industrial Internet of Things (IIoT)
Cloud, fog, mist, edge and mobile edge computing
Industrial Cyber–Physical Systems (ICPS)
Human-Machine Interfaces
Industrial Wireless Sensor Networks
Distributed Ledger Technologies (DLT)
Low-Power Wide-Area Network (LPWAN)
Advanced sensors
Cybersecurity in IIoT
Cognitive IIoT

Published Papers (11 papers)

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Research

15 pages, 2037 KiB

Open AccessArticle

Future Industrial Networks in Process Automation: Goals, Challenges, and Future Directions

by Johan Åkerberg, Johan Furunäs Åkesson, Jorgen Gade, Maryam Vahabi, Mats Björkman, Mehrzad Lavassani, Rahul Nandkumar Gore, Thomas Lindh and Xiaolin Jiang

Appl. Sci. 2021, 11(8), 3345; https://doi.org/10.3390/app11083345 - 08 Apr 2021

Cited by 11 | Viewed by 2731

Abstract

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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28 pages, 1313 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Security Vulnerabilities in LPWANs—An Attack Vector Analysis for the IoT Ecosystem

by Nuno Torres, Pedro Pinto and Sérgio Ivan Lopes

Appl. Sci. 2021, 11(7), 3176; https://doi.org/10.3390/app11073176 - 02 Apr 2021

Cited by 32 | Viewed by 5000

Abstract

Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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17 pages, 678 KiB

Open AccessArticle

TFBN: A Cost Effective High Performance Hierarchical Interconnection Network

by M. M. Hafizur Rahman, Mohammed Al-Naeem, Mohammed N. M. Ali and Abu Sufian

Appl. Sci. 2020, 10(22), 8252; https://doi.org/10.3390/app10228252 - 20 Nov 2020

Cited by 8 | Viewed by 2637

Abstract

In order to fulfill the increasing demand for computation power to process a boundless data concurrently within a very short time or real-time in many areas such as IoT, AI, machine learning, smart grid, and big data analytics, we need exa-scale or zetta-scale computation in the near future. Thus, to have this level of computation, we need a massively parallel computer (MPC) system that shall consist of millions of nodes; and, for the interconnection of these massive numbers of nodes, conventional topologies are infeasible. Thus, a hierarchical interconnection network (HIN) is a rational way to connect huge nodes. Through this article, we are proposing a new HIN, which is a tori-connected flattened butterfly network (TFBN) for the next generation MPC system. Numerous basic modules are hierarchically interconnected as a toroidal connection, whereby the basic modules are flattened butterfly networks. We have studied the network architecture, static network performance, and static cost-effectiveness of the proposed TFBN in detail; and compared static network and cost-effectiveness performance of the TFBN to those of TTN, torus, TESH, and mesh networks. It is depicted that TFBN possesses low diameter and average distance, high arc connectivity, and temperate bisection width. It also has better cost-effectiveness and cost-performance trade-off factor compared to those of TTN, torus, TESH, and mesh networks. The only shortcoming is that the complexity of wiring of the TFBN is higher than that of those networks; this is because the basic module necessitates some extra short length link to form the flattened butterfly network. Therefore, TFBN is a high performance and cost-effective HIN, and it will be a good option for the next generation MPC system. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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17 pages, 4251 KiB

Open AccessArticle

Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios

by David Todoli-Ferrandis, Javier Silvestre-Blanes, Víctor Sempere-Payá and Ana Planes-Martínez

Appl. Sci. 2020, 10(22), 7964; https://doi.org/10.3390/app10227964 - 10 Nov 2020

Cited by 3 | Viewed by 1752

Abstract

Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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17 pages, 2683 KiB

Open AccessArticle

A New Architectural Approach to Monitoring and Controlling AM Processes

by Muhammad Adnan, Yan Lu, Albert Jones, Fan-Tien Cheng and Ho Yeung

Appl. Sci. 2020, 10(18), 6616; https://doi.org/10.3390/app10186616 - 22 Sep 2020

Cited by 10 | Viewed by 3010

Abstract

The abilities to both monitor and control additive manufacturing (AM) processes in real-time are necessary before the routine production of quality AM parts will be possible. Currently, neither ability exist! The major reason is that AM processes are different from traditional manufacturing processes in many ways and so are the sensors and the monitoring data collected from them. In traditional manufacturing, that data is mostly numeric in nature. To that numeric data, AM monitoring data add large volumes of a variety of in situ, high-speed, image data. Collecting, fusing, and analyzing all that AM data and making the necessary control decisions is not possible using traditional, rigid, hierarchical-control architectures. Therefore, researchers are proposing to use real-time, machine-learning algorithms to analyze the data and to execute the other control functions. This paper identifies those control functions and proposes a new architecture to integrate them. This paper also shows an example of using that architecture to analyze the melt-pool, shape analysis using a clustering method. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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46 pages, 10638 KiB

