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Applications of Internet of Things Networks in 5G and Beyond

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Internet of Things".

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Editors

Department of Information Engineering, School of Engineering, University of Florence, Florence, Italy
Interests: 5G networks; IoT networks; network security; machine-to-machine protocols; routing algorithms; network simulations/emulations; physical layer security; machine learning; graph theory; edge/fog computing
Department of Information Engineering, School of Engineering, University of Florence, Florence, Italy
Interests: 5G and IoT networks; stochastic network calculus; matching theory; chaos theory; machine learning; virtualization; software defined networks; resource allocation; edge/fog computing

Topical Collection Information

Dear Colleagues,

The pervasiveness of Internet of Things technology is expected to be ever-increasing in the next decade, with applications including healthcare, smart manufacturing, transport and logistics, and security, to name but a few. This explosion of devices, with different application scenarios, requires new networking paradigms and is heavily influencing the new communications standards like 5G – where the most relevant differences with respect to the previous generation are, indeed, in the support for IoT-based applications.

Even though 5G can, and will be, an enabling technology, there is still a wide area of research, both theoretical and applicated, that has to be performed. Examples of research topics under active development are spectrum coexistence, resource allocation, remote area connectivity, heterogeneous network integration, network optimization, etc. The research, of course, is not limited to improvements in 5G and intends to pave the way toward future standards like 6G.

The challenges and opportunities are not limited to communication technologies. As a matter of fact, IoT systems will produce a massive amount of data that will require proper and timely processing. In this context, the research will need to tackle issues arising from the privacy and security of user data, big data processing, networking paradigms based on virtualization, and network and resource slicing, among others.

A further set of challenges will be driven by the need for new paradigms to cope with cyber threats, as IoT systems are tightly coupled to the cyber-physical domain, meaning that threats to the cyber domain can potentially have severe outcomes in the physical domain.

Finally, the importance of green networking and computing paradigms are expected to increase in the future, both because IoT devices are typically mobile and because energy consumption reduction is nowadays a priority.

This Topical Collection seeks innovative works on a wide range of research topics, spanning both theoretical and systems research, including results from industry and academic/industrial collaborations, related but not restricted to the following topics:

  • Machine learning/AI applications for 5G networks
  • Distributed and federated machine learning/AI applications to 5G and IoT domains
  • 5G and IoT systems integration
  • Security and privacy aspects of 5G and IoT pervasive networks
  • 5G and IoT heterogeneous networks
  • 5G and IoT use cases and scenarios (e.g., e-health, vehicular networks, transport systems, autonomous driving, logistic management, etc.)
  • Age of information in pervasive networks
  • Network virtualization systems for 5G and IoT networks (e.g., SDN, NFV, virtual machine placement and migration, network slicing, etc.)
  • Service and network planning for IoT and 5G systems
  • Edge networking and computation for 5G/IoT domains
  • Evolution of 5G and IoT toward 6G and beyond networks
  • Full duplex communications in 5G and IoT networks

Prof. Dr. Tommaso Pecorella
Dr. Benedetta Picano
Collection Editors

Manuscript Submission Information

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Keywords

  • Resource allocation
  • Network virtualization
  • Machine learning
  • Network slicing
  • Security and privacy
  • Field trials

Published Papers (6 papers)

