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Future Internet
Special Issues
Artificial Intelligence-Enabled Internet of Things (IoT)
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Artificial Intelligence-Enabled Internet of Things (IoT)

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3736

Special Issue Editors

Prof. Dr. Liccardo Annalisa

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: IoT-based measurements for electrical systems; AR/VR-based distributed measurement systems; smart protections in electrical distribution systems; advanced sampling strategies for embedded measurement systems; compressive sampling-based measurements
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Flora Amato

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, 80125 Naples, Italy
Interests: artificial intelligence; machine learning; deep learning; edge computing; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a technology that enables the interconnection of devices, sensors, and data across various domains and applications. However, the IoT alone cannot fully realize its potential without the integration of Artificial Intelligence (AI), which can provide the intelligence, learning, and decision-making capabilities to the IoT systems. The combination of AI and IoT is a new frontier of innovation that promises to transform various sectors, such as smart homes, smart cities, smart industries, and smart wearables.

The aim of this special issue is to solicit original and high-quality research papers that address the challenges, opportunities, and solutions for the Artificial Intelligence Enabled Internet of Things.

Prof. Dr. Liccardo Annalisa
Prof. Dr. Flora Amato
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. Future Internet 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 1600 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

  • Artificial Intelligence
  • Internet of Things
  • edge computing
  • AI and IoT platforms
  • IoT applications

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Published Papers (2 papers)

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Research

20 pages, 4390 KiB  
Article
Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
by Abdelaziz Bouzidi, Lala Rajaoarisoa and Luka Claeys
Future Internet 2024, 16(8), 282; https://doi.org/10.3390/fi16080282 - 6 Aug 2024
Viewed by 1466
Abstract
To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions [...] Read more.
To fully harness the potential of wind turbine systems and meet high power demands while maintaining top-notch power quality, wind farm managers run their systems 24 h a day/7 days a week. However, due to the system’s large size and the complex interactions of its many components operating at high power, frequent critical failures occur. As a result, it has become increasingly important to implement predictive maintenance to ensure the continued performance of these systems. This paper introduces an innovative approach to developing a head-mounted fault display system that integrates predictive capabilities, including deep learning long short-term memory neural networks model integration, with anomaly explanations for efficient predictive maintenance tasks. Then, a 3D virtual model, created from sampled and recorded data coupled with the deep neural diagnoser model, is designed. To generate a transparent and understandable explanation of the anomaly, we propose a novel methodology to identify a possible subset of characteristic variables for accurately describing the behavior of a group of components. Depending on the presence and risk level of an anomaly, the parameter concerned is displayed in a piece of specific information. The system then provides human operators with quick, accurate insights into anomalies and their potential causes, enabling them to take appropriate action. By applying this methodology to a wind farm dataset provided by Energias De Portugal, we aim to support maintenance managers in making informed decisions about inspection, replacement, and repair tasks. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Internet of Things (IoT))
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18 pages, 620 KiB  
Article
Optimizing Requirements Prioritization for IoT Applications Using Extended Analytical Hierarchical Process and an Advanced Grouping Framework
by Sarah Kaleem, Muhammad Asim, Mohammed El-Affendi and Muhammad Babar
Future Internet 2024, 16(5), 160; https://doi.org/10.3390/fi16050160 - 6 May 2024
Viewed by 1592
Abstract
Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with [...] Read more.
Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Internet of Things (IoT))
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Self-Supervised Learning Approaches in Italian Dysarthric Speech Recognition
Authors: Davide Mulfari and Massimo Villari
Affiliation: MIFT Department - University of Messina, Italy
Abstract: In recent years, Self-Supervised Learning (SSL) approaches have gained prominence across various domains. One particularly relevant field where SSL has made significant strides is Automatic Speech Recognition (ASR). In ASR, SSL methods leverage pre-trained models on large amounts of unlabeled speech data, followed by fine-tuning on smaller annotated datasets. This approach effectively addresses the challenge posed by the scarcity of speech data, especially in the context of minority languages. This article delves into the advantages of SSL specifically within the domain of dysarthric ASR. Dysarthria, a common neuromotor speech disorder, results in poor speech intelligibility, making it a critical area for research. The lack of comprehensive speech corpora further compounds the challenge. To tackle this, we propose combining SSL with Transformers-based ASR models, with a focus on leveraging Wav2Vec2 architecture. The final goal is to create isolated word recognizers for disordered speech that can operate efficiently even on edge computing nodes with limited resources. Experiments, conducted on our private dysarthric Italian corpus, demonstrate the effectiveness of our approach, with a Word Error Rate (WER) of 3.5%, showcasing the promise of SSL in enhancing ASR systems for persons with atypical speech.

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