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Internet of Things for Industrial Applications
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Internet of Things for Industrial Applications

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 24355

Special Issue Editors

Dr. Nancy Alonistioti

E-Mail Website
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
Dr. Spyros Panagiotakis

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
Dr. Evangelos K. Markakis

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, the monolithic and vendor-specific industrial control systems (ICS) 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 enables 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 the operational technology (OT) with the 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 has boosted 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 ICS, so interconnected sensors, actuators, controllers, PLCs, instruments, and other field devices are networked together with industrial applications. The internetworking technologies comprise from traditional serial protocols (e.g., RS232/485) and fieldbus topologies (e.g., Modbus, Profibus, CAN) to packet data protocols (e.g., Profinet, Industrial Ethernet), TCP/IP integration (e.g., VLANs, VPN, remote access, QoS) and wireless connectivity (e.g., WLAN, 802.15.4, LPWAN). This connectivity allows for a higher degree of automation via data collection, exchange, and analysis. Furthermore, the introduction of IoT 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: New risks and cyber-security threads abound at the different layers of ICS 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 Industrial 4.0-oriented Applications. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include but are not limited to the following:

  • Advances in Internet of Things for industrial applications;
  • Sensor networking for industrial 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 Industrial 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 industrial 4.0 environments;
  • Advances concerning the convergence of OT/IT in industrial 4.0 environments;
  • Distributed ICS for Industry 4.0;
  • Human–machine interfaces (HMI) and SCADA supervisory systems for Industry 4.0;
  • Augmented and virtual reality issues for industrial 4.0 applications;
  • Machine learning, artificial and computational intelligence for use in industrial 4.0 applications;
  • Predictive diagnostics and maintenance tools for Industry 4.0;
  • Advanced data repository and data analytics tools for industrial 4.0 applications;
  • Supply chain management for Industry 4.0.

Prof. 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 (6 papers)

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Research

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23 pages, 7930 KiB  
Article
Active 2D-DNA Fingerprinting of WirelessHART Adapters to Ensure Operational Integrity in Industrial Systems
by Willie H. Mims, Michael A. Temple and Robert F. Mills
Sensors 2022, 22(13), 4906; https://doi.org/10.3390/s22134906 - 29 Jun 2022
Cited by 1 | Viewed by 1250
Abstract
The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses [...] Read more.
The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of 840 individual authentic (modeled) versus counterfeit (non-modeled) ID verification assessments performed. Counterfeit detection is performed on a fingerprint-by-fingerprint basis with best case per-device Counterfeit Detection Rate (%CDR) estimates including 87.6% < %CDR < 99.9% and yielding an average cross-device %CDR ≈ 92.5%. This full-dimensional feature set performance was echoed by dimensionally reduced feature set performance that included per-device 87.0% < %CDR < 99.7% and average cross-device %CDR ≈ 91.4% using only 18-of-291 features—the demonstrated %CDR > 90% with an approximate 92% reduction in the number of fingerprint features is sufficiently promising for small-scale network applications and warrants further consideration. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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14 pages, 909 KiB  
Article
Melding Fog Computing and IoT for Deploying Secure, Response-Capable Healthcare Services in 5G and Beyond
by Christos Tselios, Ilias Politis, Dimitrios Amaxilatis, Orestis Akrivopoulos, Ioannis Chatzigiannakis, Spyros Panagiotakis and Evangelos K. Markakis
Sensors 2022, 22(9), 3375; https://doi.org/10.3390/s22093375 - 28 Apr 2022
Cited by 9 | Viewed by 2457
Abstract
The fifth generation (5G) of mobile networks is designed to mark the beginning of the hyper-connected society through a broad set of novel features and disruptive characteristics, delivering massive connectivity, coverage and availability paired with unprecedented speed, throughput and capacity. Such a highly [...] Read more.
The fifth generation (5G) of mobile networks is designed to mark the beginning of the hyper-connected society through a broad set of novel features and disruptive characteristics, delivering massive connectivity, coverage and availability paired with unprecedented speed, throughput and capacity. Such a highly capable networking paradigm, facilitated by its integrated segments and available subsystems, will propel numerous cutting-edge, innovative and versatile services, spanning every possible business vertical. Augmented, response-capable healthcare services have already been identified as one of the prime objectives of both vendors and customers; therefore, addressing controversies and shortcomings related to the specific field is considered a priority for all stakeholders. The scope of this paper is to present the architectural elements of 5G which enable efficient, remote healthcare services along with emergency health monitoring and response capability. In addition, we propose a holistic scheme based on technical enablers such as Internet-of-Things (IoT) and Fog Computing, for mitigating common issues and current limitations which may compromise the proclaimed service delivery. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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23 pages, 3171 KiB  
Article
A One-Stage Ensemble Framework Based on Convolutional Autoencoder for Remaining Useful Life Estimation
by Yong-Keun Park, Min-Kyung Kim and Jumyung Um
Sensors 2022, 22(7), 2817; https://doi.org/10.3390/s22072817 - 06 Apr 2022
Cited by 2 | Viewed by 1945
Abstract
As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the [...] Read more.
As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of this kind of production line, it is important to interpret mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline—from data collection (from different sources) to preprocessing, data conversion, synchronization, and deep learning classification—to estimate the total power use of the future process plan, is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building an power estimation model without manual data preprocessing. The proposed system is applied to a modular factory, connected with machine controllers, using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline, with the result of the power profile being synchronized with the robot program. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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14 pages, 549 KiB  
Article
Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals
by Yunlei Zhang, Xiaolin Gong, Kaihua Liu and Shuai Zhang
Sensors 2021, 21(9), 3286; https://doi.org/10.3390/s21093286 - 10 May 2021
Cited by 12 | Viewed by 3615
Abstract
State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of [...] Read more.
State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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Review

