AI-Enabled 6G Networks and Applications E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 175

Veröffentlichungsjahr: 2022

Beschreibung

AI-ENABLED 6G NETWORKS AND APPLICATIONS Provides authoritative guidance on utilizing AI techniques in 6G network design and optimization Written and edited by active researchers, this book covers hypotheses and practical considerations and provides insights into the design of evolutionary AI algorithms for 6G networks, with focus on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields. AI-Enabled 6G Networks and Applications includes a review of AI techniques for 6G Networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks, and communication networks. * Reviews various types of AI techniques such as AI-empowered mobile edge computing, intelligent handover management, and smart spectrum management * Describes how AI techniques manage computation efficiency, algorithm robustness, hardware development, and energy management * Identifies and provides solutions to problems in current 4G/5G networks and emergent 6G architectures * Discusses privacy and security issues in IoT-enabled 6G Networks * Examines the use of machine learning to achieve closed-loop optimization and intelligent wireless communication AI-Enabled 6G Networks and Applications is an essential reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols, suitable for graduate students and researchers in network forensics and optimization, computer science, and engineering.

Leseprobe

AI‐Enabled 6G Networks and Applications

Edited by

Deepak Gupta

Maharaja Agrasen Institute of Technology, India

Dr. Mahmoud Ragab

Associate Professor

Department of Information Technology

Faculty of Computing and Information Technology

King Abdulaziz University, Jeddah, Saudi Arabia

Romany Fouad Mansour

Associate Professor

Department of Mathematics

Faculty of Science

New Valley University, Egypt.

Aditya Khamparia

Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India

Ashish Khanna

Maharaja Agrasen Institute of Technology, India

This edition first published 2023© 2023 John Wiley & Sons Ltd

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Library of Congress Cataloging‐in‐Publication DataNames: Gupta, Deepak, Ph.D., editor. | Ragab, Mahmoud, editor. | Mansour, Romany Fouad, editor. | Khamparia, Aditya, 1988– editor. | Khanna, Ashish (Of Guru Gobind Singh Indraprastha University), editor.Title: AI-enabled 6G networks and applications / edited by Deepak Gupta, Mahmoud Ragab, Romany Fouad Mansour, Aditya Khamparia, Ashish Khanna.Description: Hoboken, NJ : Wiley, 2023.Identifiers: LCCN 2022023670 (print) | LCCN 2022023671 (ebook) | ISBN 9781119812647 (cloth) | ISBN 9781119812708 (adobe pdf) | ISBN 9781119812715 (epub)Subjects: LCSH: Mobile communication systems. | Artificial intelligence.Classification: LCC TK5103.2 .A434 2023 (print) | LCC TK5103.2 (ebook) | DDC 621.384–dc23/eng/20220812LC record available at https://lccn.loc.gov/2022023670LC ebook record available at https://lccn.loc.gov/2022023671

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List of Contributors

Elsaid M. AbdelrahimDepartment of Mathematics Computer Science DivisionFaculty of Science Tanta University Tanta, Egypt

Emad A.‐B. Abdel‐SalamDepartment of Mathematics Faculty of Science New Valley University El‐Kharga, Egypt

Maha M. AlthobaitiDepartment of Computer ScienceCollege of Computing and Information Technology Taif University Taif, Saudi Arabia

Natasha MaderaMechatronics Engineering Program, Universidad Simón Bolívar, Barranquilla, Colombia

Victor Hugo C. de AlbuquerqueDepartment of Teleinformatics Engineering Federal University of Ceará Fortaleza, Brazil

Marcello Carvalho dos ReisGraduate Program in Telecommunication EngineeringFederal Institute of EducationScience and Technology of CearáFortaleza, BrazilandMeteora, Fortaleza, Brazil

Adnen El AmraouiUniv. Artois, UR 3926 Laboratoire de Génie Informatiqueet d’Automatique de l’Artois (LGI2A), F-62400, Béthune, France

José Escorcia‐GutierrezResearch Center - CIENS, Escuela Naval de Suboficiales ARC Barranquilla, Barranquilla, ColombiaandBiomedical Engineering Program, Corporación Universitaria Reformada, Barranquilla, Colombia

M. IlayarajaSchool of Computing Kalasalingam Academy of Research and EducationKrishnankoil Tamilnadu, India

Ayman M. MahmoudDepartment of Mathematics Faculty of ScienceNew Valley UniversityEl‐Kharga, Egypt

Vinita MalikGraduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza, BrazilandCentral Library Central University of Haryana, Mahendragarh, Haryana, India

Romany F. MansourDepartment of Mathematics Faculty of Science New Valley University El‐Kharga, Egypt

Kanagaraj NarayanasamyDepartment of Computer Applications Karpagam Academy of Higher Education (Deemed to be University) Coimbatore, Tamilnadu, India

R. Pandi SelvamPG Department of Computer ScienceVidhyaa Giri College of Arts and Science, PuduvayalKaraikudi, Tamilnadu, India

