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Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous...
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Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 5521

Special Issue Editors

Dr. Rameez Asif

E-Mail Website
Guest Editor
National Centre for Motorsport Engineering (NCME), Faculty of Engineering, University of Bolton, Bolton BL3 5AB, UK
Interests: artificial intelligence and robotics; microwave and wireless communications; signal processing; avionics communications; heterogeneous wireless networks; software defined radios; miniaturized transceiver design; meta-materials design; MU-massive MIMO
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Ali

E-Mail Website
Guest Editor
Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK
Interests: avionics communications networks; heterogeneous wireless networks; software defined networks; artificial intelligence; embedded systems; network security and desktop application development

Special Issue Information

Dear Colleagues,

The demand for the Always Best Connected (ABC) paradigm is evolving throughout the life cycle of communications and the digital entertainment industry. Today, with the sky-rocketing demands of digitally connected life globally, more bandwidth is needed than ever. The advent of technologies such as 4G, 5G and 6G is contributing considerably to fulfilling the data speed demand, but the extent of their coverage represents a barrier to these technologies. Several solutions have been proposed by researchers around the world to overcome the limitations of individual radio access technologies such as cognitive radios, collaborative radio resource management, heterogenous wireless communications networks etc., with the assistance of Artificial Intelligence (AI), machine learning (ML), and new technologies such as software-defined networking (SDN), etc. The amalgamation of different radio access technologies, AI/ML, SDN, and other new technologies introduces a large number of benefits but comes with the price of new cybersecurity vulnerabilities and new additions and upgrades in the architecture and protocol stacks.

This Special Issue aims to address issues that are involved in the analysis, design, and implementation of different communication layers featuring in a heterogeneous wireless network for seamless mobility, security, and resource allocation augmented with AI/ML, SDN, and other new technologies, including techniques that can help to secure this communication.

This includes:

  • Heterogeneous wireless networks;
  • Seamless mobility in heterogeneous wireless networks;
  • Satellite communications;
  • Vehicular communications networks based on software-defined networks;
  • AI/ML-assisted radio link selection;
  • Channel design and coding;
  • AI/ML-assisted cybersecurity for heterogeneous wireless networks;
  • Mobility protocols for fast moving vehicular communications networks;
  • SDN-assisted security architecture for heterogeneous wireless networks;
  • Link selection in multi-link node wireless networks;
  • Handovers in wireless networks;
  • Load balancing in wireless networks;
  • Network management;
  • Encryption techniques for transmitter and receiver design;
  • Cybersecurity.

Dr. Rameez Asif
Dr. Muhammad Ali
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

  • encryption techniques
  • wireless networks
  • AI/ML
  • SDN
  • link selection

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Published Papers (4 papers)

