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Application of Artificial Intelligence in the New Era of Communication Networks

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 29553

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Special Issue Editors

Dr. Teodor B Iliev

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Guest Editor

Department of Telecommunications, University of Ruse, 7017 Ruse, Bulgaria
Interests: digital communications; communication theory; signal processing; channel modeling; artificial intelligence; wireless communications; mobile networks; GNSS
Special Issues, Collections and Topics in MDPI journals

Dr. Lorant Andras Szolga

E-Mail Website
Guest Editor

Basis of Electronics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Interests: VLSI technology; internet of things; spice simulation; electronics; semiconductor engineering; microelectronics

Dr. Gani Sergazin

E-Mail Website
Guest Editor

Department of Information Security, Eurasian National University, Nursultan 010000, Kazakhstan
Interests: robotics and mechatronics; artificial intelligence; machine learning; transmissions

Special Issue Information

Dear Colleagues,

Applications of machine learning in wireless and mobile communications networks have been receiving increasing attention, especially in the new era of big data and IoT, where data mining and data analysis technologies are effective approaches to solving wireless system issues. Artificial intelligence is one of the leading technologies in 5G, beyond 5G, and future 6G networks. Intelligence is endowing the tendency to throw open the capabilities of the 5G networks and the future 6G mobile wireless networks by leveraging the universal infrastructure, open network architectures, software-defined networking, network function virtualization, multi-access edge computing, vehicular network, etc. The implementation of the blockchain and mobile edge computing have become a significant part of the new wireless and mobile communication networks and will help the calculations to be performed as close to the IoT devices as possible.

The main aim of this Special Issue is to provide an overview of the current research on wireless and mobile communication technologies, based on contributions from machine learning, mobile edge computing, blockchain, and other fields of artificial intelligence, including channel modelling, signal estimation and detection, energy efficiency, vehicular communications, and wireless multimedia communications. The topics of interest include, but are not limited to:

Wireless and wireline communications;
Beyond 5G & 6G access and core networks;
Blockchain services and applications;
Artificial intelligence and intelligent systems;
Big data analysis;
Cloud technologies and applications;
Machine learning;
Internet of Everything;
Autonomous driving and V2X solutions;
Next-generation networks;
Holographic Communication;
Cyber Security;
e-Health.

Dr. Teodor B Iliev
Dr. Lorant Andras Szolga
Dr. Gani Sergazin
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. 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
wireless networks
5G and beyond
6G mobile networks
radio communications
network function virtualization
data analisys
edge computing
mmWaves
software-defined networkings
extended (XR) and augmented reality (AR)

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Related Special Issue

Application of Artificial Intelligence in the New Era of Communication Networks, 2nd Edition in Electronics (1 article)

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

3 pages, 124 KiB

Open AccessEditorial

Editorial for the Special Issue on “Application of Artificial Intelligence in the New Era of Communication Networks”

by Teodor Iliev, Lorant Andras Szolga and Gani Sergazin

Electronics 2025, 14(7), 1315; https://doi.org/10.3390/electronics14071315 - 26 Mar 2025

Viewed by 298

Abstract

The applications of machine learning in wireless and mobile communication net-works have been receiving increasing attention, especially in the new era of big data and the Internet of Things (IoT), where data mining and data analysis technologies are effective approaches to solving wireless [...] Read more.

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

Research

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16 pages, 2124 KiB

Open AccessArticle

SmartDENM—A System for Enhancing Pedestrian Safety Through Machine Vision and V2X Communication

by Abdulagha Dadashev and Árpád Török

Electronics 2025, 14(5), 1026; https://doi.org/10.3390/electronics14051026 - 4 Mar 2025

