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Industrial Internet of Things (IIoT) Platforms and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 12624

Special Issue Editors


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Guest Editor
Department of Informatics and Telecommunications, University of Athens, Athens, Greece
Interests: mobile networks; future internet/NGI; cognitive management; autonomic communications; reconfigurable mobile systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
Interests: communications and networking; Internet of Things; pervasive and physical computing; sensor networks; industrial informatics; location and context awareness; informatics in education
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Greece
Interests: edge networking; cyber security; public safety; digital video broadcasting; edge computing; SDN; NFV; Internet of Things; network management; network virtualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the modern landscape of Industry 4.0, monolithic and vendor-specific industrial control systems (ICSs) of the past, with little or any interaction with the internet world, are pushed to create a digitally interconnected and software-defined control ecosystem. In such highly distributed and heterogeneous environments, specialized modular software allows for the centralized management and orchestration of available services and infrastructures controlling the manufacturing process. The latter provides a unified interoperable intelligent framework for the integration of operational technology (OT) with information technology (IT) that can ideally enable vendor-agnostic and policy-driven infrastructure control, as well as monitoring, decision, execution, and reporting services for large-scale workloads and product lifecycle management. The integration of OT with IT benefits industries by reducing cost and risks along with higher performance and gains in flexibility. A critical trend that boosts OT and IT convergence in the context of smart industries is the emergence of the Industrial Internet of Things (IIoT). IIoT refers to the evolution of typical ICSs, so interconnected sensors, actuators, controllers, PLCs, instruments, and other field devices are networked together with industrial applications. Internetworking technologies comprise traditional serial protocols (e.g., RS232/485) and fieldbus topologies (e.g., Modbus, Profibus, and CAN) to packet data protocols (e.g., PROFINET and Industrial Ethernet), TCP/IP integration (e.g., VLANs, VPN, remote access, and QoS), and wireless connectivity (e.g., WLAN, 802.15.4, and LPWAN). This connectivity allows for a higher degree of automation via data collection, exchange, and analysis. Furthermore, the introduction of the IIoT into industrial environments has brought the need for data processing closer to the field devices to improve response times and save bandwidth, thus, opening the path to edge/fog computing in industrial applications. However, the emergence of this evolution comes with a price: novel risks and cyber-security threads abound at the different layers of ICSs which industrial employers should become aware of.

Hence, IIoT is an umbrella term that incorporates advances from various technological fields such as wireless and computer networking, sensor networks, cyber-physical systems, cloud and edge computing, big data analytics, artificial intelligence and machine learning, and cybersecurity.

The goal of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in the Internet of Things for Industry-4.0-oriented applications. Topics of interest include, but are not limited to, the following:

  • Advances in the Internet of Things for industrial applications;
  • Sensor networking for Industry 4.0 applications;
  • Advances concerning the various smart industries (smart factories, manufacturing, healthcare, agriculture, farming, cities, grids, etc.);
  • Empirical studies from the deployment of IIoT applications in industrial environments;
  • Advanced wireless networking for industrial use;
  • Communication and networking issues for industrial environments;
  • Network management issues for Industry 4.0 environments;
  • Edge/fog/cloud computing for Industry 4.0;
  • Network function virtualization (NFV) and software-defined networking (SDN) issues for industrial use;
  • Cybersecurity issues and solutions for Industry 4.0 environments;
  • Advances concerning the convergence of OT/IT in Industry 4.0 environments;
  • Distributed ICSs for Industry 4.0;
  • Human–machine interfaces (HMI) and SCADA supervisory systems for Industry 4.0;
  • Augmented and virtual reality issues for Industry 4.0 applications;
  • Machine learning, artificial, and computational intelligence for use in Industry 4.0 applications;
  • Predictive diagnostics and maintenance tools for Industry 4.0;
  • Advanced data repository and data analytics tools for Industry 4.0 applications;
  • Supply chain management for Industry 4.0.

Dr. Nancy Alonistioti
Dr. Spyros Panagiotakis
Dr. Evangelos K. Markakis
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 submissions that pass pre-check are 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. Sensors 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 2600 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

  • industrial informatics
  • Industry 4.0
  • OT/IT convergence
  • Internet of Things
  • sensor networks
  • computer networks
  • wireless communications
  • network management
  • network function virtualization and software-defined networking
  • cybersecurity
  • predictive maintenance
  • edge/fog/cloud computing
  • smart industries (factories, manufacturing, healthcare, agriculture, farming, cities, grids, etc.)
  • machine learning, artificial and computational intelligence
  • augmented and virtual reality
  • supply chain management
  • data analytics
  • human–computer interaction

Published Papers (8 papers)

