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5G/6G Networks for the Internet of Things: Communication Technologies and...
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5G/6G Networks for the Internet of Things: Communication Technologies and Challenges

A topical collection in Future Internet (ISSN 1999-5903). This collection belongs to the section "Internet of Things".

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Editor

Dr. Sachin Sharma

E-Mail Website
Collection Editor
School of Electrical and Electronic Engineering, Technological University Dublin, D07 EWV4 Dublin, Ireland
Interests: telecommunications; Software-Defined Networks (SDNs); Network Function Virtualization; cloud computing; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

A 5G network can achieve 10 times the data rate and connect 1000 times the number of devices as compared to its predecessors, i.e., 4G networks. Additionally, 6G will enhance the capabilities of 5G to a much higher level, allowing millions of smart devices and applications to seamlessly exchange data with low latency and high data rates. Moreover, with only a few milli- or microseconds of delay, 5G and 6G may bring killer applications such as autonomous vehicles, remote surgeries, drone delivery services, and virtual and augmented reality, among others. As 5G/6G technology becomes more prevalent, the Internet of Things (IoT) is expected to gain more momentum. Furthermore, several 5G/6G testbeds have been built to facilitate research in the area of IoT networks enabled by 5G/6G.

This Topical Collection covers a broad range of topics related to 5G and 6G, including, but not limited to, the following:

1) Software-Defined Networking for 5G/6G-enabled Internet of Things;

2) Virtualization Techniques including Network Function Virtualization for 5G/6G-enabled networks for Internet of Things;

3) Machine Learning and Deep Learning Solutions for 5G/6G-enabled Internet of Things;

4) Cloud Computing Solutions for 5G/6G-enabled Internet of Things

5) Blockchain solutions for the Internet of Things networks enabled with 5G/6G;

6) Quantum communication and Quantum machine learning for the Internet of Things networks enabled with 5G/6G;

7) Innovative Applications including network security, smart cities, and e-healthcare applications for the Internet of Things networks enabled with 5G/6G;

8) Energy-Efficient Solutions for the Internet of Things networks enabled with 5G/6G;

9) Experimental Research on testbeds for Internet of Things networks enabled with 5G/6G;

10) Challenges and future research for 5G/6G and beyond 6G technologies.

Dr. Sachin Sharma
Collection Editor

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 collection 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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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
  • SDN
  • NFV
  • blockchain
  • quantum machine learning
  • experimental (or testbed) research

Published Papers (8 papers)

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2025

Jump to: 2024, 2023, 2022

22 pages, 5550 KiB  
Article
Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning
by Abimbola Efunogbon, Enjie Liu, Renxie Qiu and Taiwo Efunogbon
Future Internet 2025, 17(2), 69; https://doi.org/10.3390/fi17020069 - 6 Feb 2025
Cited by 1 | Viewed by 904
Abstract
This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. [...] Read more.
This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions. Full article
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2024

Jump to: 2025, 2023, 2022

26 pages, 1452 KiB  
Article
Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks
by Md Kamruzzaman, Nurul I. Sarkar and Jairo Gutierrez
Future Internet 2024, 16(11), 408; https://doi.org/10.3390/fi16110408 - 6 Nov 2024
Cited by 1 | Viewed by 2399
Abstract
Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network [...] Read more.
Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones. Full article
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17 pages, 329 KiB  
Article
Traffic Classification in Software-Defined Networking Using Genetic Programming Tools
by Spiridoula V. Margariti, Ioannis G. Tsoulos, Evangelia Kiousi and Eleftherios Stergiou
Future Internet 2024, 16(9), 338; https://doi.org/10.3390/fi16090338 - 19 Sep 2024
Cited by 3 | Viewed by 1695
Abstract
The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, [...] Read more.
The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features. Full article
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24 pages, 8449 KiB  
Article
A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
by Ayman Khalil and Besma Zeddini
Future Internet 2024, 16(2), 56; https://doi.org/10.3390/fi16020056 - 8 Feb 2024
Cited by 5 | Viewed by 2098
Abstract
The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, [...] Read more.
The intersection of cybersecurity and opportunistic networks has ushered in a new era of innovation in the realm of wireless communications. In an increasingly interconnected world, where seamless data exchange is pivotal for both individual users and organizations, the need for efficient, reliable, and sustainable networking solutions has never been more pressing. Opportunistic networks, characterized by intermittent connectivity and dynamic network conditions, present unique challenges that necessitate innovative approaches for optimal performance and sustainability. This paper introduces a groundbreaking paradigm that integrates the principles of cybersecurity with opportunistic networks. At its core, this study presents a novel routing protocol meticulously designed to significantly outperform existing solutions concerning key metrics such as delivery probability, overhead ratio, and communication delay. Leveraging cybersecurity’s inherent strengths, our protocol not only fortifies the network’s security posture but also provides a foundation for enhancing efficiency and sustainability in opportunistic networks. The overarching goal of this paper is to address the inherent limitations of conventional opportunistic network protocols. By proposing an innovative routing protocol, we aim to optimize data delivery, minimize overhead, and reduce communication latency. These objectives are crucial for ensuring seamless and timely information exchange, especially in scenarios where traditional networking infrastructures fall short. By large-scale simulations, the new model proves its effectiveness in the different scenarios, especially in terms of message delivery probability, while ensuring reasonable overhead and latency. Full article
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2023

