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Future Internet
Special Issues
Intelligent IoT and Wireless Communication
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Intelligent IoT and Wireless Communication

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 800

Special Issue Editors

Dr. Suhardi Azliy Junoh

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: wireless networks; Internet of Things; pervasive computing; machine learning
Prof. Dr. Jae-Young Pyun

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: machine learning; deep learning; IoT protocol design; IoT applications in indoor positioning systems; real-time location systems; UWB radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of smart devices and the Internet of Things (IoT) has driven the need for intelligent wireless communication and positioning systems across various applications, including indoor navigation, security, and smart cities. However, challenges such as dynamic network environments, resource allocation, and secure data transmission require advanced solutions. Artificial intelligence (AI) and machine learning (ML) are crucial in enhancing network efficiency, optimizing resource management, and improving localization accuracy. This Special Issue explores AI-driven IoT networks and wireless communication, covering intelligent signal processing, energy-efficient network design, indoor and outdoor localization, and secure data exchange. We invite the submission of original research, review articles, and innovative algorithmic developments that contribute to both theoretical advancements and practical implementations in this evolving field.

Dr. Suhardi Azliy Junoh
Prof. Dr. Jae-Young Pyun
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
  • wireless communication
  • internet of things
  • smart cities
  • indoor positioning systems

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Published Papers (1 paper)

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Research

34 pages, 2740 KiB  
Article
Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
by Anja Tanović and Ivan Mezei
Future Internet 2025, 17(8), 343; https://doi.org/10.3390/fi17080343 - 30 Jul 2025
Viewed by 457
Abstract
This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point [...] Read more.
This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point arithmetic. The system is trained on a reduced MNIST dataset (1000 resized samples) and evaluated using EMNIST and MNIST-C for anomaly detection. Seven fully connected autoencoder architectures are first evaluated on a PC to explore the impact of model size and batch size on training time and anomaly detection performance. Selected models are then re-implemented in the C programming language and deployed on a single ESP32 device, achieving training times as short as 12 min, inference latency as low as 9 ms, and F1 scores of up to 0.87. Autoencoders are further tested on ten devices in a real-world federated learning experiment using Wi-Fi. We explore non-IID and IID data distribution scenarios: (1) digit-specialized devices and (2) partitioned datasets with varying content and anomaly types. The results show that small unmodified autoencoder models can be effectively trained and evaluated directly on low-power hardware. The best models achieve F1 scores of up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. Despite some clients being trained on corrupted datasets, federated aggregation proves resilient, maintaining high overall performance. The resource analysis shows that more than half of the models and all the training-related allocations fit entirely in internal RAM. These findings confirm the feasibility of local float32 training and collaborative anomaly detection on low-cost hardware, supporting scalable and privacy-preserving edge intelligence. Full article
(This article belongs to the Special Issue Intelligent IoT and Wireless Communication)
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