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Technologies
Special Issues
IoT-Enabling Technologies and Applications—2nd Edition
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IoT-Enabling Technologies and Applications—2nd Edition

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1679

Special Issue Editor

Prof. Dr. Xavier Fernando

E-Mail Website
Guest Editor
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: wireless communications; signal processing; optical–wireless communications; machine learning; IoT; tracking and localization; integrated sensing and localization; VANETs; aerial–terrestrial networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will cover original research and extensive review articles on IoT-enabling technologies and applications, including, but not limited to, the following topics:

  • IoT architectures and their applications;
  • Challenges and issues in IoT such as security, privacy, and environmental impacts;
  • Wireless sensor networks and their applications in IoT systems;
  • Integrated sensing and communications (ISACs) in IoT systems;
  • Challenges in aerial, terrestrial, and below-earth IoT networks;
  • Intelligent reflecting surfaces in IoT networks;
  • Cloud, fog, and edge computing in IoT systems;
  • Big data analytics and its use in IoT systems;
  • Embedded systems and their role in IoT systems;
  • Semantic search engines and their use in IoT systems;
  • Machine learning and artificial intelligence for IoT applications;
  • Smart cities, autonomous vehicles, and other user cases of IoT technologies;
  • Digital twins and their applications with IoT.

I look forward to your contributions.

Prof. Dr. Xavier Fernando
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. Technologies 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

  • IoT
  • security
  • privacy
  • reliability
  • machine/deep learning
  • localization
  • IoT traffic/device classification
  • sensor fusion
  • wireless sensor networks
  • energy harvesting
  • multimodal techniques
  • fog/edge computing
  • digital twins
  • latency

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Published Papers (1 paper)

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31 pages, 5551 KB  
Article
Optimized Intrusion Detection in the IoT Through Statistical Selection and Classification with CatBoost and SNN
by Brou Médard Kouassi, Abou Bakary Ballo, Kacoutchy Jean Ayikpa, Diarra Mamadou and Youssouf Diabagate
Technologies 2025, 13(10), 441; https://doi.org/10.3390/technologies13100441 - 30 Sep 2025
Viewed by 296
Abstract
With the rapid expansion of the Internet of Things (IoT), interconnected systems are becoming increasingly vulnerable to cyberattacks, making intrusion detection essential but difficult. The marked imbalance between regular traffic and attacks, as well as the redundancy of variables from multiple sensors and [...] Read more.
With the rapid expansion of the Internet of Things (IoT), interconnected systems are becoming increasingly vulnerable to cyberattacks, making intrusion detection essential but difficult. The marked imbalance between regular traffic and attacks, as well as the redundancy of variables from multiple sensors and protocols, greatly complicates this task. The study aims to improve the robustness of IoT intrusion detection systems by reducing the risks of overfitting and false negatives through appropriate rebalancing and variable selection strategies. We combine two data rebalancing techniques, Synthetic Minority Over-sampling Technique (SMOTE) and Random Undersampling (RUS), with two feature selection methods, LASSO and Mutual Information, and then evaluate their performance on two classification models: CatBoost and a Simple Neural Network (SNN). The experiments show the superiority of CatBoost, which achieves an accuracy of 82% compared to 80% for SNN, and confirm the effectiveness of SMOTE over RUS, particularly for SNN. The CatBoost + SMOTE + LASSO configuration stands out with a recall of 82.43% and an F1-score of 85.08%, offering the best compromise between detection and reliability. These results demonstrate that combining rebalancing and variable selection techniques significantly enhances the performance and reliability of intrusion detection systems in the IoT, thereby strengthening cybersecurity in connected environments. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications—2nd Edition)
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