Special Issue "Emerging Paradigms and Architectures for Industry 4.0 Applications"

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

Deadline for manuscript submissions: 15 March 2021.

Special Issue Editors

Dr. Paula Fraga-Lamas
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Guest Editor
Department of Computer Engineering, Universidade da Coruña, 15071 A Coruña, Spain
Interests: blockchain; Distributed Ledger Technology (DLT); cybersecurity; fog computing; Internet of Things; Cyber-Physical Systems; Industry 4.0; wearables; Augmented Reality; Traceability
Special Issues and Collections in MDPI journals
Dr. Tiago M. Fernández-Caramés
Website
Guest Editor
Department of 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 and Collections in MDPI journals
Prof. Dr. Sérgio I. Lopes
Website
Guest Editor
School of Technology and Management, Polytechnic Institute of Viana do Castelo, 900-347 Viana do Castelo, Portugal
Interests: networked embedded systems; cyber-physical systems; systems of systems; wireless sensor networks; edge computing; fog computing; green computing; green communications; IoT; IIoT; industry 4.0; indoor positioning; smart sensors; signal processing; data analytics; visual analytics

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (9 papers)

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Research

Open AccessArticle
TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
Appl. Sci. 2020, 10(22), 8252; https://doi.org/10.3390/app10228252 - 20 Nov 2020
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 [...] Read more.
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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Open AccessArticle
Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
Appl. Sci. 2020, 10(22), 7964; https://doi.org/10.3390/app10227964 - 10 Nov 2020
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 [...] Read more.
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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Open AccessArticle
A New Architectural Approach to Monitoring and Controlling AM Processes
Appl. Sci. 2020, 10(18), 6616; https://doi.org/10.3390/app10186616 - 22 Sep 2020
Cited by 1
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 [...] Read more.
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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Open AccessArticle
Mini Wind Harvester and a Low Power Three-Phase AC/DC Converter to Power IoT Devices: Analysis, Simulation, Test and Design
Appl. Sci. 2020, 10(18), 6347; https://doi.org/10.3390/app10186347 - 11 Sep 2020
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 [...] Read more.
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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Open AccessArticle
Use Case Based Blended Teaching of IIoT Cybersecurity in the Industry 4.0 Era
Appl. Sci. 2020, 10(16), 5607; https://doi.org/10.3390/app10165607 - 13 Aug 2020
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 [...] Read more.
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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Open AccessArticle
Timing Predictability and Security in Safety-Critical Industrial Cyber-Physical Systems: A Position Paper
Appl. Sci. 2020, 10(9), 3125; https://doi.org/10.3390/app10093125 - 30 Apr 2020
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. [...] Read more.
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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Open AccessArticle
Towards Secure and Usable Certificate-Based Authentication System Using a Secondary Device for an Industrial Internet of Things
Appl. Sci. 2020, 10(6), 1962; https://doi.org/10.3390/app10061962 - 13 Mar 2020
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 [...] Read more.
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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Open AccessArticle
Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network
Appl. Sci. 2020, 10(1), 139; https://doi.org/10.3390/app10010139 - 23 Dec 2019
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 [...] Read more.
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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Open AccessArticle
Reference Models for Digital Manufacturing Platforms
Appl. Sci. 2019, 9(20), 4433; https://doi.org/10.3390/app9204433 - 18 Oct 2019
Cited by 2
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 [...] Read more.
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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