Viewed by 987

Abstract

A pivotal moment in the leap toward autonomous vehicles in recent years has revealed the need to enhance vehicle-to-everything (V2X) communication systems so as to improve road safety. A key challenge is to integrate real-time pedestrian detection to permit the use of timely alerts in situations where vulnerable road users, especially pedestrians, might pose a risk. Seeing that, in this article, a YOLO-based object detection model was used to identify pedestrians and extract key data such as bounding box coordinates and confidence levels. These data were encoded afterward into decentralized environmental notification messages (DENM) using ASN.1 schemas to ensure compliance with V2X standards, allowing for real-time communication between vehicles and infrastructure. This research identified that the integration of pedestrian detection with V2X communication brought about a reliable system wherein the roadside unit (RSU) broadcasts DENM alerts to vehicles. These vehicles, upon receiving the messages, initiate appropriate responses such as slowing down or lane changing, with the testing demonstrating reliable message transmission and high pedestrian detection accuracy in simulated–controlled environments. To conclude, this work demonstrates a scalable framework for improving road safety by combining machine vision with V2X communication. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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18 pages, 11457 KiB

Open AccessArticle

Shallow Learning-Based Intrusion Detection System for In-Vehicle Network: ASIC Implementation

by Minseok Choi, Myeongjin Lee, Hyungchul Im, Joosock Lee and Seongsoo Lee

Electronics 2025, 14(4), 683; https://doi.org/10.3390/electronics14040683 - 10 Feb 2025

Viewed by 715

Abstract

This paper presents an Application-Specific Integrated Circuit (ASIC) implementation and Field-Programmable Gate Array (FPGA) verification of a Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) designed to enhance the security of an in-vehicle Controller Area Network (CAN) BUS and detect malicious messages. The CNN model employs a lightweight architecture with a single convolution layer using a 2 × 2 kernel and integrates a filter algorithm optimized for Fuzzy and Spoofing attacks to improve the performance. The IDS is implemented on an Electronic Control Unit platform powered by an ARM Cortex-M3 core and uses SRAM to store the parameters utilized by the CNN model and filter algorithm, targeting ASIC implementation with TSMC 180 nm technology. Functional verification was conducted by configuring a simplified CAN bus environment using the Xilinx Nexys Video FPGA and PEAK-System PCAN-USB, which was validated in real-time against DoS, Spoofing, and Fuzzy attack scenarios. The proposed lightweight CNN-based IDS achieved a fast detection speed of 0.0233 ms and an average accuracy of 99.6879%, thereby demonstrating its potential to enhance the security of in-vehicle CAN BUS. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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18 pages, 4529 KiB

Open AccessArticle

Autonomous Medical Robot Trajectory Planning with Local Planner Time Elastic Band Algorithm

by Arjon Turnip, Muhamad Arsyad Faridhan, Bambang Mukti Wibawa and Nursanti Anggriani

Electronics 2025, 14(1), 183; https://doi.org/10.3390/electronics14010183 - 4 Jan 2025

Viewed by 1621

Abstract

Robots have made significant contributions across various industries due to their efficiency and effectiveness. However, indoor navigation remains challenging due to complex environments and sensor signal interference. Changes in indoor conditions and the limited range of GPS signals necessitate the development of an accurate and efficient indoor robot navigation system. This study aims to create an autonomous indoor navigation system for medical robots using sensors such as Marvelmind, LiDAR, IMU, and an odometer, along with the Time Elastic Band (TEB) local planning algorithm to detect dynamic obstacles. The algorithm’s performance is evaluated using metrics like path length, duration, speed smoothness, path smoothness, Mean Squared Error (MSE), and positional error. In the test arena, TEB demonstrated superior efficiency with a path length of 155.55 m, 9.83 m shorter than the Dynamic Window Approach (DWA), which covered 165.38 m, and had a lower yaw error of 0.012 radians. TEB outperformed DWA in terms of speed smoothness, path smoothness, and MSE. In the Sterile Room Arena, TEB had an average path length of 14.84 m, slightly longer than DWA’s 14.32 m, but TEB navigated 2.82 s faster. Additionally, TEB showed better speed and path smoothness. In the Obstacle Room Arena, TEB recorded an average path length of 21.96 m in 57.3 s, outperforming DWA, which covered 23.44 m in 61 s, with better results in MSE, speed smoothness, and path smoothness, highlighting superior path consistency. These findings indicate that the TEB algorithm is an effective choice as a local planner in dynamic hospital environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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13 pages, 5155 KiB