2023

Jump to: 2022, 2021

41 pages, 1311 KiB  
Article
Utilization of 5G Technologies in IoT Applications: Current Limitations by Interference and Network Optimization Difficulties—A Review
by Mario Pons, Estuardo Valenzuela, Brandon Rodríguez, Juan Arturo Nolazco-Flores and Carolina Del-Valle-Soto
Sensors 2023, 23(8), 3876; https://doi.org/10.3390/s23083876 - 11 Apr 2023
Cited by 18 | Viewed by 14081
Abstract
5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection [...] Read more.
5G (fifth-generation technology) technologies are becoming more mainstream thanks to great efforts from telecommunication companies, research facilities, and governments. This technology is often associated with the Internet of Things to improve the quality of life for citizens by automating and gathering data recollection processes. This paper presents the 5G and IoT technologies, explaining common architectures, typical IoT implementations, and recurring problems. This work also presents a detailed and explained overview of interference in general wireless applications, interference unique to 5G and IoT, and possible optimization techniques to overcome these challenges. This manuscript highlights the importance of addressing interference and optimizing network performance in 5G networks to ensure reliable and efficient connectivity for IoT devices, which is essential for adequately functioning business processes. This insight can be helpful for businesses that rely on these technologies to improve their productivity, reduce downtime, and enhance customer satisfaction. We also highlight the potential of the convergence of networks and services in increasing the availability and speed of access to the internet, enabling a range of new and innovative applications and services. Full article
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17 pages, 6026 KiB  
Article
RF-Alphabet: Cross Domain Alphabet Recognition System Based on RFID Differential Threshold Similarity Calculation Model
by Yajun Zhang, Yan Yang, Zijian Li, Zhixiong Yang, Xu Liu and Bo Yuan
Sensors 2023, 23(2), 920; https://doi.org/10.3390/s23020920 - 13 Jan 2023
Viewed by 1684
Abstract
Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a [...] Read more.
Gesture recognition can help people with a speech impairment to communicate and promote the development of Human-Computer Interaction (HCI) technology. With the development of wireless technology, passive gesture recognition based on RFID has become a research hotspot. In this paper, we propose a low-cost, non-invasive and scalable gesture recognition technology, and successfully implement the RF-alphabet, a gesture recognition system for complex, fine-grained, domain-independent 26 English letters; the RF-alphabet has three major advantages: first, this paper achieves complete capture of complex, fine-grained gesture data by designing a dual-tag, dual-antenna layout. Secondly, to overcome the disadvantages of the large training sets and long training times of traditional deep learning. We design and combine the Difference threshold similarity calculation prediction model to extract digital signal features to achieve real-time feature analysis of gesture signals. Finally, the RF alphabet solves the problem of confusing the signal characteristics of letters. Confused letters are distinguished by comparing the phase values of feature points. The RF-alphabet ends up with an average accuracy of 90.28% and 89.7% in different domains for new users and new environments, respectively, by performing feature analysis on similar signals. The real-time, robustness, and scalability of the RF-alphabet are proven. Full article
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2022

Jump to: 2023, 2021

12 pages, 4871 KiB  
Article
A Comprehensive Evaluation of Generating a Mobile Traffic Data Scheme without a Coarse-Grained Process Using CSR-GAN
by Tomoki Tokunaga and Kimihiro Mizutani
Sensors 2022, 22(5), 1930; https://doi.org/10.3390/s22051930 - 01 Mar 2022
Viewed by 1609
Abstract
Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve [...] Read more.
Large-scale mobile traffic data analysis is important for efficiently planning mobile base station deployment plans and public transportation plans. However, the storage costs of preserving mobile traffic data are becoming much higher as traffic increases enormously population density of target areas. To solve this problem, schemes to generate a large amount of mobile traffic data have been proposed. In the state-of-the-art of the schemes, generative adversarial networks (GANs) are used to transform a large amount of traffic data into a coarse-grained representation and generate the original traffic data from the coarse-grained data. However, the scheme still involves a storage cost, since the coarse-grained data must be preserved in order to generate the original traffic data. In this paper, we propose a scheme to generate the mobile traffic data by using conditional-super-resolution GAN (CSR-GAN) without requiring a coarse-grained process. Through experiments using two real traffic data, we assessed the accuracy and the amount of storage data needed. The results show that the proposed scheme, CSR-GAN, can reduce the storage cost by up to 45% compared to the traditional scheme, and can generate the original mobile traffic data with 94% accuracy. We also conducted experiments by changing the architecture of CSR-GAN, and the results show an optimal relationship between the amount of traffic data and the model size. Full article
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21 pages, 816 KiB  
Article
Mobility-Aware Proactive Edge Caching Optimization Scheme in Information-Centric IoV Networks
by Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie and Ermanno Pietrosemoli
Sensors 2022, 22(4), 1387; https://doi.org/10.3390/s22041387 - 11 Feb 2022
Cited by 18 | Viewed by 2516
Abstract
Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core [...] Read more.
Edge caching is a promising approach to alleviate the burden on the backhaul of network links. It has a significant role in the Internet of Vehicle (IoV) networks performance by providing cached data at the edge and reduce the burden of the core network caused by the number of participating vehicles and data volume. However, due to the limited computing and storage capabilities of edge devices, it is hard to guarantee that all contents are cached and every requirement of the device are satisfied for all users. In this paper, we design an Information-Centric Network (ICN) with mobility-aware proactive caching scheme to provide delay-sensitive services on IoV networks. The real-time status and interaction of vehicles with other vehicles and Roadside Units (RSU) is modeled using a Markov process. Mobility aware proactive edge caching decision that maximize network performance while minimizing transmission delay is applied. Our numerical simulation results show that the proposed scheme outperforms related caching schemes in terms of latency by 20–25% in terms of latency and by 15–23% in cache hits. Full article
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2021