Open AccessArticle

Mini Wind Harvester and a Low Power Three-Phase AC/DC Converter to Power IoT Devices: Analysis, Simulation, Test and Design

by Borja Pozo, José Ángel Araujo, Henrik Zessin, Loreto Mateu, José Ignacio Garate and Peter Spies

Appl. Sci. 2020, 10(18), 6347; https://doi.org/10.3390/app10186347 - 11 Sep 2020

Cited by 7 | Viewed by 2761

Abstract

Wind energy harvesting is a widespread mature technology employed to collect energy, but it is also suitable, and not yet fully exploited at small scale, for powering low power electronic systems such as Internet of Things (IoT) systems like structural health monitoring, on-line sensors, predictive maintenance, manufacturing processes and surveillance. The present work introduces a three-phase mini wind energy harvester and an Alternate Current/Direct Current (AC/DC) converter. The research analyzes in depth a wind harvester’s operation principles in order to extract its characteristic parameters. It also proposes an equivalent electromechanical model of the harvester, and its accuracy has been verified with prototype performance results. Moreover, unlike most of the converters which use two steps for AC/DC signal conditioning—a rectifier stage and a DC/DC regulator—this work proposes a single stage converter to increase the system efficiency and, consequently, improve the energy transfer. Moreover, the most suitable AC/DC converter architecture was chosen and optimized for the best performance taking into account: the target power, efficiency, voltage levels, operation frequency, duty cycle and load required to implement the aforementioned converter. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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29 pages, 15201 KiB

Open AccessArticle

Use Case Based Blended Teaching of IIoT Cybersecurity in the Industry 4.0 Era

by Tiago M. Fernández-Caramés and Paula Fraga-Lamas

Appl. Sci. 2020, 10(16), 5607; https://doi.org/10.3390/app10165607 - 13 Aug 2020

Cited by 16 | Viewed by 4523

Abstract

Industry 4.0 and Industrial Internet of Things (IIoT) are paradigms that are driving current industrial revolution by connecting to the Internet industrial machinery, management tools or products so as to control and gather data about them. The problem is that many IIoT/Industry 4.0 devices have been connected to the Internet without considering the implementation of proper security measures, thus existing many examples of misconfigured or weakly protected devices. Securing such systems requires very specific skills, which, unfortunately, are not taught extensively in engineering schools. This article details how Industry 4.0 and IIoT cybersecurity can be learned through practical use cases, making use of a methodology that allows for carrying out audits to students that have no previous experience in IIoT or industrial cybersecurity. The described teaching approach is blended and has been imparted at the University of A Coruña (Spain) during the last years, even during the first semester of 2020, when the university was closed due to the COVID-19 pandemic lockdown. Such an approach is supported by online tools like Shodan, which ease the detection of vulnerable IIoT devices. The feedback results provided by the students show that they consider useful the proposed methodology, which allowed them to find that 13% of the IIoT/Industry 4.0 systems they analyzed could be accessed really easily. In addition, the obtained teaching results indicate that the established course learning outcomes are accomplished. Therefore, this article provides useful guidelines for teaching industrial cybersecurity and thus train the next generation of security researchers and developers. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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17 pages, 1435 KiB

Open AccessArticle

Timing Predictability and Security in Safety-Critical Industrial Cyber-Physical Systems: A Position Paper

by Saad Mubeen, Elena Lisova and Aneta Vulgarakis Feljan

Appl. Sci. 2020, 10(9), 3125; https://doi.org/10.3390/app10093125 - 30 Apr 2020

Cited by 9 | Viewed by 2886

Abstract

Cyber Physical Systems (CPSs) are systems that are developed by seamlessly integrating computational algorithms and physical components, and they are a result of the technological advancement in the embedded systems and distributed systems domains, as well as the availability of sophisticated networking technology. Many industrial CPSs are subject to timing predictability, security and functional safety requirements, due to which the developers of these systems are required to verify these requirements during the their development. This position paper starts by exploring the state of the art with respect to developing timing predictable and secure embedded systems. Thereafter, the paper extends the discussion to time-critical and secure CPSs and highlights the key issues that are faced when verifying the timing predictability requirements during the development of these systems. In this context, the paper takes the position to advocate paramount importance of security as a prerequisite for timing predictability, as well as both security and timing predictability as prerequisites for functional safety. Moreover, the paper identifies the gaps in the existing frameworks and techniques for the development of time- and safety-critical CPSs and describes our viewpoint on ensuring timing predictability and security in these systems. Finally, the paper emphasises the opportunities that artificial intelligence can provide in the development of these systems. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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16 pages, 1017 KiB

