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Special Issue Information

Dear Colleagues,

We live in an interconnected world where different smart devices and people are connected together. The advancement of high-tech services and reliability, efficiency, speed, and accuracy of services have become crucial and popular in many aspects. The key elements include the Industrial Internet of Things, big data, and supply chains.

The Industrial Internet of Things (IIoT) is a platform that allows a network of devices (sensors, smart meters, etc.) to communicate, analyze data, and process information collaboratively in the service of individuals or organizations. Big data (BD) have the core values of volume, velocity, variety, and veracity. Supply chains require modern advancement and support from the IIoT and big data to make all services efficient, fast, accurate, and reliable. These three can combine and work together to produce greater impacts and contributions, such as in the issues surrounding IIoT devices, their interconnectedness, and the services they may offer, including efficient, effective, and secure analysis of the data IIoT produces. Machine learning and other advanced techniques, models and tools, and issues of security and trust are often related to each other. IoT technologies may mature and become part of our everyday lives. Second, BD can be jointly used with machine learning, AI, and statistical and other advanced techniques, models, and methods, which can create value for the people and organizations adopting it. Forecasting, deep analysis, and analytics can help to identify weaknesses and make improvements based on different analyses. Third, suppliers can receive real-time updates on their stocks and demands, manufacturers and transport companies on the workloads, destination, and resource distributions, and customers on the delivery of their goods. Investors can make better decisions regarding their goods, resources, sales, and management, and supply chains can achieve a greater sustainable ecosystem with the help of IIoT and Big Data.

This Special Issue brings experts, practitioners, scientists, and decision makers from academia and industry together. Our aim is to foster a strong, lively, and well-connected international research community. We welcome innovative ideas, concepts, services, techniques, research outputs, and industrial applications. We will select the top authors from IIoTBDSC 2022 http://iiotbdsc.com/2022/, but we also welcome any authors with regular paper submissions.

Prof. Dr. Victor Chang
Guest Editor

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 Internet of Things (IIoT)
big data
supply chain
artificial intelligence

Published Papers (2 papers)

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Research

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18 pages, 1376 KiB

Open AccessArticle

Evaluation of Production of Digital Twins Based on Blockchain Technology

by Nada A. Nabeeh, Mohamed Abdel-Basset, Abduallah Gamal and Victor Chang

Electronics 2022, 11(8), 1268; https://doi.org/10.3390/electronics11081268 - 17 Apr 2022

Cited by 16 | Viewed by 2990

Abstract

A blockchain, as a form of distributed ledger technology, represents the unanimity of replication, synchronization, and sharing of data among various geographical sites. Blockchains have demonstrated impressive and effective applications throughout many aspects of the business. Blockchain technology can lead to the advent of the construction of Digital Twins (DTs). DTs involve the real representation of physical devices digitally as a virtual representation of both elements and dynamics prior to the building and deployment of actual devices. DT products can be built using blockchain-based technology in order to achieve sustainability. The technology of DT is one of the emerging novel technologies of Industry 4.0, along with artificial intelligence (AI) and the Internet of Things (IoT). Therefore, the present study adopts intelligent decision-making techniques to conduct a biased analysis of the drivers, barriers, and risks involved in applying blockchain technologies to the sustainable production of DTs. The proposed model illustrates the use of neutrosophic theory to handle the uncertain conditions of real-life situations and the indeterminate cases evolved in decision-makers’ judgments and perspectives. In addition, the model applies the analysis of Multi-criteria Decision Making (MCDM) methods through the use of ordered weighted averaging (OWA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to achieve optimal rankings for DT production providers based on consistent weighted decision-maker’s judgments in order to maintain and to assure sustainability. An empirical study is applied to the uncertain environment to aid decision-makers in achieving ideal decisions for DT providers with respect to various DT challenges, promoting sustainability and determining the best service providers. The Monte Carlo simulation method is used to illustrate, predict, and forecast the importance of the weights of decision-makers’ judgments as well as the direct impact on the sustainability of DT production. Full article

(This article belongs to the Special Issue Advances in Industrial IoT, Big Data and Supply Chain)

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Review

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17 pages, 903 KiB

Open AccessReview

A Brief Review on Internet of Things, Industry 4.0 and Cybersecurity

by Roman Rudenko, Ivan Miguel Pires, Paula Oliveira, João Barroso and Arsénio Reis

Electronics 2022, 11(11), 1742; https://doi.org/10.3390/electronics11111742 - 30 May 2022

Cited by 9 | Viewed by 3293

Abstract

The advance of industrialization regarding the optimization of production to obtain greater productivity and consequently generate more profits has led to the emergence of Industry 4.0, which aims to create an environment called smart manufacturing. On the other hand, the Internet of Things is a global network of interrelated physical devices, such as sensors, actuators, intelligent applications, computers, mechanical machines, objects, and people, becoming an essential part of the Internet. These devices are data sources that provide abundant information on manufacturing processes in an industrial environment. A concern of this type of system is processing large sets of data and generating knowledge. These challenges often raise concerns about security, more specifically cybersecurity. Good cybersecurity practices make it possible to avoid damage to production lines and information. With the growing increase in threats in terms of security, this paper aims to carry out a review of existing technologies about cybersecurity in intelligent manufacturing and an introduction to the architecture of the IoT and smart manufacturing. Full article

(This article belongs to the Special Issue Advances in Industrial IoT, Big Data and Supply Chain)

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