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Research

16 pages, 7131 KiB  
Article
Estimation of Tool Life in the Milling Process—Testing Regression Models
by Andrzej Paszkiewicz, Grzegorz Piecuch, Tomasz Żabiński, Marek Bolanowski, Mateusz Salach and Dariusz Rączka
Sensors 2023, 23(23), 9346; https://doi.org/10.3390/s23239346 - 23 Nov 2023
Viewed by 743
Abstract
The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and [...] Read more.
The article presents an attempt to identify an appropriate regression model for the estimation of cutting tool lifespan in the milling process based on the analysis of the R2 parameters of these models. The work is based on our own experiments and the accumulated database (which we make available for further use). The study uses a Haas VF-1 milling machine equipped with vibration sensors and based on a Beckhoff PLC data collector. As the acquired sensor data are continuous, and in order to account for dependencies between them, regression models were used. Support Vector Regression (SVR), decision trees and neural networks were tested during the work. The results obtained show that the best prediction results with the lowest error values were obtained for two-dimensional neural networks using the LBFGS solver (93.9%). Very similar results were also obtained for SVR (93.4%). The research carried out is related to the realisation of intelligent manufacturing dedicated to Industry 4.0 in the field of monitoring production processes, planning service downtime and reducing the level of losses resulting from damage to materials, semi-finished products and tools. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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18 pages, 2440 KiB  
Article
Exploring IoT Vulnerabilities in a Comprehensive Remote Cybersecurity Laboratory
by Ismael Delgado, Elio Sancristobal, Sergio Martin and Antonio Robles-Gómez
Sensors 2023, 23(22), 9279; https://doi.org/10.3390/s23229279 - 20 Nov 2023
Viewed by 908
Abstract
With the rapid proliferation of Internet of things (IoT) devices across various sectors, ensuring robust cybersecurity practices has become paramount. The complexity and diversity of IoT ecosystems pose unique security challenges that traditional educational approaches often fail to address comprehensively. Current curricula may [...] Read more.
With the rapid proliferation of Internet of things (IoT) devices across various sectors, ensuring robust cybersecurity practices has become paramount. The complexity and diversity of IoT ecosystems pose unique security challenges that traditional educational approaches often fail to address comprehensively. Current curricula may provide theoretical knowledge but typically lack the practical components necessary for students to engage with real-world cybersecurity scenarios. This gap hinders the development of proficient cybersecurity professionals capable of securing complex IoT infrastructures. To bridge this educational divide, a remote online laboratory was developed, allowing students to gain hands-on experience in identifying and mitigating cybersecurity threats in an IoT context. This virtual environment simulates real IoT ecosystems, enabling students to interact with actual devices and protocols while practicing various security techniques. The laboratory is designed to be accessible, scalable, and versatile, offering a range of modules from basic protocol analysis to advanced threat management. The implementation of this remote laboratory demonstrated significant benefits, equipping students with the necessary skills to confront and resolve IoT security issues effectively. Our results show an improvement in practical cybersecurity abilities among students, highlighting the laboratory’s efficacy in enhancing IoT security education. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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15 pages, 1277 KiB  
Article
AALLA: Attack-Aware Logical Link Assignment Cost-Minimization Model for Protecting Software-Defined Networks against DDoS Attacks
by Sameer Ali, Saw Chin Tan, Ching Kwang Lee, Zulfadzli Yusoff, Muhammad Reazul Haque, Alexios Mylonas and Nikolaos Pitropakis
Sensors 2023, 23(21), 8922; https://doi.org/10.3390/s23218922 - 02 Nov 2023
Viewed by 1326
Abstract
Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its “network brain” located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In [...] Read more.
Software-Defined Networking (SDN), which is used in Industrial Internet of Things, uses a controller as its “network brain” located at the control plane. This uniquely distinguishes it from the traditional networking paradigms because it provides a global view of the entire network. In SDN, the controller can become a single point of failure, which may cause the whole network service to be compromised. Also, data packet transmission between controllers and switches could be impaired by natural disasters, causing hardware malfunctioning or Distributed Denial of Service (DDoS) attacks. Thus, SDN controllers are vulnerable to both hardware and software failures. To overcome this single point of failure in SDN, this paper proposes an attack-aware logical link assignment (AALLA) mathematical model with the ultimate aim of restoring the SDN network by using logical link assignment from switches to the cluster (backup) controllers. We formulate the AALLA model in integer linear programming (ILP), which restores the disrupted SDN network availability by assigning the logical links to the cluster (backup) controllers. More precisely, given a set of switches that are managed by the controller(s), this model simultaneously determines the optimal cost for controllers, links, and switches. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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17 pages, 6284 KiB  
Article
A Robot-Operation-System-Based Smart Machine Box and Its Application on Predictive Maintenance
by Yeong-Hwa Chang, Yu-Hsiang Chai, Bo-Lin Li and Hung-Wei Lin
Sensors 2023, 23(20), 8480; https://doi.org/10.3390/s23208480 - 15 Oct 2023
Viewed by 1083
Abstract
Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate [...] Read more.
Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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13 pages, 2893 KiB  
Article
