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Electronics
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Advanced Future Communication Techniques and Security Solutions for 6G and...
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Advanced Future Communication Techniques and Security Solutions for 6G and Internet of Things

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

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 7399

Special Issue Editors

Prof. Dr. Lixin Wang

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: network security; intrusion detection systems; wireless networks; algorithm design and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianhua Yang

E-Mail Website
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: digital topology; network security; image processing; holes counting-technical report
Special Issues, Collections and Topics in MDPI journals
Dr. Sukjin Lee

E-Mail
Guest Editor
TSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USA
Interests: internet of things; network protocols; computer vision; machine learning/neural networks; patten classification; target tracking; signal processing
Prof. Dr. Qiang Ye

E-Mail Website
Guest Editor
Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
Interests: wireless networks; mobile computing; internet of things; network security; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With commercial 5G communication networks rapidly deployed and widely available in several countries, there is an increased demand for academia and business to envision next-generation 6G communications techniques to overcome the major limitation of the 5G networks. These 6G and Internet of things (IoT) technologies will fulfill the requirements of a fully connected world and provide ubiquitous connectivity for all computing devices. 6G communication networks are expected to provide more diverse capabilities than their predecessors, and are more than likely to support diverse applications beyond current communication technologies. They are described as fully integrated, Internet-based systems that provide instant communication between users, devices, vehicles, and the surrounding environment. The wide applications, such as artificial intelligence, virtual reality, Internet of things, and blockchain technology have directed the development of 6G communication systems. The successful operation of their functionalities needs to meet stringent requirements, such as edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communication, terahertz communication, massive ultra-reliable and low-latency communications, and blockchain, to enable the inclusion of edge intelligent devices and computing. To move large amounts of data to where and when they are needed, 6G networks will need to customize services to meet demands, transmit valued data, and interact with users.

In this Special Issue of MDPI’s Electronics, we look for original and creative contributions that speak across multiple subfields of Advanced Future Communication Techniques for 6G and Internet of Things. Research papers with theoretical and/or practical approaches as well as review papers are all welcome. Topics of interest include, but are not limited to:

  • Advanced physical-layer techniques and standards;
  • AI-enabled edge computing;
  • Reconfigurable intelligent surfaces;
  • Terahertz communication techniques for 6G and IoT;
  • Unified communication platforms;
  • Massive ultra-reliable and low-latency communications;
  • 6G-based Vehicular IoT;
  • 6G for Industrial IoT;
  • Applications of AI in IoT;
  • Applications of AI in 6G;
  • Applications of 6G and IoT in the medical field;
  • Security and privacy in 6G and IoT;
  • Network security.

Dr. Lixin Wang
Dr. Jianhua Yang
Dr. Sukjin Lee
Prof. Dr. Qiang Ye
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

  • internet of things
  • 6G wireless networks
  • communication techniques
  • IoT security
  • 6G network security

Published Papers (4 papers)

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Research

17 pages, 5082 KiB  
Article
An Anomaly Detection Method Based on Multiple LSTM-Autoencoder Models for In-Vehicle Network
by Taeguen Kim, Jiyoon Kim and Ilsun You
Electronics 2023, 12(17), 3543; https://doi.org/10.3390/electronics12173543 - 22 Aug 2023
Viewed by 1565
Abstract
The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks [...] Read more.
The CAN (Controller Area Network) protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the expected behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs (Electronic Control Units) within the network. To tackle this issue, we present an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. The system effectively detects anomalies by analyzing different types of features separately using the LSTM-Autoencoder model. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system’s high precision with a 99% detection rate in identifying anomalies. Full article
(This article belongs to the Special Issue Advanced Future Communication Techniques and Security Solutions for 6G and Internet of Things)
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36 pages, 3268 KiB  
Article
Federated Learning-Based Lightweight Two-Factor Authentication Framework with Privacy Preservation for Mobile Sink in the Social IoMT
by B. D. Deebak and Seong Oun Hwang
Electronics 2023, 12(5), 1250; https://doi.org/10.3390/electronics12051250 - 5 Mar 2023
Cited by 2 | Viewed by 1934
Abstract
The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable [...] Read more.
The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches. Full article
(This article belongs to the Special Issue Advanced Future Communication Techniques and Security Solutions for 6G and Internet of Things)
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19 pages, 1531 KiB  
Article
Detecting Stepping-Stone Intrusion and Resisting Intruders’ Manipulation via Cross-Matching Network Traffic and Random Walk
by Jianhua Yang, Lixin Wang, Maochang Qin and Noah Neundorfer
Electronics 2023, 12(2), 394; https://doi.org/10.3390/electronics12020394 - 12 Jan 2023
Viewed by 1421
Abstract
Attackers can exploit compromised hosts to launch attacks over the Internet. This protects an intruder, placing them behind a long connection chain consisting of multiple compromised hosts. Such attacks are called stepping-stone intrusions. Many algorithms have been proposed to detect stepping-stone intrusions, but [...] Read more.
Attackers can exploit compromised hosts to launch attacks over the Internet. This protects an intruder, placing them behind a long connection chain consisting of multiple compromised hosts. Such attacks are called stepping-stone intrusions. Many algorithms have been proposed to detect stepping-stone intrusions, but most detection algorithms are weak in resisting intruders’ session manipulation, such as chaff-perturbation. This paper proposes a novel detection algorithm: Packet Cross-Matching and RTT-based two-dimensional random walk. Theoretical proof shows network traffic cross matching can be effective in resisting attackers’ chaff attack. Our experimental results over the AWS cloud show that the proposed algorithm can resist attackers’ chaff attacks up to a chaff rate of 100%. Full article
(This article belongs to the Special Issue Advanced Future Communication Techniques and Security Solutions for 6G and Internet of Things)
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16 pages, 449 KiB  
Article
DNNs Based Computation Offloading for LEO Satellite Edge Computing
by Jian Wu, Min Jia, Liang Zhang and Qing Guo
Electronics 2022, 11(24), 4108; https://doi.org/10.3390/electronics11244108 - 9 Dec 2022
Cited by 6 | Viewed by 1522
Abstract
Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is [...] Read more.
Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user’s offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning-based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near-optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user’s bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory. Full article
(This article belongs to the Special Issue Advanced Future Communication Techniques and Security Solutions for 6G and Internet of Things)
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