Open AccessArticle

Towards Secure and Usable Certificate-Based Authentication System Using a Secondary Device for an Industrial Internet of Things

by Jusop Choi, Junsung Cho, Hyoungshick Kim and Sangwon Hyun

Appl. Sci. 2020, 10(6), 1962; https://doi.org/10.3390/app10061962 - 13 Mar 2020

Cited by 5 | Viewed by 2677

Abstract

As the number of controllers and devices increases in Industrial Internet of Things (IIoT) applications, it is essential to provide a secure and usable user authentication system for human operators who have to manage tens or hundreds of controllers and devices with his/her password. In this paper, we propose a formally verified certificate-based authentication system using a secondary network device for such IIoT applications. In the proposed system, a user’s sign key is encrypted with a secret key that can be computed with his/her password and a secret parameter in a secondary device to securely protect the sign key. To demonstrate the feasibility of the proposed system, we implemented a prototype with standard cryptographic algorithms (AES-256, RSA-3072, and ECDSA-256). The experiment results demonstrated that the execution time overhead of the sign key recovery process was 0.039 and 0.073 s, respectively, for RSA-3072 and ECDSA-256, which was marginal compared with the total execution time (0.383 s for RSA-3072 and 0.319 s for ECDSA-256) of the conventional system. We also verified the security of the proposed protocol using a formal verification tool called ProVerif. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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19 pages, 4404 KiB

Open AccessArticle

Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

by Xia Fang, Han Fang, Zhan Feng, Jie Wang and Libin Zhou

Appl. Sci. 2020, 10(1), 139; https://doi.org/10.3390/app10010139 - 23 Dec 2019

Cited by 1 | Viewed by 2561

Abstract

It is difficult to combine human sensory cognition with quality detection to form a pattern recognition system based on human perception. In the future, miniature stepper motor modules will be widely used in advanced intelligent equipment. However, the reducer module based on powder metallurgy parts and the stepper motor may have various defects during operation, with varying definitions of those that affect the user comfort. It is tremendously important to develop an intelligent system to effectively simulate human senses. In this work, an elaborated personification of the perceptual system is proposed to simulate the ventral and flow of the human perception system: two branch systems consisting of a spatiotemporal convolutional neural network (S-CNN) and a concatenated HoppingNet temporal convolutional neural network (T-CNN). To ensure high robustness of the system, we combined principal component analysis (PCA) with the opinions of an experienced quality control (QC) team members to screen the data, and used a bionic ear to simulate human perception characteristics. After repeated comparisons of the tester, the results show that our anthropoid pattern sensing system has high accuracy and robustness for a stepper motor module. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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25 pages, 3781 KiB

Open AccessArticle

Reference Models for Digital Manufacturing Platforms

by Francisco Fraile, Raquel Sanchis, Raul Poler and Angel Ortiz

Appl. Sci. 2019, 9(20), 4433; https://doi.org/10.3390/app9204433 - 18 Oct 2019

Cited by 56 | Viewed by 9649

Abstract

This paper presents an integrated reference model for digital manufacturing platforms, based on cutting edge reference models for the Industrial Internet of Things (IIoT) systems. Digital manufacturing platforms use IIoT systems in combination with other added-value services to support manufacturing processes at different levels (e.g., design, engineering, operations planning, and execution). Digital manufacturing platforms form complex multi-sided ecosystems, involving different stakeholders ranging from supply chain collaborators to Information Technology (IT) providers. This research analyses prominent reference models for IIoT systems to align the definitions they contain and determine to what extent they are complementary and applicable to digital manufacturing platforms. Based on this analysis, the Industrial Internet Integrated Reference Model (I3RM) for digital manufacturing platforms is presented, together with general recommendations that can be applied to the architectural definition of any digital manufacturing platform. Full article

(This article belongs to the Special Issue Emerging Paradigms and Architectures for Industry 4.0 Applications)

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