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Artificial Intelligence for the IoT and Industrial IoT

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

Deadline for manuscript submissions: closed (25 April 2023) | Viewed by 3662

Special Issue Editors

Dr. Carlos Ramos
E-Mail Website
Guest Editor
GECAD–Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto, Porto, Portugal
Interests: artificial intelligence and decision support systems
1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals
GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal
Interests: artificial intelligence and IoT

Special Issue Information

Dear Colleagues,

The digitization of the manufacturing industry has led to leaner and more efficient production under the Industry 4.0 concept. Beginning with Industry 4.0, research on smart IoT technologies based on intelligence and connectivity is being conducted in various industrial fields. Artificial Intelligence applications open up a broad spectrum of opportunities to add value during manufacturing by optimizing processes and generating new business models.

This Special Issue intends to unite the topics of Artificial Intelligence for the IoT and Industrial IoT and, overall, address the wide range of applications of Artificial Intelligence for the IoT and Industrial IoT. Both review articles and original research papers related to the application of Artificial Intelligence for the IoT and Industrial IoT are welcome.

Topics of interest include (but are not limited to) the following:

(1) Reference Architectural Model for IoT and Industry IoT;

(2) Security concerns with AI-powered mechanisms in an IoT and Industry IoT environment;

(3) Artificial intelligence and deep learning application for IoT and Industry IoT;

(4) Performance analysis based on complex networks in IoT and Industrial IoT networks.

Dr. Carlos Ramos
Prof. Dr. Juan M. Corchado
Dr. Luis Gomes
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.

Published Papers (2 papers)

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Research

12 pages, 1830 KiB  
Article
Secure and Reliable Big-Data-Based Decision Making Using Quantum Approach in IIoT Systems
Sensors 2023, 23(10), 4852; https://doi.org/10.3390/s23104852 - 18 May 2023
Cited by 1 | Viewed by 1095
Abstract
Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big [...] Read more.
Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories. Full article
(This article belongs to the Special Issue Artificial Intelligence for the IoT and Industrial IoT)
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28 pages, 8016 KiB  
Article
Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements
Sensors 2022, 22(23), 9130; https://doi.org/10.3390/s22239130 - 24 Nov 2022
Cited by 2 | Viewed by 1507
Abstract
As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping [...] Read more.
As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping from excess pressure. However, to obtain these benefits, it is necessary to overcome some difficulties such as sensing the pressure of geographically distant consumer units and developing a control logic that is capable of making use of the data from these sensors and, at the same time, a good solution in terms of cost benefit. Therefore, this work has the purpose of developing a pressure monitoring and control system for water supply networks, using the ESP8266 microcontroller to collect data from pressure sensors for the integrated ScadaLTS supervisory system via the REST API. The modeling of the plant was developed using artificial neural networks together with fuzzy pressure control, both designed using the Python language. The proposed method was tested by considering a pumping station and two reference units located in the city of João Pessoa, Brazil, in which there was an excess of pressure in the supply network and low performance from the old controls, during the night period from 12:00 a.m. to 6:00 a.m. The field results estimated 2.9% energy saving in relation to the previous form of control and a guarantee that the pressure in the network was at a healthy level. Full article
(This article belongs to the Special Issue Artificial Intelligence for the IoT and Industrial IoT)
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