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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 8102

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Dr. Marc Ruiz

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CCABA - Advanced Broadband Communications Center, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Interests: network architecture design; autonomic network operation; AI/ML-empowered network control; intent-based networking
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Special Issue Information

Dear Colleagues,

After the success of the first focused and standalone 5G trials, the beyond 5G (B5G) paradigm is becoming the mainstream of academic and industry-driven research in next-generation networks. Future B5G networks must be able to operate with massive small-cell deployments and end-to-end connectivity in support of heterogeneous use cases with very different requirements in terms of bandwidth, latency and reliability.

Cost-effective B5G networks will be achieved by integrating innovative small cells (e.g., LiFi) with other existing 5G technologies, and with different backhaul transport network solutions providing high and adaptive capacity on demand. Thus, transport network solutions at both the optical and packet layer and across different domains should be based on key enablers such as pluggability and programmability. In addition, the control and orchestration of such complex network environments will demand solutions exploring zero-touch and intent-based networking paradigms.

Dr. Marc Ruiz
Guest Editor

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Keywords

e2e connectivity
Innovative small cells
Optical-wireless integration
Open programmable networks
Zero-touch network control
Intent-based networking
AI-empowered network operation

Published Papers (3 papers)

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18 pages, 3653 KiB

Open AccessArticle

Deep Learning-Based Adaptive Compression and Anomaly Detection for Smart B5G Use Cases Operation

by Ahmad El Sayed, Marc Ruiz, Hassan Harb and Luis Velasco

Sensors 2023, 23(2), 1043; https://doi.org/10.3390/s23021043 - 16 Jan 2023

Cited by 3 | Viewed by 1934

Abstract

The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of network resources in the next decades. Among different B5G use cases, the Digital Twin (DT) has been identified as a key high bandwidth-demanding use case. The creation and operation of a DT require the continuous collection of an enormous and widely distributed amount of sensor telemetry data which can overwhelm the transport layer. Therefore, the reduction in such transported telemetry data is an essential objective of smart use case operation. Moreover, deep telemetry data analysis, i.e., anomaly detection, can be executed in a hierarchical way to reduce the processing needed to perform such analysis in a centralized way. In this paper, we propose a smart management system consisting of a hierarchical architecture for telemetry sensor data analysis using deep autoencoders (AEs). The system contains AE-based methods for the adaptive compression of telemetry time series data using pools of AEs (called AAC), as well as for anomaly detection in single (called SS-AD) and multiple (called MS-AGD) sensor streams. Numerical results using experimental telemetry data show compression ratios of up to 64% with reconstruction errors of less than 1%, clearly improving upon the benchmark state-of-the-art methods. In addition, fast and accurate anomaly detection is demonstrated for both single and multiple-sensor scenarios. Finally, a great reduction in transport network capacity resources of 50% and more is obtained by smart use case operation for distributed DT scenarios. Full article

(This article belongs to the Special Issue Towards Next Generation beyond 5G (B5G) Networks)

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21 pages, 2689 KiB

Open AccessArticle

Robust Handover Optimization Technique with Fuzzy Logic Controller for Beyond 5G Mobile Networks

by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah and Abdulraqeb Alhammadi

Sensors 2022, 22(16), 6199; https://doi.org/10.3390/s22166199 - 18 Aug 2022

Cited by 18 | Viewed by 2086

Abstract

Mobility management is an essential process in mobile networks to ensure a high quality of service (QoS) for mobile user equipment (UE) during their movements. In fifth generation (5G) and beyond (B5G) mobile networks, mobility management becomes more critical due to several key factors, such as the use of Millimeter Wave (mmWave) and Terahertz, a higher number of deployed small cells, massive growth of connected devices, the requirements of a higher data rate, and the necessities for ultra-low latency with high reliability. Therefore, providing robust mobility techniques that enable seamless connections through the UE’s mobility has become critical and challenging. One of the crucial handover (HO) techniques is known as mobility robustness optimization (MRO), which mainly aims to adjust HO control parameters (HCPs) (time-to-trigger (TTT) and handover margin (HOM)). Although this function has been introduced in 4G and developed further in 5G, it must be more efficient with future mobile networks due to several key challenges, as previously illustrated. This paper proposes a Robust Handover Optimization Technique with a Fuzzy Logic Controller (RHOT-FLC). The proposed technique aims to automatically configure HCPs by exploiting the information on Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and UE velocity as input parameters for the proposed technique. The technique is validated through various mobility scenarios in B5G networks. Additionally, it is evaluated using a number of major HO performance metrics, such as HO probability (HOP), HO failure (HOF), HO ping-pong (HOPP), HO latency (HOL), and HO interruption time (HIT). The obtained results have also been compared with other competitive algorithms from the literature. The results show that RHOT-FLC has achieved considerably better performance than other techniques. Furthermore, the RHOT-FLC technique obtains up to 95% HOP reduction, 95.8% in HOF, 97% in HOPP, 94.7% in HOL, and 95% in HIT compared to the competitive algorithms. Overall, RHOT-FLC obtained a substantial improvement of up to 95.5% using the considered HO performance metrics. Full article

(This article belongs to the Special Issue Towards Next Generation beyond 5G (B5G) Networks)

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33 pages, 1565 KiB

Open AccessArticle

A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems

by Douglas Chagas da Silva, José Olimpio Rodrigues Batista, Jr., Marco Antonio Firmino de Sousa, Gustavo Marques Mostaço, Claudio de Castro Monteiro, Graça Bressan, Carlos Eduardo Cugnasca and Regina Melo Silveira

Sensors 2022, 22(16), 6066; https://doi.org/10.3390/s22166066 - 13 Aug 2022

Cited by 3 | Viewed by 2730

Abstract

The Network Slice Selection Function (NSSF) in heterogeneous technology environments is a complex problem, which still does not have a fully acceptable solution. Thus, the implementation of new network selection strategies represents an important issue in development, mainly due to the growing demand for applications and scenarios involving 5G and future networks. This work presents an integrated solution for the NSSF problem, called the Network Slice Selection Function Decision-Aid Framework (NSSF DAF), which consists of a distributed solution in which a part is executed on the user’s equipment (for example, smartphones, Unmanned Aerial Vehicles, IoT brokers) functioning as a transparent service, and another at the Edge of the operator or service provider. It requires a low consumption of computing resources from mobile devices and offers complete independence from the network operator. For this purpose, protocols and software tools are used to classify slices, employing the following four multicriteria methods to aid decision making: VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations). The general objective is to verify the similarity among these methods and applications to the slice classification and selection process, considering a specific scenario in the framework. It also uses machine learning through the K-means clustering algorithm, adopting a hybrid solution in the implementation and operation of the NSSF service in multi-domain slicing environments of heterogeneous mobile networks. Testbeds were conducted to validate the proposed framework, mapping the adequate quality of service requirements. The results indicate a real possibility of offering a complete solution to the NSSF problem that can be implemented in Edge, in Core, or even in the 5G Radio Base Station itself, without the incremental computational cost of the end user’s equipment, allowing for an adequate quality of experience. Full article

(This article belongs to the Special Issue Towards Next Generation beyond 5G (B5G) Networks)

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