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Electronics
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IoT Based Intelligent Communications: Modelling, Practice and Applications
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IoT Based Intelligent Communications: Modelling, Practice and Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 March 2026 | Viewed by 3247

Special Issue Editors

Prof. Dr. Di Lin

E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: big data analysis; Internet of Things; mobile cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Zongbo Hao

E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: deep learning; image and video processing

Special Issue Information

Dear Colleagues,

In the vast and ever-evolving realm of the Internet of Things (IoT), conventional machine learning (ML) algorithms have played a crucial role in solving complex functions and facilitating complicated decision-making processes. Moreover, ML has also shown its potential capabilities in guiding IoT towards more autonomous and efficient systems, as demonstrated by its efficiency in tasks such as big data analysis and interpretation. However, with the arrival of the sixth generation (6G) communications era, marked by ubiquitous connectivity, extremely low latency, and ultrahigh reliability, the future landscape of IoT is undergoing significant changes. In this context, conventional ML models are encountering substantial challenges in adapting to the vast scale and complexity of data, fulfilling the requirements for real-time processing, and maintaining stable performance within interconnected IoT systems.

The emergence of artificial intelligence (AI) represents a significant milestone in overcoming the limitations of conventional ML. AI models are redefining the scope of ML, introducing capabilities for customized data generation and real-time adaptation. This paradigm shift in AI indicates a potential for mutual enhancement between AI and IoT. This interaction fosters groundbreaking innovations, where AI enhances the capabilities of IoT and IoT enriches AI, reciprocally. While the integration of AI and IoT brings promising advancements, it introduces several challenges. From the perspective of AI empowering IoT, as IoT devices proliferate and the generated data become increasingly complicated, the optimization of AI algorithms for large-scale data processing becomes challenging. Moreover, ensuring the flexibility of AI systems to adapt to the dynamic environments within IoT, where the wireless channel, user states, and system resources typically change at varying timescales, complicates efficient and real-time decisions. In addition, regarding the enhancement of AI by IoT, maintaining the relevance and quality of data sourced from IoT devices could be complicated since the data must be representative, unbiased, and comprehensive to enable accurate learning and adaptation by AI. Furthermore, as AI integrates with various IoT devices, security and privacy concerns emerge due to extensive interconnections.

Tackling these challenges is essential to facilitate the integration of AI and the future IoT. Accordingly, this Special Issue is dedicated to offering a platform for researchers from both academia and industry to share the latest research findings and innovative solutions for the integration of AI and IoT. 

The potential topics of this special issue include, but are not limited to, the following:

  • AI-assisted radio resource management in IoT;
  • Network optimization in IoT through AI;
  • AI-enabled security protection and privacy preserving in IoT;
  • Mobile edge intelligence based on AI for IoT;
  • AI for security at physical layer designs for IoT;
  • AI-based integrated sensing and communication for IoT;
  • AI-enabled IoT for healthcare, education, and transportation applications;
  • Testbed and platform for AI-enabled IoT.

Prof. Dr. Di Lin
Dr. Zongbo Hao
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 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. Electronics 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 2400 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
  • machine learning

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Published Papers (3 papers)

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Research

27 pages, 7033 KB  
Article
Network Traffic Prediction for Multiple Providers in Digital Twin-Assisted NFV-Enabled Network
by Ying Hu, Ben Liu, Jianyong Li and Linlin Jia
Electronics 2025, 14(20), 4129; https://doi.org/10.3390/electronics14204129 - 21 Oct 2025
Cited by 1 | Viewed by 567
Abstract
This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and network resource providers. It faces several key challenges like data privacy [...] Read more.
This manuscript investigates the network traffic prediction problem, with the aim of predicting network traffic on a network function virtualization (NFV)-enabled and digital twin (DT)-assisted physical network for network service providers and network resource providers. It faces several key challenges like data privacy and different variation patterns of network traffic for multiple service function chain (SFC) requests. In view of this, we address the network traffic prediction problem by jointly considering the above key challenges in this manuscript. Specifically, we formulate the virtual network function (VNF) migration and SFC placement problems as integer linear programming (ILP) that aim to maximize acceptance revenues, minimize network resource costs, minimize energy consumption, and minimize migration cost. Then, we define the Markov Decision Process (MDP) for the network traffic prediction problem, and propose a model and algorithm to solve the problem. The simulation results demonstrate that our algorithms outperform benchmark algorithms and achieve a better performance. Full article
(This article belongs to the Special Issue IoT Based Intelligent Communications: Modelling, Practice and Applications)
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25 pages, 16941 KB  
Article
KAN-Sense: Keypad Input Recognition via CSI Feature Clustering and KAN-Based Classifier
by Minseok Koo and Jaesung Park
Electronics 2025, 14(15), 2965; https://doi.org/10.3390/electronics14152965 - 24 Jul 2025
Cited by 1 | Viewed by 777
Abstract
Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition [...] Read more.
Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article
(This article belongs to the Special Issue IoT Based Intelligent Communications: Modelling, Practice and Applications)
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20 pages, 2478 KB  
Article
An RF Fingerprinting Blind Identification Method Based on Deep Clustering for IoMT Security
by Di Lin, Yansu Pang, Shenyuan Chen, Jun Huang and Haoqi Xian
Electronics 2025, 14(8), 1504; https://doi.org/10.3390/electronics14081504 - 9 Apr 2025
Cited by 1 | Viewed by 1077
Abstract
To tackle the issue of unknown spoofing attacks in the Internet of Medical Things (IoMT), we put forward an iterative deep clustering model for blind RF fingerprint recognition. This model seamlessly combines a representation learning module and a clustering module, facilitating end—to—end training [...] Read more.
To tackle the issue of unknown spoofing attacks in the Internet of Medical Things (IoMT), we put forward an iterative deep clustering model for blind RF fingerprint recognition. This model seamlessly combines a representation learning module and a clustering module, facilitating end—to—end training and optimization. Its parameters are updated according to an innovative loss function. Moreover, this model incorporates a noise—canceling self—encoder module to reduce noise and extract the noise—independent intrinsic fingerprints of devices. In comparison with other algorithms, the proposed model remarkably improves the blind recognition performance for the identification of unknown devices in the IoMT. Full article
(This article belongs to the Special Issue IoT Based Intelligent Communications: Modelling, Practice and Applications)
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