Jump to: 2023, 2022

26 pages, 2622 KiB  
Review
Projections of IoT Applications in Colombia Using 5G Wireless Networks
by Alexis Barrios-Ulloa, Dora Cama-Pinto, Johan Mardini-Bovea, Jorge Díaz-Martínez and Alejandro Cama-Pinto
Sensors 2021, 21(21), 7167; https://doi.org/10.3390/s21217167 - 28 Oct 2021
Cited by 7 | Viewed by 3811
Abstract
Wireless technologies are increasingly relevant in different activities and lines of the economy, as well as in the daily life of people and companies. The advent of fifth generation networks (5G) implies a promising synergy with the Internet of Things (IoT), allowing for [...] Read more.
Wireless technologies are increasingly relevant in different activities and lines of the economy, as well as in the daily life of people and companies. The advent of fifth generation networks (5G) implies a promising synergy with the Internet of Things (IoT), allowing for more automations in production processes and an increase in the efficiency of information transmission, managing to improve the efficiency in decision-making through tools such as big data and artificial intelligence. This article presents a description of the 5G implementation process in Colombia, as well as a revision of opportunities when combining with IoT in featured sectors of the departmental development plans, such as agriculture, tourism, health, the environment, and industry. Results shows that the startup of 5G in Colombia has been a slow process, but there are comparisons with similar procedures in other developed countries. Additionally, we present examples of 5G and IoT applications which can be promoted in Colombia, aimed at improving the quality of life of their habitants and promoting economic development. Full article
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18 pages, 1682 KiB  
Article
Enhanced Security Authentication Based on Convolutional-LSTM Networks
by Xiaoying Qiu, Xuan Sun and Monson Hayes
Sensors 2021, 21(16), 5379; https://doi.org/10.3390/s21165379 - 09 Aug 2021
Cited by 3 | Viewed by 1936
Abstract
The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To [...] Read more.
The performance of classical security authentication models can be severely affected by imperfect channel estimation as well as time-varying communication links. The commonly used approach of statistical decisions for the physical layer authenticator faces significant challenges in a dynamically changing, non-stationary environment. To address this problem, this paper introduces a deep learning-based authentication approach to learn and track the variations of channel characteristics, and thus improving the adaptability and convergence of the physical layer authentication. Specifically, an intelligent detection framework based on a Convolutional-Long Short-Term Memory (Convolutional-LSTM) network is designed to deal with channel differences without knowing the statistical properties of the channel. Both the robustness and the detection performance of the learning authentication scheme are analyzed, and extensive simulations and experiments show that the detection accuracy in time-varying environments is significantly improved. Full article
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