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Applied Sciences
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Edge Intelligence for beyond 5G Networks
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Edge Intelligence for beyond 5G Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 10637

Special Issue Editors

Dr. Mamta Agiwal

E-Mail Website
Guest Editor
Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
Interests: 5G; B5G; random access; IoT; DRX; paging
Dr. Maheshwari Mukesh Kumar

E-Mail Website
Guest Editor
Associate Professor, Department of Electrical Engineering, Bahria University Karachi Campus, Karachi, Pakistan
Interests: 5G; B5G; random access; IoT; DRX; paging; AI

Special Issue Information

Dear Colleagues,

With the 5G research already maturing towards global standardization, the focus is now shifting towards Beyond-5G (B5G) developments. The capabilities of B5G wireless communication are expected to be much higher, and are mostly driven by millions of connected devices and intensive services and applications. The wireless devices (smart terminals, IoT devices, sensors, video cameras, etc.) that generate several hundred observations and several terabytes of data are located at the edge of the network. Edge intelligence (EI), powered by artificial intelligence (AI) techniques (machine learning, deep neural networks, etc.), offers to provide powerful computational processing,  massive data acquisition, and edge-caching capabilities at the proximity to the end users. B5G networks when empowered by AI systems at the edge networks can enable better insight into the dynamics of the operating environment, which would result in an effective management of resources. AI-empowered efficient resource scheduling strategies in turn can support the ultra-high reliability and ultra-low latency requirements of several novel applications of B5G networks, such as AR/VR services, self-driving cars, intelligent transport, Industry 4.0, and extensive surveillance. Edge intelligence for B5G networks has the potential to drive future data-intensive applications and services.

This new paradigm of edge-intelligence-empowered B5G presents several research opportunities.  In this Special Issue we are pleased to invite topics that address challenges in edge intelligence as the crucial technology in B5G networks. The Special Issue aims to highlight the latest concepts, implementations, and applications in the field of edge intelligence for B5G networks. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Frameworks and models for edge-intelligence-empowered B5G;
  • AI empowered edge-cloud architecture for B5G;
  • Concepts and protocols for edge intelligence empowered B5G;
  • Edge computation, communication, caching, and control in B5G;
  • Edge intelligence for computation offloading in B5G;
  • Edge intelligence for caching and storage in B5G;
  • Reinforcement learning approaches for edge-intelligent B5G;
  • Networking between edge nodes for resource scheduling;
  • Resource optimization for edge-intelligence-empowered B5G;
  • Network traffic prediction and control in edge-intelligent B5G;
  • Energy efficiency and greenness–performance tradeoff for edge-intelligent B5G;
  • Security and privacy in edge-intelligent B5G;
  • Use cases/applications highlighting the potential of edge intelligence for B5G.

Dr. Mamta Agiwal
Dr. Maheshwari Mukesh Kumar
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. Applied Sciences 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

  • B5G
  • Artificial Intelligence (AI)
  • machine learning
  • Indistry 4.0
  • caching
  • resource optmization
  • energy efficiency

Published Papers (3 papers)

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Research

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17 pages, 1328 KiB  
Article
A Heuristic Fuzzy Based 5G Network Orchestration Framework for Dynamic Virtual Network Embedding
by Srinivasan Thiruvenkadam, Venkatapathy Sujitha, Han-Gue Jo and In-Ho Ra
Appl. Sci. 2022, 12(14), 6942; https://doi.org/10.3390/app12146942 - 08 Jul 2022
Cited by 3 | Viewed by 1569
Abstract
Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to [...] Read more.
Network slicing has become an unavoidable requirement for allocating 5G mobile network resources when sharing resources among devices that have varying needs. As a result, the virtual network slices get resources from the shared physical infrastructure that matches their needs. In order to maximize the use of shared resources, it is critical to provide an efficient virtual network embedding strategy for mapping each user’s requests to a physical infrastructure. Virtual network embedding primarily deals with the two most important network parameters—node mapping and link mapping. This paper proposes the heuristic fuzzy algorithm for node mapping and Dijikstra’s algorithm for link mapping. The proposed fuzzy based multi-criteria decision making technique uses membership functions for node parameters to prepare node mapping. By determining the shortest path, Dijikstra’s algorithm is used to provide link mapping. The proposed strategy is tested under dynamic physical infrastructure conditions for validation. The average acceptance ratio, costrevenue ratio, and average utilization of node capacity and link bandwidth are used to evaluate the performance of the proposed strategy. In addition, the obtained results are compared to the literature to show that the proposed strategy is effective. Full article
(This article belongs to the Special Issue Edge Intelligence for beyond 5G Networks)
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11 pages, 4456 KiB  
Article
Analysis of FBMC Waveform for 5G Network Based Smart Hospitals
by Balamurali Ramakrishnan, Arun Kumar, Sumit Chakravarty, Mehedi Masud and Mohammed Baz
Appl. Sci. 2021, 11(19), 8895; https://doi.org/10.3390/app11198895 - 24 Sep 2021
Cited by 18 | Viewed by 2058
Abstract
Nowadays, many prevalent frameworks for medical care have been projected, studied, and implemented. The load and challenges of traditional hospitals are increasing daily, leading to inefficient service in the health system. Smart hospitals based on advanced techniques play a crucial part in advancing [...] Read more.
Nowadays, many prevalent frameworks for medical care have been projected, studied, and implemented. The load and challenges of traditional hospitals are increasing daily, leading to inefficient service in the health system. Smart hospitals based on advanced techniques play a crucial part in advancing the health services of rural people. It spares the time and money involved in travel, and patient medical reports can be shared instantly with the experts regardless of geographical constraints. Currently, the role of technology in hospitals is limited due to various restrictions, such as the obtainability of a high spectrum, low latency, and high-speed network. In this paper, we focused on the implementation of an advanced waveform with high spectral performance. Filer Bank Multi-Carrier (FBMC) is considered a strong contender for the upcoming 5G-centered smart hospitals due to its high data rate, no leakage of the spectrum, and less sensitivity to frequency error. In addition, a comparison of the spectral utilization of orthogonal frequency division multiplexing (OFDM) and FBMC in terms of bit error rate (BER), peak power (PP), power spectral density (PSD), noise-PSD, capacity and magnitude, and phase response is illustrated. Numerical results show that the FBMC achieved a throughput gain of 1 dB and its spectral performance is better than the OFDM; hence, it is a better choice for the proposed application compared to the current standard OFDM. Full article
(This article belongs to the Special Issue Edge Intelligence for beyond 5G Networks)
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Review

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22 pages, 917 KiB  
Review
Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems
by Afra Domeke, Bruno Cimoli and Idelfonso Tafur Monroy
Appl. Sci. 2022, 12(13), 6617; https://doi.org/10.3390/app12136617 - 29 Jun 2022
Cited by 11 | Viewed by 4653
Abstract
Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and [...] Read more.
Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed. Full article
(This article belongs to the Special Issue Edge Intelligence for beyond 5G Networks)
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