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29 pages, 2003 KiB  
Review
Ionizing Radiation Monitoring Technology at the Verge of Internet of Things
by Muhammad Ikmal Ahmad, Mohd Hafizi Ab. Rahim, Rosdiadee Nordin, Faizal Mohamed, Asma’ Abu-Samah and Nor Fadzilah Abdullah
Sensors 2021, 21(22), 7629; https://doi.org/10.3390/s21227629 - 17 Nov 2021
Cited by 22 | Viewed by 7075
Abstract
As nuclear technology evolves, and continues to be used in various fields since its discovery less than a century ago, radiation safety has become a major concern to humans and the environment. Radiation monitoring plays a significant role in preventive radiological nuclear detection [...] Read more.
As nuclear technology evolves, and continues to be used in various fields since its discovery less than a century ago, radiation safety has become a major concern to humans and the environment. Radiation monitoring plays a significant role in preventive radiological nuclear detection in nuclear facilities, hospitals, or in any activities associated with radioactive materials by acting as a tool to measure the risk of being exposed to radiation while reaping its benefit. Apart from in occupational settings, radiation monitoring is required in emergency responses to radiation incidents as well as outdoor radiation zones. Several radiation sensors have been developed, ranging from as simple as a Geiger-Muller counter to bulkier radiation systems such as the High Purity Germanium detector, with different functionality for use in different settings, but the inability to provide real-time data makes radiation monitoring activities less effective. The deployment of manned vehicles equipped with these radiation sensors reduces the scope of radiation monitoring operations significantly, but the safety of radiation monitoring operators is still compromised. Recently, the Internet of Things (IoT) technology has been introduced to the world and offered solutions to these limitations. This review elucidates a systematic understanding of the fundamental usage of the Internet of Drones for radiation monitoring purposes. The extension of essential functional blocks in IoT can be expanded across radiation monitoring industries, presenting several emerging research opportunities and challenges. This article offers a comprehensive review of the evolutionary application of IoT technology in nuclear and radiation monitoring. Finally, the security of the nuclear industry is discussed. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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25 pages, 1696 KiB  
Review
A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT
by Muhammad Almas Khan, Muazzam A. Khan, Sana Ullah Jan, Jawad Ahmad, Sajjad Shaukat Jamal, Awais Aziz Shah, Nikolaos Pitropakis and William J. Buchanan
Sensors 2021, 21(21), 7016; https://doi.org/10.3390/s21217016 - 22 Oct 2021
Cited by 60 | Viewed by 6704
Abstract
A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive [...] Read more.
A large number of smart devices in Internet of Things (IoT) environments communicate via different messaging protocols. Message Queuing Telemetry Transport (MQTT) is a widely used publish–subscribe-based protocol for the communication of sensor or event data. The publish–subscribe strategy makes it more attractive for intruders and thus increases the number of possible attacks over MQTT. In this paper, we proposed a Deep Neural Network (DNN) for intrusion detection in the MQTT-based protocol and also compared its performance with other traditional machine learning (ML) algorithms, such as a Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbour (kNN), Decision Tree (DT), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs). The performance is proved using two different publicly available datasets, including (1) MQTT-IoT-IDS2020 and (2) a dataset with three different types of attacks, such as Man in the Middle (MitM), Intrusion in the network, and Denial of Services (DoS). The MQTT-IoT-IDS2020 contains three abstract-level features, including Uni-Flow, Bi-Flow, and Packet-Flow. The results for the first dataset and binary classification show that the DNN-based model achieved 99.92%, 99.75%, and 94.94% accuracies for Uni-flow, Bi-flow, and Packet-flow, respectively. However, in the case of multi-label classification, these accuracies reduced to 97.08%, 98.12%, and 90.79%, respectively. On the other hand, the proposed DNN model attains the highest accuracy of 97.13% against LSTM and GRUs for the second dataset. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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