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Processes
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Innovating Architecture, Processes and Applications in Industry IoT
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Innovating Architecture, Processes and Applications in Industry IoT

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 6047

Special Issue Editors

Dr. Guo-Shiang Lin

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 402, Taiwan
Interests: image/video processing; machine learning; computer vision; multimedia applications
Special Issues, Collections and Topics in MDPI journals
Dr. Ming-Te Chen

E-Mail Website
Guest Editor
Department of Information Computer Science, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: industry IoT; information security; cryptography; digital signature
Dr. Chieh-Ling Huang

E-Mail Website
Guest Editor
Department of Information Computer Science, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: industry IoT; video image processing; digital image processing
Dr. Yi-Ying Chang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: signal, image and video processing; image segmentation; digital image processing

Special Issue Information

Dear Colleagues,

In the past decade, with the advancement of ICT communication technology, Industry 4.0 has also transformed from manual control processes to automatic control processes. However, with the rise of Industry 4.0, it also drives the development of related research topics such as Industrial Internet of Things (IIoT), artificial intelligence, big data analysis, intelligent manufacturing, digital twin, cognitive intelligence and cyber-physical systems. Industry 4.0 also integrates ICT technologies to efficiently provide manufacturers and supply chain manufacturers with solutions and improve productivity to build smart factories.

 However, at present, smart factories are not only related to the acquisition of production data and the digitization of related manufacturing processes, but also have a direct impact on the evolution of production lines and upstream and downstream supply chains. Most smart factories are based on the IIoT architecture framework model.  Through IIoT model, smart factories can build heterogeneous network sensing devices and related networks (sensors, embedded computers, RFID tags, edge computing networks, mobile devices) to convey sensored information.  Based on IoT information sensing and big data processing technology, IIoT can improve the time efficiency, scalability, interconnectivity, cost-effectiveness, security and operational efficiency of the IoT-based architecture. Under such a framework, an industrial automation feedback information system can be constructed through an intelligent network connection. With the rapid development of this field, knowledge interaction between manufacturers and stakeholders to drive new trends and challenges in the Industrial Internet of Things is a major issue. Therefore, we invite colleagues from the research community to share their innovative contributions to the Industrial Internet of Things. Special attention but not limited to:

  • Developing and new methodology and architectures for IoT;
  • Digital Twin system design and new models for Industry 4.0;
  • Cloud-based solutions for IIoT;
  • Machine-Learning-based on smart IIoT solutions;
  • Data-mining techniques for IIoT industry 4.0;
  • Smart IIoT solutions;
  • Big data analysis and data process optimization in industry 4.0;
  • Cyber-physical system design and new models;
  • Performance, scalability, and reliability in the IIoT;
  • Data security and privacy aspects in the IIoT;
  • Standard, platforms, testbed, and validation for the IIoT;
  • IIoT for regulatory and supervisory control systems.

Dr. Guo-Shiang Lin
Dr. Ming-Te Chen
Dr. Chieh-Ling Huang
Dr. Yi-Ying Chang
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. Processes is an international peer-reviewed open access monthly 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

  • Industry 4.0
  • industry IoT
  • smart IoT
  • cyber-physical system
  • data security
  • reliability
  • scalability
  • digital twin system

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Published Papers (2 papers)

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14 pages, 3619 KiB  
Article
Enhanced Cyber Attack Detection Process for Internet of Health Things (IoHT) Devices Using Deep Neural Network
by Kedalu Poornachary Vijayakumar, Krishnadoss Pradeep, Ananthakrishnan Balasundaram and Manas Ranjan Prusty
Processes 2023, 11(4), 1072; https://doi.org/10.3390/pr11041072 - 3 Apr 2023
Cited by 32 | Viewed by 3521
Abstract
Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming [...] Read more.
Internet of Health Things plays a vital role in day-to-day life by providing electronic healthcare services and has the capacity to increase the quality of patient care. Internet of Health Things (IoHT) devices and applications have been growing rapidly in recent years, becoming extensively vulnerable to cyber-attacks since the devices are small and heterogeneous. In addition, it is doubly significant when IoHT involves devices used in healthcare domain. Consequently, it is essential to develop a resilient cyber-attack detection system in the Internet of Health Things environment for mitigating the security risks and preventing Internet of Health Things devices from becoming exposed to cyber-attacks. Artificial intelligence plays a primary role in anomaly detection. In this paper, a deep neural network-based cyber-attack detection system is built by employing artificial intelligence on latest ECU-IoHT dataset to uncover cyber-attacks in Internet of Health Things environment. The proposed deep neural network system achieves average higher performance accuracy of 99.85%, an average area under receiver operator characteristic curve 0.99 and the false positive rate is 0.01. It is evident from the experimental result that the proposed system attains higher detection rate than the existing methods. Full article
(This article belongs to the Special Issue Innovating Architecture, Processes and Applications in Industry IoT)
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16 pages, 5112 KiB  
Article
Environmental Prediction in Cold Chain Transportation of Agricultural Products Based on K-Means++ and LSTM Neural Network
by Junjie Jiang, Cuiling Peng, Wenjing Liu, Shuangyin Liu, Zhijie Luo and Ningxia Chen
Processes 2023, 11(3), 776; https://doi.org/10.3390/pr11030776 - 6 Mar 2023
Cited by 7 | Viewed by 2139
Abstract
Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment [...] Read more.
Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment prediction method, combining k-means++ and a long short-term memory (LSTM) neural network, is proposed according to the characteristics of the cold chain transportation environment of agricultural products. The proposed prediction model can predict the trend of cold chain environment changes in the next ten minutes, which allows cold chain vehicle managers to issue control instructions to the environmental control equipment in advance. The fusion process for temperature and humidity data measured by multiple data sensors is performed with the k-means++ algorithm, and then the fused data are fed into an LSTM neural network for prediction based on time series. The prediction error of the prediction model proposed in this paper is very satisfactory, with a root-mean-square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and R-squared of 0.5707, 0.2484, 0.3258, 0.0312 and 0.9660, respectively, for temperature prediction, and with an RMSE, MAE, MSE, mean absolute percentage error and R-squared of 1.6015, 1.1770, 2.5648, 0.2736 and 0.9702, respectively, for humidity prediction. Finally, the LSTM neural network and back propagation (BP) neural network are compared in order to enhance the reliability of the results. In terms of the prediction effect of the temperature and humidity in cold chain vehicles transporting agricultural products, the proposed model has a higher prediction accuracy than that of existing models and can provide strategic support for the fine management and regulation of the cold chain transportation environment. Full article
(This article belongs to the Special Issue Innovating Architecture, Processes and Applications in Industry IoT)
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