Fault Detection on the Edge and Adaptive Communication for State of Alert in Industrial Internet of Things
by Yuri Santo, Roger Immich, Bruno L. Dalmazo and André Riker
Sensors 2023, 23(7), 3544; https://doi.org/10.3390/s23073544 - 28 Mar 2023
Cited by 3 | Viewed by 1571
Abstract
Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides [...] Read more.
Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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24 pages, 3141 KiB  
Article
Experimental Assessment of Common Crucial Factors That Affect LoRaWAN Performance on Suburban and Rural Area Deployments
by Markos Fragkopoulos, Spyridon Panagiotakis, Michail Kostakis, Evangelos K. Markakis, Nikolaos Astyrakakis and Athanasios Malamos
Sensors 2023, 23(3), 1316; https://doi.org/10.3390/s23031316 - 24 Jan 2023
Cited by 2 | Viewed by 1624
Abstract
LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and [...] Read more.
LoRaWAN networks might be a technology that could facilitate extreme energy-efficient operation while offering great capacity for suburban and rural area deployment, but this can be a challenging task for a network administrator. Constraints that deform the trade-off triangle of coverage, scalability and energy efficiency need to be overcome. The scope of this study is to review the limitations of the LoRaWAN protocol in order to summarize and assess the crucial factors that affect communication performance, related to data rate allocation, bidirectional traffic and radio spectrum utilization. Based on the literature, these factors correspond mostly to configurable payload transmission parameters, including transmission interval, data rate allocation, requirement for acknowledgements and retransmission. In this work, with simulation experiments, we find that collision occurrences greatly affect channel occupancy. In particular, it was evaluated that collision occurrence is increasingly affected by transmission intervals, which have the most significant negative impact on packet delivery rate (PDR). We then validated that clustering of end nodes in the vicinity of a gateway, taking into account distance and transmission settings, can improve network scalability. This can assure distribution of the total transmission time to end nodes with respect to application-related QoS requirements. Following this clustering approach, we achieved a PDR greater than 0.90 in a simulation setting with 6000 end nodes in a 10 km coverage. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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27 pages, 12324 KiB  
Article
Low-Cost UWB Based Real-Time Locating System: Development, Lab Test, Industrial Implementation and Economic Assessment
by Andrea Volpi, Letizia Tebaldi, Guido Matrella, Roberto Montanari and Eleonora Bottani
Sensors 2023, 23(3), 1124; https://doi.org/10.3390/s23031124 - 18 Jan 2023
Cited by 5 | Viewed by 2097
Abstract
This paper presents the technical development and subsequent testing of a Real-Time Locating System based on Ultra-Wideband signals, with the aim to appraise its potential implementation in a real industrial case. The system relies on a commercial Radio Indoor Positioning System, called Qorvo [...] Read more.
This paper presents the technical development and subsequent testing of a Real-Time Locating System based on Ultra-Wideband signals, with the aim to appraise its potential implementation in a real industrial case. The system relies on a commercial Radio Indoor Positioning System, called Qorvo MDEK1001, which makes use of UWB RF technology to determine the position of RF-tags placed on an item of interest, which in turn is located in an area covered by specific fixed antennas (anchors). Testing sessions were carried out both in an Italian laboratory and in a real industrial environment, to determine the best configurations according to some selected performance indicators. The results support the adoption of the proposed solution in industrial environments to track assets and work in progress. Moreover, most importantly, the solution developed is cheap in nature: indeed, normally tracking solutions involve a huge investment, quite often not affordable above all by small-, medium- and micro-sized enterprises. The proposed low-cost solution instead, as demonstrated by the economic assessment completing the work, justifies the feasibility of the investment. Hence, results of this paper ultimately constitute a guidance for those practitioners who intend to adopt a similar system in their business. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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21 pages, 25560 KiB  
Article
Towards the Digital Twin (DT) of Narrow-Band Internet of Things (NBIoT) Wireless Communication in Industrial Indoor Environment
by Muhammad Dangana, Shuja Ansari, Syed Muhammad Asad, Sajjad Hussain and Muhammad Ali Imran
Sensors 2022, 22(23), 9039; https://doi.org/10.3390/s22239039 - 22 Nov 2022
Cited by 6 | Viewed by 2076
Abstract
A study of the behavior of NB-IoT wireless communication in an industrial indoor environment was conducted in this paper. With Wireless Insite software, a scenario in the industrial sector was simulated and modeled. Our research examined how this scenario or environment affected the [...] Read more.
A study of the behavior of NB-IoT wireless communication in an industrial indoor environment was conducted in this paper. With Wireless Insite software, a scenario in the industrial sector was simulated and modeled. Our research examined how this scenario or environment affected the communication parameters of NB-IoT’s physical layer. In this context, throughput levels among terminals as well as between terminals and transceiver towers, the power received at signal destination points, signal-to-noise ratios (SNRs) in the environment, and distances between terminals and transceivers are considered. These simulated results are also compared with the calculated or theoretical values of these parameters. The results show the effect of the industrial setting on wireless communication. The differences between the theoretical and simulated values are also established. Full article
(This article belongs to the Special Issue Industrial Internet of Things (IIoT) Platforms and Applications)
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