Open AccessArticle

A Linear Regression-Based Methodology to Improve the Stability of a Low-Cost GPS Receiver Using the Precision Timing Signals from an Atomic Clock

by Shilpa Manandhar, Sneha Saravanan, Yu Song Meng and Yung Chuen Tan

Electronics 2024, 13(16), 3321; https://doi.org/10.3390/electronics13163321 - 21 Aug 2024

Cited by 1 | Viewed by 3768

Abstract

The global positioning system (GPS) is widely known for its applications in navigation, timing, and positioning. However, its accuracy can be greatly impacted by the performance of its receiver clocks, especially for a low-cost receiver equipped with lower-grade clocks like crystal oscillators. The objective of this study is to develop a model to improve the stability of a low-cost receiver. To achieve this, a machine-learning-based linear regression algorithm is proposed to predict the differences of the low-cost GPS receiver compared to the precision timing source. Experiments were conducted using low-cost receivers like Ublox and expensive receivers like Septentrio. The model was implemented and the clocks of low-cost receivers were steered. The outcomes demonstrate a notable enhancement in the stability of low-cost receivers after the corrections were applied. This improvement underscores the efficacy of the proposed model in enhancing the performance of low-cost GPS receivers. Consequently, these low-cost receivers can be cost-effectively utilized for various purposes, particularly in applications requiring the deployment of numerous GPS receivers to achieve extensive spatial coverage. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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20 pages, 4927 KiB

Open AccessArticle

Blockchain-Based Control Plane Attack Detection Mechanisms for Multi-Controller Software-Defined Networks

by Abrar Alkhamisi, Iyad Katib and Seyed M. Buhari

Electronics 2024, 13(12), 2279; https://doi.org/10.3390/electronics13122279 - 11 Jun 2024

Cited by 2 | Viewed by 1774

Abstract

A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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20 pages, 4590 KiB

Open AccessArticle

Adaptive Whitening and Feature Gradient Smoothing-Based Anti-Sample Attack Method for Modulated Signals in Frequency-Hopping Communication

by Yanhan Zhu, Yong Li and Zhu Duan

Electronics 2024, 13(9), 1784; https://doi.org/10.3390/electronics13091784 - 5 May 2024

Cited by 1 | Viewed by 1676

Abstract

In modern warfare, frequency-hopping communication serves as the primary method for battlefield information transmission, with its significance continuously growing. Fighting for the control of electromagnetic power on the battlefield has become an important factor affecting the outcome of war. As communication electronic warfare evolves, jammers employing deep neural networks (DNNs) to decode frequency-hopping communication parameters for smart jamming pose a significant threat to communicators. This paper proposes a method to generate adversarial samples of frequency-hopping communication signals using adaptive whitening and feature gradient smoothing. This method targets the DNN cognitive link of the jammer, aiming to reduce modulation recognition accuracy and counteract smart interference. First, the frequency-hopping signal is adaptively whitened. Subsequently, rich spatiotemporal features are extracted from the hidden layer after inputting the signal into the deep neural network model for gradient calculation. The signal’s average feature gradient replaces the single-point gradient for iteration, enhancing anti-disturbance capabilities. Simulation results show that, compared with the existing gradient symbol attack algorithm, the attack success rate and migration rate of the adversarial samples generated by this method are greatly improved in both white box and black box scenarios. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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14 pages, 7335 KiB

Open AccessArticle

Towards Implementation of Emotional Intelligence in Human–Machine Collaborative Systems

by Miroslav Markov, Yasen Kalinin, Valentina Markova and Todor Ganchev

Electronics 2023, 12(18), 3852; https://doi.org/10.3390/electronics12183852 - 12 Sep 2023

