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Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond
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Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 4433

Special Issue Editors

Dr. Razvan Craciunescu

E-Mail Website
Guest Editor
Telecommunication Department, University Politechnica of Bucharest, Bucharest, Romania
Interests: 5G; 6G; Internet of Things; wireless sensors networks; digital twin; vehicular sensor networks; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Ciprian Romeo Comşa

E-Mail Website
Guest Editor
Telecommunication Department, Gheorghe Asachi Technical University of Iasi (TUIASI), 700506 Iasi, Romania
Interests: 5G; 6G; Internet of Things; localization; automotive embedded systems; V2X; vehicular sensor networks; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a new Special Issue, entitled "Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond". The advent of 5G and the emerging development of 6G have opened up new possibilities in the field of the Internet of Things (IoT). This Special Issue aims to explore the cutting-edge technologies that leverage 5G/6G capabilities to revolutionize IoT environments and extend their impact beyond traditional boundaries. Additionally, this Special Issue is dedicated to exploring other environments related to IoT.

The convergence of 5G/6G and IoT brings forth unprecedented opportunities for enhanced connectivity, ultra-low latency, massive device connectivity, and high data rates. These advancements pave the way for transformative applications across diverse domains, including smart cities, industrial automation, healthcare, transportation, agriculture, and beyond. This Special Issue seeks to uncover innovative research and breakthroughs that leverage the synergistic potential of 5G/6G and IoT.

We invite authors to submit original research articles that showcase the latest advancements and demonstrate the state of the art in leveraging advanced technologies in 5G/6G-enabled IoT environments. Contributions may encompass a broad range of topics, such as novel communication protocols, network architectures, edge computing solutions, artificial intelligence and machine learning techniques, security and privacy mechanisms, slicing, zero-touch orchestration, energy efficiency strategies, and novel applications empowered by the convergence of 5G/6G and IoT.

Furthermore, we encourage the submission of articles that explore the broader impact of these advanced technologies beyond IoT. This may include the integration of emerging technologies like augmented reality, virtual reality, edge intelligence, and other groundbreaking paradigms that extend the frontiers of 5G/6G-enabled environments.

Authors are encouraged to present their research findings, methodologies, experimental evaluations, real-world deployments, and comprehensive reviews of the state of the art. We welcome articles that provide valuable insights, address challenges, and propose innovative solutions to shape the future of 5G/6G-enabled IoT and its intersection with other revolutionary technologies.

We look forward to receiving your valuable contributions to this Special Issue.

Dr. Razvan Craciunescu
Dr. Ciprian Romeo Comşa
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. Sensors 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 2600 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

  • 5G/6G
  • Internet of Things (IoT)
  • edge intelligence
  • zero-touch orchestration
  • artificial intelligence (AI)
  • security and privacy
  • energy efficiency
  • augmented reality (AR)
  • wireless AI

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Published Papers (3 papers)

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Research

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30 pages, 7641 KiB  
Article
Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method
by Sanying Zhu, Shutong Zhou, Liuquan Wang, Chenxin Zang, Yanqiang Liu and Qiang Liu
Sensors 2024, 24(20), 6542; https://doi.org/10.3390/s24206542 - 10 Oct 2024
Viewed by 493
Abstract
With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure [...] Read more.
With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions. Full article
(This article belongs to the Special Issue Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond)
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18 pages, 16371 KiB  
Article
Insight into Anomaly Detection and Prediction and Mobile Network Security Enhancement Leveraging K-Means Clustering on Call Detail Records
by Zagroz Aziz and Robert Bestak
Sensors 2024, 24(6), 1716; https://doi.org/10.3390/s24061716 - 7 Mar 2024
Viewed by 1546
Abstract
The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement [...] Read more.
The dynamic and evolving nature of mobile networks necessitates a proactive approach to security, one that goes beyond traditional methods and embraces innovative strategies such as anomaly detection and prediction. This study delves into the realm of mobile network security and reliability enhancement through the lens of anomaly detection and prediction, leveraging K-means clustering on call detail records (CDRs). By analyzing CDRs, which encapsulate comprehensive information about call activities, messaging, and data usage, this research aimed to unveil hidden patterns indicative of anomalous behavior within mobile networks and security breaches. We utilized 14 million one-year CDR records. The mobile network used had deployed the latest network generation, 5G, with various sources of network elements. Through a systematic analysis of historical CDR data, this study offers insights into the underlying trends and anomalies prevalent in mobile network traffic. Furthermore, by harnessing the predictive capabilities of the K-means algorithm, the proposed framework facilitates the anticipation of future anomalies based on learned patterns, thereby enhancing proactive security measures. The findings of this research can contribute to the advancement of mobile network security by providing a deeper understanding of anomalous behavior and effective prediction mechanisms. The utilization of K-means clustering on CDR data offers a scalable and efficient approach to anomaly detection, with 96% accuracy, making it well suited for network reliability and security applications in large-scale mobile networks for 5G networks and beyond. Full article
(This article belongs to the Special Issue Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond)
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Review

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18 pages, 961 KiB  
Review
The Advantage of the 5G Network for Enhancing the Internet of Things and the Evolution of the 6G Network
by Georgios Gkagkas, Dimitrios J. Vergados, Angelos Michalas and Michael Dossis
Sensors 2024, 24(8), 2455; https://doi.org/10.3390/s24082455 - 11 Apr 2024
Cited by 1 | Viewed by 1499
Abstract
The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research [...] Read more.
The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network. Full article
(This article belongs to the Special Issue Advanced Technologies in 5G/6G-Enabled IoT Environments and Beyond)
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