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Intelligent Industrial Internet of Things: From Theory to Real-World Applications
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Intelligent Industrial Internet of Things: From Theory to Real-World Applications

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

Deadline for manuscript submissions: 20 December 2024 | Viewed by 3848

Special Issue Editors

Dr. Joan Navarro

E-Mail Website
Guest Editor
Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: big data; cloud computing; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Víctor Caballero

E-Mail Website
Guest Editor
Computer Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: web of things; internet of things; cloud computing; semantic web; mental health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Agustín Zaballos

E-Mail Website
Guest Editor
Computer Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: internet technologies; smart grids; ubiquitous sensor networks; internet of things communications
Special Issues, Collections and Topics in MDPI journals
Dr. Alan Briones

E-Mail Website
Guest Editor
Computer Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: internet of things; digital transformation; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Ester Vidaña

E-Mail Website
Guest Editor
Computer Engineering Department of the La Salle, Universitat Ramon Llull, 08022 Barcelona, Spain
Interests: acoustic event detection; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Intelligent Industrial Internet of Things (IoT) has enabled industrial sectors to successfully integrate machinery, devices, and sensors with digital systems, networks, and data-driven applications. In fact, the number of use-cases that take advantage of the Intelligent Industrial IoT and its supporting technological pillars (e.g., low-power sensing and perception, data-driven applications, artificial intelligence, cybersecurity, and communication networks) has drastically increased in recent years. However, this rapid growth has created a noticeable asymmetry between industry and academia. Thus, it has become apparent that there is a mismatch between what is taught and researched in academic settings versus what it is actually being utilized and required in industry.

This Special Issue's main objective is to bridge this gap and (r)establish the connection and symmetry between academic teachings and industrial needs in the realm of Intelligent Industrial IoT by showcasing theory, innovative applications/use-cases, and prospective developments from both an academic and industrial perspective. We invite innovative and original research contributions to cover all aspects of the development and application of the following and related topics, including but not limited to:

  • Training methods for Intelligent IoT;
  • Artificial intelligence in IoT;
  • IoT for intelligent acoustic monitoring;
  • Computing on the edge;
  • On-device signal processing;
  • Cybersecure IoT domains;
  • IoT for naturalized environments (nature-based solutions);
  • Industrial challenges for the IoT;
  • Quantum networks for IoT domains;
  • Data-driven applications for IoT;
  • Intelligent Industrial IoT for health;
  • Web of Things and Intelligent Industrial Internet of Things;

Dr. Joan Navarro
Dr. Víctor Caballero
Prof. Dr. Agustín Zaballos
Dr. Alan Briones
Dr. Ester Vidaña
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

  • intelligent industrial IoT training
  • artificial intelligence
  • edge computing
  • IoT innovative applications
  • IoT technologies

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Published Papers (2 papers)

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Research

18 pages, 1918 KiB  
Article
Acoustic Comfort Prediction: Integrating Sound Event Detection and Noise Levels from a Wireless Acoustic Sensor Network
by Daniel Bonet-Solà, Ester Vidaña-Vila and Rosa Ma Alsina-Pagès
Sensors 2024, 24(13), 4400; https://doi.org/10.3390/s24134400 - 7 Jul 2024
Viewed by 1454
Abstract
There is an increasing interest in accurately evaluating urban soundscapes to reflect citizens’ subjective perceptions of acoustic comfort. Various indices have been proposed in the literature to achieve this purpose. However, many of these methods necessitate specialized equipment or extensive data collection. This [...] Read more.
There is an increasing interest in accurately evaluating urban soundscapes to reflect citizens’ subjective perceptions of acoustic comfort. Various indices have been proposed in the literature to achieve this purpose. However, many of these methods necessitate specialized equipment or extensive data collection. This study introduces an enhanced predictor for dwelling acoustic comfort, utilizing cost-effective data consisting of a 30-s audio clip and location information. The proposed predictor incorporates two rating systems: a binary evaluation and an acoustic comfort index called ACI. The training and evaluation data are obtained from the “Sons al Balcó” citizen science project. To characterize the sound events, gammatone cepstral coefficients are used for automatic sound event detection with a convolutional neural network. To enhance the predictor’s performance, this study proposes incorporating objective noise levels from public IoT-based wireless acoustic sensor networks, particularly in densely populated areas like Barcelona. The results indicate that adding noise levels from a public network successfully enhances the accuracy of the acoustic comfort prediction for both rating systems, reaching up to 85% accuracy. Full article
(This article belongs to the Special Issue Intelligent Industrial Internet of Things: From Theory to Real-World Applications)
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15 pages, 4736 KiB  
Article
Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
by Mayra A. Torres-Hernández, Miguel H. Escobedo-Barajas, Héctor A. Guerrero-Osuna, Teodoro Ibarra-Pérez, Luis O. Solís-Sánchez and Ma del R. Martínez-Blanco
Sensors 2023, 23(15), 6935; https://doi.org/10.3390/s23156935 - 4 Aug 2023
Cited by 1 | Viewed by 1787
Abstract
At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart [...] Read more.
At present, modern society is experiencing a significant transformation. Thanks to the digitization of society and manufacturing, mainly because of a combination of technologies, such as the Internet of Things, cloud computing, machine learning, smart cyber-physical systems, etc., which are making the smart factory and Industry 4.0 a reality. Currently, most of the intelligence of smart cyber-physical systems is implemented in software. For this reason, in this work, we focused on the artificial intelligence software design of this technology, one of the most complex and critical. This research aimed to study and compare the performance of a multilayer perceptron artificial neural network designed for solving the problem of character recognition in three implementation technologies: personal computers, cloud computing environments, and smart cyber-physical systems. After training and testing the multilayer perceptron, training time and accuracy tests showed each technology has particular characteristics and performance. Nevertheless, the three technologies have a similar performance of 97% accuracy, despite a difference in the training time. The results show that the artificial intelligence embedded in fog technology is a promising alternative for developing smart cyber-physical systems. Full article
(This article belongs to the Special Issue Intelligent Industrial Internet of Things: From Theory to Real-World Applications)
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