Cited by 3 | Viewed by 1930

Abstract

Social awareness and relationship management components can be seen as a form of emotional intelligence. In the present work, we propose task-related adaptation on the machine side that accounts for a person’s momentous cognitive and emotional state. We validate the practical significance of the proposed approach in person-specific and person-independent setups. The analysis of results in the person-specific setup shows that the individual optimal performance curves for that person, according to the Yerkes–Dodson law, are displaced. Awareness of these curves allows for automated recognition of specific user profiles, real-time monitoring of the momentous condition, and activating a particular relationship management strategy. This is especially important when a deviation is detected caused by a change in the person’s state of mind under the influence of known or unknown factors. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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16 pages, 1328 KiB

Open AccessArticle

An Efficient Classification of Rice Variety with Quantized Neural Networks

by Mustafa Tasci, Ayhan Istanbullu, Selahattin Kosunalp, Teodor Iliev, Ivaylo Stoyanov and Ivan Beloev

Electronics 2023, 12(10), 2285; https://doi.org/10.3390/electronics12102285 - 18 May 2023

Cited by 12 | Viewed by 2992

Abstract

Rice, as one of the significant grain products across the world, features a wide range of varieties in terms of usability and efficiency. It may be known with various varieties and regional names depending on the specific locations. To specify a particular rice type, different features are considered, such as shape and color. This study uses an available dataset in Turkey consisting of five different varieties: Ipsala, Arborio, Basmati, Jasmine, and Karacadag. The dataset introduces 75,000 grain images in total; each of the 5 varieties has 15,000 samples with a 256 × 256-pixel dimension. The main contribution of this paper is to create Quantized Neural Network (QNN) models to efficiently classify rice varieties with the purpose of reducing resource usage on edge devices. It is well-known that QNN is a successful method for alleviating high computational costs and power requirements in response to many Deep Learning (DL) algorithms. These advantages of the quantization process have the potential to provide an efficient environment for artificial intelligence applications on microcontroller-driven edge devices. For this purpose, we created eight different QNN networks using the MLP and Lenet-5-based deep learning models with varying quantization levels to be trained by the dataset. With the Lenet-5-based QNN network created at the W3A3 quantization level, a 99.87% classification accuracy level was achieved with only 23.1 Kb memory size used for the parameters. In addition to this tremendous benefit of memory usage, the number of billion transactions per second (GOPs) is 23 times less than similar classification studies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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18 pages, 1133 KiB

Open AccessArticle

ML-Based Traffic Classification in an SDN-Enabled Cloud Environment

by Omayma Belkadi, Alexandru Vulpe, Yassin Laaziz and Simona Halunga

Electronics 2023, 12(2), 269; https://doi.org/10.3390/electronics12020269 - 5 Jan 2023

Cited by 13 | Viewed by 4608

Abstract

Traffic classification plays an essential role in network security and management; therefore, studying traffic in emerging technologies can be useful in many ways. It can lead to troubleshooting problems, prioritizing specific traffic to provide better performance, detecting anomalies at an early stage, etc. In this work, we aim to propose an efficient machine learning method for traffic classification in an SDN/cloud platform. Traffic classification in SDN allows the management of flows by taking the application’s requirements into consideration, which leads to improved QoS. After our tests were implemented in a cloud/SDN environment, the method that we proposed showed that the supervised algorithms used (Naive Bayes, SVM (SMO), Random Forest, C4.5 (J48)) gave promising results of up to 97% when using the studied features and over 95% when using the generated features. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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Review

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30 pages, 693 KiB

Open AccessReview

Machine-Learning-Based Traffic Classification in Software-Defined Networks

by Rehab H. Serag, Mohamed S. Abdalzaher, Hussein Abd El Atty Elsayed, M. Sobh, Moez Krichen and Mahmoud M. Salim

Electronics 2024, 13(6), 1108; https://doi.org/10.3390/electronics13061108 - 18 Mar 2024

Cited by 17 | Viewed by 5057

Abstract

Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in the New Era of Communication Networks)

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