Pooja SinghGraduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Ceará, Fortaleza, BrazilandDepartment of Computer Science and Engineering, GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

Carlos SotoMechanical Engineering ProgramUniversidad Autónoma del CaribeBarranquilla, Colombia

Melitsa Torres‐TorresResearch Group IET‐UACUniversidad Autónoma del CaribeBarranquilla, Colombia

Preface

With the rapid development of diversified applications (e.g. virtual and augmented reality, telematics, remote surgery, and holographic projection) as well as construction of smart terminals and infrastructures, current networks (e.g. 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Keeping this in consideration both industry and academia have been engaged in research related to 6G networks. Recently, artificial intelligence (AI) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this book presents an AI‐enabled intelligent architecture, hardware, computing techniques, and related diversified applications for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment, and intelligent service provisioning with evolutionary computing techniques.

This book includes review of AI techniques for 6G networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI‐empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book also includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks, and communication networks. It is intended as a reference guide for advanced hybrid computational intelligence methods for 6G supportive networks and protocols for graduate students and researchers in network forensics and optimization, computer science, and engineering.

Objective of the Book

The key features of the book are to highlight the encountered problems in 4G and 5G networks and provide suitable solutions to counter them. It also throws light on how to employ AI techniques to efficiently and effectively optimize the network performance and enable intelligent mobility. It enables usage of strong learning ability to achieve network intelligentization, closed‐loop optimization, and intelligent wireless communication for 6G networks. The book also examines privacy issues and challenges related to data‐intensive technologies in IoT enabled 6G networks. It also highlights important future research directions and potential solutions for AI‐enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development, and energy management.

Organization of the Book

This book is organized in eight chapters with the following brief description:

Chapter 1: Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks

In proposed work author introduces a metaheuristic moth flame optimization algorithm for energy efficient clustering (MMFO‐EEC) technique for 6G enabled unmanned aerial vehicle (UAV) networks. The major intention of the MMFO‐EEC technique is the proficient election of cluster heads (CHs) and cluster organization in 6G enabled UAV networks. The presented MMFO‐EEC technique mainly employs the MFO algorithm to effectually pick out the appropriate UAVs as CHs in the network.

Chapter 2: A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks

This chapter develops a novel data offloading with deep learning enabled cyberattack detection (DADL‐CAD) model for edge computing in 6G networks. The proposed DADL‐CAD technique primarily designs recurrent neural network (RNN) model for traffic flow forecasting in the edge computing enabled 6G networks. The performance validation of the DADL‐CAD technique is examined under various aspects, and the comparative study reported the supremacy of the DADL‐CAD technique over the recent approaches.

Chapter 3: Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks

In this chapter author develops a novel Henry gas solubility optimization with deep learning enabled traffic flow forecasting (HSGODL‐TFF) technique for 6G enabled vehicular networks. The presented HSGODL‐TFF technique primarily intends to forecast the level of traffic in the 6G enabled VANET. HSGO algorithm can be applied for optimally modifying the hyperparameters (such as learning rate, epoch count, and batch size) of the DBN model thereby improving the forecasting performance. The experimental validation of the HSGODL‐TFF model is performed on test data, and the results are inspected under several aspects.

Chapter 4: Crow Search Algorithm Based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment

The proposed chapter introduces a novel crow search algorithm based vector quantization approach for image compression in 6G enabled IIoT environment, called CSAVQ‐ICIIoT model. The proposed CSAVQ‐ICIIoT model intends to accomplish effectual image compression by optimizing codebook construction process in 6G enabled IIoT platform. The CSAVQ‐ICIIoT technique includes Linde–Buzo–Gray (LBG) with vector quantization (VQ) technique for image compression.

Chapter 5: Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks

This chapter presents an artificial intelligence enabled dingo optimizer for energy management (AIDO‐EM) in 6G networks. The presented AIDO‐EM technique involves the major goal of minimizing the energy utilization and maximizing the lifetime of the 6G enabled IoT devices. For accomplishing this, a new dingo optimization algorithm (DOA) is applied for cluster enabled routing to achieve effective data distribution among the devices and choose effective gateway heads (GWH).

Chapter 6: Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks

In this chapter an adaptive whale optimization with deep learning enabled RefineDet network (AWO‐DLRDN) for vision assistance on 6G networks is discussed. The major intention of the AWO‐DLRDN technique is to determine the nearby objects and their approximate distance to the visually impaired people. The proposed AWO‐DLRDN technique primarily undergoes data augmentation and image annotation process as a preprocessing step. The performance validation of the AWO‐DLRDN technique is experimented with using benchmark dataset, and the comparison study reported the enhancements of the AWO‐DLRDN technique over the other techniques.

Chapter 7: Efficient Deer Hunting Optimization Algorithm Based Spectrum Sensing Approach for 6G Communication Networks