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Technologies
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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 December 2026 | Viewed by 7102

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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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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Related Special Issue

Published Papers (5 papers)

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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
Cited by 3 | Viewed by 1380
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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34 pages, 15045 KB  
Article
Integration of Road Data Collected Using LSB Audio Steganography
by Adam Stančić, Ivan Grgurević, Marko Matulin and Marko Periša
Technologies 2025, 13(12), 597; https://doi.org/10.3390/technologies13120597 - 18 Dec 2025
Viewed by 1096
Abstract
Modern traffic-monitoring systems increasingly rely on supplemental analytical data to complement video recordings, yet such data are rarely integrated into video containers without altering the original footage. This paper proposes a lightweight audio-based approach for embedding road-condition information using a Least Significant Bit [...] Read more.
Modern traffic-monitoring systems increasingly rely on supplemental analytical data to complement video recordings, yet such data are rarely integrated into video containers without altering the original footage. This paper proposes a lightweight audio-based approach for embedding road-condition information using a Least Significant Bit (LSB) steganography framework. The method operates by serializing sensor data, encoding it into the LSB positions of synthetically generated audio, and subsequently compressing the audio track while preserving imperceptibility and video integrity. A series of controlled experiments evaluates how waveform type, sampling rate, amplitude, and frequency influence the storage efficiency and quality of WAV and FLAC stego-audio files. Additional tests examine the impact of embedding capacity and output-quality settings on compression behavior. Results reveal clear trade-offs between audio quality, data capacity, and file size, demonstrating that the proposed framework enables efficient, secure, and scalable integration of metadata into surveillance recordings. The findings establish practical guidelines for deploying LSB-based audio embedding in real traffic-monitoring environments. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications—2nd Edition)
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19 pages, 2421 KB  
Article
Modeling of a Hardware and Software System for Non-Invasive Monitoring of the Feeding Behavior of Farm Animals
by Oleg Ivashchuk, Zhanat Kenzhebayeva, Alexei Zhigalov, Moldir Allaniyazova, Gulnara Kaziyeva, Kaiyrbek Makulov, Vyacheslav Fedorov and Olga Ivashchuk
Technologies 2026, 14(2), 127; https://doi.org/10.3390/technologies14020127 - 18 Feb 2026
Viewed by 574
Abstract
This paper presents the design of a hardware–software system for non-invasive automated monitoring of feeding behavior in livestock with biometric identification of individual animals. Neural network models for animal identification from images and individual recognition have been developed and trained. A solution is [...] Read more.
This paper presents the design of a hardware–software system for non-invasive automated monitoring of feeding behavior in livestock with biometric identification of individual animals. Neural network models for animal identification from images and individual recognition have been developed and trained. A solution is proposed to address the challenge of acquiring a sufficient number of personalized animal images for training the identification neural network. A transfer learning approach is introduced for pig identification, where the network is first trained on a large-scale dataset of more than three million human face images obtained from open sources and subsequently fine-tuned by training the upper layers on a significantly smaller dataset consisting of 5610 pig face images. Experimental results demonstrated the high effectiveness of the system: the Top-1 identification accuracy reached 95.1%, while the ROC AUC in open-set recognition tasks achieved 0.95. The processing time per frame on an NVIDIA RTX 4090 GPU was 1.4 ms (724 FPS). Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications—2nd Edition)
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28 pages, 3634 KB  
Article
Design and Deployment of an IoT-Based Digital Agriculture System in a Hydroponic Plant Factory
by Herrera-Arroyo Raul Omar, Moreno-Aguilera Cristal Yoselin, Coral Martinez-Nolasco, Víctor Sámano-Ortega, Mauro Santoyo-Mora and Martínez-Nolasco Juan José
Technologies 2026, 14(5), 247; https://doi.org/10.3390/technologies14050247 - 22 Apr 2026
Viewed by 1197
Abstract
The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to [...] Read more.
The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to a Plant Factory (PF) for hydroponic vegetable cultivation using the Nutrient Film Technique (NFT). The objective of this study was to develop a system capable of effectively monitoring and controlling the environmental variables that directly influence the microclimate of a closed agricultural environment. The proposed system integrates a four-layer IoT architecture based on a MODBUS RS-485 communication bus, which allows for continuous data acquisition and the operation of multiple sensors and controlled devices. Additionally, user-oriented tools such as a human–machine interface (HMI), a web application, a mobile application and an automatic alert module were incorporated, enhancing accessibility and remote supervision. Experimental results showed stable control performance of ambient temperature (TA), relative humidity (RH), photoperiod, and photosynthetic photon flux density (PPFD), along with continuous monitoring of CO2 concentration. A 30-day validation experiment using Swiss chard (Beta vulgaris L. var. cicla) under controlled conditions was conducted. The results showed progressive plant development, with leaf area increasing from 15.17 cm2 to 690.39 cm2, plant height from 7 cm to 31 cm, fresh weight from 23 g to 171 g, and the number of leaves from 9 to 20. These results support the functional validity of the proposed system as a reliable platform for environmental monitoring and control in controlled-environment agriculture. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications—2nd Edition)
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23 pages, 2625 KB  
Article
An Enhanced XGBoost-Based Framework for Efficient Multi-Class Cyber Threat Detection in Industrial IoT Networks
by Adel A. Ahmed and Talal A. A. Abdullah
Technologies 2026, 14(5), 274; https://doi.org/10.3390/technologies14050274 - 1 May 2026
Cited by 1 | Viewed by 786
Abstract
Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper [...] Read more.
Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper proposes an efficient multi-stage intrusion detection framework based on an enhanced Extreme Gradient Boosting (XGBoost) model for IIoT environments. The proposed framework integrates data preprocessing, class imbalance handling, hyperparameter optimization, probability calibration, and class-specific decision thresholds within a unified pipeline. In addition, calibrated probability outputs are utilized as continuous indicators of prediction confidence, enabling more reliable and risk-aware decision-making. The hierarchical multi-stage design decomposes the detection task into progressively refined classification levels, improving discrimination among complex and overlapping attack categories. The framework is evaluated using the Edge-IIoTset benchmark dataset, which reflects realistic IIoT network traffic under both normal and malicious conditions. Experimental results demonstrate that the proposed approach achieved significant performance improvements, including up to 21% increase in recall and 15% improvement in macro F1 score compared to the baseline models. Furthermore, the model exhibits low inference latency and supports efficient deployment in time-sensitive IIoT monitoring scenarios. These results indicate that the proposed framework provides an effective and scalable solution for multi-class cyber threat detection in IIoT networks. Full article
(This article belongs to the Special Issue IoT-Enabling Technologies and Applications—2nd Edition)
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