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Research

20 pages, 9115 KiB  
Article
Optimized Real-Time Decision Making with EfficientNet in Digital Twin-Based Vehicular Networks
by Qasim Zia, Avais Jan, Dong Yang, Haijing Zhang and Yingshu Li
Electronics 2025, 14(6), 1084; https://doi.org/10.3390/electronics14061084 - 9 Mar 2025
Viewed by 1015
Abstract
Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular [...] Read more.
Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular ad hoc network applications now use convolutional neural networks (CNNs). However, the growing model size and latency make implementing them in real-time systems challenging, and most previous studies focusing on using CNNs still face significant challenges. Some effective models with sustainable performances have recently been proposed. One of the most advanced models among them is EfficientNet. One may consider it a family of network models with significantly fewer parameters and computational costs. This paper proposes EfficientNet-based optimized real-time decision-making in the DT-VANET architecture. This paper investigates the performance of EfficientNet in digital-based vehicular ad hoc networks. Extensive experiments have proved that EfficientNet outperforms CNN models (ResNet50, VGG16) in accuracy, latency, computational efficiency, and convergence time, which proves its effectiveness in real-time applications of DT-VANET. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
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14 pages, 382 KiB  
Article
Smart Wireless Sensor Networks with Virtual Sensors for Forest Fire Evolution Prediction Using Machine Learning
by Ahshanul Haque and Hamdy Soliman
Electronics 2025, 14(2), 223; https://doi.org/10.3390/electronics14020223 - 7 Jan 2025
Cited by 2 | Viewed by 1377
Abstract
Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to [...] Read more.
Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to emulate forest fire dynamics and predict fire scenarios using machine learning. Building on this foundation, this study explores the integration of virtual sensors to enhance the prediction capabilities of the WSN. Virtual sensors were generated using polynomial regression models and incorporated into a supervector framework, effectively augmenting the data from physical sensors. The enhanced dataset was used to train a multi-layer perceptron neural network (MLP NN) to classify multiple fire scenarios, covering both early warning and advanced fire states. Our experimental results demonstrate that the addition of virtual sensors significantly improves the accuracy of fire scenario predictions, especially in complex situations like “Fire with Thundering” and “Fire with Thundering and Lightning”. The extended model’s ability to predict early warning scenarios such as lightning and smoke is particularly promising for proactive fire management strategies. This paper highlights the potential of combining physical and virtual sensors in WSNs to achieve superior prediction accuracy and scalability of the field without any extra cost. Such findings pave the way for deploying scalable (cost-effective), intelligent monitoring systems capable of addressing the growing challenges of forest fire prevention and management. We obtained significant results in specific scenarios based on the number of virtual sensors added, while in some scenarios, the results were less promising compared to using only physical sensors. However, the integration of virtual sensors enables coverage of much larger areas, making it a highly promising approach despite these variations. Future work includes further optimization of the virtual sensor generation process and expanding the system’s capability to handle large-scale forest environments. Moreover, utilizing virtual sensors will alleviate many challenges associated with the huge number of deployed physical sensors. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
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13 pages, 432 KiB  
Article
Transmit Precoding via Block Diagonalization with Approximately Optimized Distance Measures for Limited Feedback in Dense Cellular Networks with Multiantenna Base Stations
by Sihoon Kwak, Jae-Ik Kong and Moonsik Min
Electronics 2024, 13(20), 3973; https://doi.org/10.3390/electronics13203973 - 10 Oct 2024
Viewed by 683
Abstract
This study introduces distance metrics for quantized-channel-based precoding in multiuser multiantenna systems, aiming to enhance spectral efficiency in dense cellular networks. Traditional metrics, such as the chordal distance, face limitations when dealing with scenarios involving limited feedback and multiple receive antennas. We address [...] Read more.
This study introduces distance metrics for quantized-channel-based precoding in multiuser multiantenna systems, aiming to enhance spectral efficiency in dense cellular networks. Traditional metrics, such as the chordal distance, face limitations when dealing with scenarios involving limited feedback and multiple receive antennas. We address these challenges by developing distance measures that more accurately reflect network conditions, including the impact of intercell interference. Our distance measures are specifically designed to approximate the instantaneous rate of each user by estimating the unknown components during the quantization stage. This approach enables the associated users to efficiently estimate their achievable rates during the quantization process. Our distance measures are specifically designed for block diagonalization precoding, a method known for its computational efficiency and strong performance in multi-user multiple-input and multiple-output systems. The proposed metrics outperform conventional distance measures, particularly in environments where feedback resources are constrained, as is often the case in 5G and emerging 6G networks. The enhancements are especially significant in dense cellular networks, where accurate channel state information is critical for maintaining high spectral efficiency. Our findings suggest that these new distance measures offer a robust solution for improving the performance of limited-feedback-based precoding in cellular networks. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
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18 pages, 1292 KiB  
Article
Network Attack Classification with a Shallow Neural Network for Internet and Internet of Things (IoT) Traffic
by Jörg Ehmer, Yvon Savaria, Bertrand Granado, Jean-Pierre David and Julien Denoulet
Electronics 2024, 13(16), 3318; https://doi.org/10.3390/electronics13163318 - 21 Aug 2024
Cited by 5 | Viewed by 1425
Abstract
In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, [...] Read more.
In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems. Full article
(This article belongs to the Special Issue Machine Learning (ML) Augmented Communication Techniques for Secure Mobile Heterogeneous Wireless Networks and Safety Critical Networks)
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