Jump to: 2025, 2024, 2022

14 pages, 2477 KiB  
Article
Blockchain-Enabled NextGen Service Architecture for Mobile Internet Offload
by Raman Singh, Zeeshan Pervez and Hitesh Tewari
Future Internet 2023, 15(5), 173; https://doi.org/10.3390/fi15050173 - 5 May 2023
Cited by 1 | Viewed by 2468
Abstract
The amalgamation of heterogeneous generations of mobile cellular networks around the globe has resulted in diverse data speed experiences for end users. At present, there are no defined mechanisms in place for subscribers of a mobile network operator (MNO) to use the services [...] Read more.
The amalgamation of heterogeneous generations of mobile cellular networks around the globe has resulted in diverse data speed experiences for end users. At present, there are no defined mechanisms in place for subscribers of a mobile network operator (MNO) to use the services of third-party WiFi providers. MNOs also have no standardized procedures to securely interact with each other, and allow their subscribers to use third-party services on a pay-as-you-go basis. This paper proposes a blockchain-enabled offloading framework that allows a subscriber of a mobile operator to temporarily use another MNO or WiFi provider’s higher-speed network. A smart contract is employed to allow diverse entities, such as MNOs, brokers and WiFi providers, to automatically execute mutual agreements, to enable the utilization of third-party infrastructure in a secure and controlled manner. The proposed framework is tested using Ethereum’s testnet on the Goerli network using Alchemy and Hardhat. The analysis of the results obtained shows that the proposed technique helps mobile operators to offer improved user experience in the form of average speed and latency. The experiments show that the average time taken to deliver a 500 MB file is reduced from 10.23 s to 0.91 s for the global average scenario, from 6.09 s to 0.50 s for 5G, from 13.50 s to 0.50 s for 4G-LTE, from 41.11 s to 0.49 s for 4G, and from 339.11 s to 0.49 s for the 3G scenario. The results also show that, with WiFi offloading, users from all cellular generations can enjoy a similar quality of services, because delivery time ranges from 0.49 s to 0.91 s for offloaded experiments whereas for the non-offloaded scenario it ranges from 6.09 s to 339.11 s. Full article
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18 pages, 4321 KiB  
Article
Study of Coded ALOHA with Multi-User Detection under Heavy-Tailed and Correlated Arrivals
by María E. Sousa-Vieira and Manuel Fernández-Veiga
Future Internet 2023, 15(4), 132; https://doi.org/10.3390/fi15040132 - 30 Mar 2023
Cited by 2 | Viewed by 1899
Abstract
In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to [...] Read more.
In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance. Full article
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21 pages, 4165 KiB  
Article
An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
by Eduardo Ferreira, Pedro Sebastião, Francisco Cercas, Carlos Sá Costa and Américo Correia
Future Internet 2023, 15(2), 58; https://doi.org/10.3390/fi15020058 - 31 Jan 2023
Cited by 2 | Viewed by 2608
Abstract
Today, the internet is fundamental to social inclusion. There are many people that live in remote areas, and the only way to supply internet services is through the use of microwave terrestrial and satellite systems. Thus, it is important to have efficient tools [...] Read more.
Today, the internet is fundamental to social inclusion. There are many people that live in remote areas, and the only way to supply internet services is through the use of microwave terrestrial and satellite systems. Thus, it is important to have efficient tools to design and optimize these systems. In this paper, a tool with the objective to shorten the time spent in the design process of microwave terrestrial and satellite point-to-point links is presented. This tool can be applied in academia by engineering students, providing an extended analysis of many sections of a link project design, as well as in professional practice by telecommunication engineering departments, presenting a concise step-by-step interactive design process. This tool uses three-dimensional world visualization, with the Cesium Application Programming Interface (API), to display and analyze site-specific characteristics that can disrupt the link’s quality of service (QoS). Using this visualization, two ray-tracing algorithms were developed to analyze signal diffraction and reflection mainly throughout terrestrial links. Using this new algorithm, an innovative process for signal diffraction and reflection calculations was created. Using updated standards provided by the International Telecommunication Union Radiocommunication Sector (ITU-R), the characteristics of the defined simulated links could be predicted, thus providing the user with the metrics of signal quality and system link budget. Full article
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2022

Jump to: 2025, 2024, 2023

14 pages, 950 KiB  
Article
Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
by Kirill Chemrov, Dmitry Bankov, Evgeny Khorov and Andrey Lyakhov
Future Internet 2022, 14(10), 296; https://doi.org/10.3390/fi14100296 - 16 Oct 2022
Cited by 8 | Viewed by 2828
Abstract
Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices [...] Read more.
Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic. Full article
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