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

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 10729

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
Prof. 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. Electronics 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 2400 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 (3 papers)

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Research

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16 pages, 1082 KiB  
Article
Fog Computing Enabled Locality Based Product Demand Prediction and Decision Making Using Reinforcement Learning
by Gone Neelakantam, Djeane Debora Onthoni and Prasan Kumar Sahoo
Electronics 2021, 10(3), 227; https://doi.org/10.3390/electronics10030227 - 20 Jan 2021
Cited by 5 | Viewed by 3016
Abstract
Wastage of perishable and non-perishable products due to manual monitoring in shopping malls creates huge revenue loss in supermarket industry. Besides, internal and external factors such as calendar events and weather condition contribute to excess wastage of products in different regions of supermarket. [...] Read more.
Wastage of perishable and non-perishable products due to manual monitoring in shopping malls creates huge revenue loss in supermarket industry. Besides, internal and external factors such as calendar events and weather condition contribute to excess wastage of products in different regions of supermarket. It is a challenging job to know about the wastage of the products manually in different supermarkets region-wise. Therefore, the supermarket management needs to take appropriate decision and action to prevent the wastage of products. The fog computing data centers located in each region can collect, process and analyze data for demand prediction and decision making. In this paper, a product-demand prediction model is designed using integrated Principal Component Analysis (PCA) and K-means Unsupervised Learning (UL) algorithms and a decision making model is developed using State-Action-Reward-State-Action (SARSA) Reinforcement Learning (RL) algorithm. Our proposed method can cluster the products into low, medium, and high-demand product by learning from the designed features. Taking the derived cluster model, decision making for distributing low-demand to high-demand product can be made using SARSA. Experimental results show that our proposed method can cluster the datasets well with a Silhouette score of ≥60%. Besides, our adopted SARSA-based decision making model outperforms over Q-Learning, Monte-Carlo, Deep Q-Network (DQN), and Actor-Critic algorithms in terms of maximum cumulative reward, average cumulative reward and execution time. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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21 pages, 8294 KiB  
Article
An Active and Passive Hybrid Battery Equalization Strategy Used in Group and between Groups
by Mingyu Gao, Jifeng Qu, Hao Lan, Qixing Wu, Huipin Lin, Zhekang Dong and Weizhong Zhang
Electronics 2020, 9(10), 1744; https://doi.org/10.3390/electronics9101744 - 21 Oct 2020
Cited by 21 | Viewed by 2663
Abstract
Active battery equalization and passive battery equalization are two important methods which can solve the inconsistency of battery cells in lithium battery groups. In this paper, a new hybrid battery equalization strategy combinfigureing the active equalizing method with a passive equalizing method is [...] Read more.
Active battery equalization and passive battery equalization are two important methods which can solve the inconsistency of battery cells in lithium battery groups. In this paper, a new hybrid battery equalization strategy combinfigureing the active equalizing method with a passive equalizing method is proposed. Among them, the implementation of the active equalizing method uses the bidirectional Flyback converter and Forward converter. This hybrid equalizing strategy adopts the concept of hierarchical equilibrium: it can be divided into two layers, the top layer is the equalization between groups, and the bottom layer is the equalization of group. There are three active equilibrium strategies and one passive equilibrium strategy. For verification purposes, a series of experiments were conducted in MATLAB 2018b/Simulink platform. The simulation and experiment results show that this hybrid battery equalizing method is efficient and feasible. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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Review

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35 pages, 2942 KiB  
Review
Enabling Emergent Configurations in the Industrial Internet of Things for Oil and Gas Explorations: A Survey
by Owoicho E. Ijiga, Reza Malekian and Uche A. K. Chude-Okonkwo
Electronics 2020, 9(8), 1306; https://doi.org/10.3390/electronics9081306 - 14 Aug 2020
Cited by 8 | Viewed by 4010
Abstract
Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods [...] Read more.
Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). Emergent configuration (EC) is a technology that can be adopted to enhance the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this paper, a survey of the concept of IoT is presented in addition to a review of IIoT systems. The application of ubiquitous computing-aided software define networking (SDN)-based EC architecture is propounded for enhancing the throughput of oil and gas production in the maritime ecosystems by managing the exploration process especially in emergency situations that involve anthropogenic oil and gas spillages. Full article
(This article belongs to the Special Issue Internet of Things for Industrial Applications)
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