Data-Driven Intelligence in Wireless Networks: Concepts, Solutions, and Applications

267 Pages·2023·19.04 MB·English

by  Muhammad Khalil Afzal, Muhammad Ateeq, Sung Won Kim

Data-Driven Intelligence
in Wireless Networks
This book highlights the importance of data-driven techniques to solve wireless
communication problems. It presents a number of problems (e.g., related to perfor-
mance, security, and social networking), and provides solutions using various data-
driven techniques, including machine learning, deep learning, federated learning,
and artificial intelligence.
This book details wireless communication problems that can be solved by data-
driven solutions. It presents a generalized approach toward solving problems using
specific data-driven techniques. The book also develops a taxonomy of problems
according to the type of solution presented and includes several case studies that
examine data-driven solutions for issues such as quality of service (QoS) in hetero-
geneous wireless networks, 5G/6G networks, and security in wireless networks.
The target audience of this book includes professionals, researchers, professors,
and students working in the field of networking, communications, machine learning,
and related fields.

Data-Driven Intelligence
in Wireless Networks
Concepts, Solutions, and Applications
Edited by
Muhammad Khalil Afzal,
Muhammad Ateeq, and
Sung Won Kim
Designed cover image: iStock – Smart City and Internet of Things, Various Communication Devices Stock
Photo
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First edition published 2023
by CRC Press
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© 2023 selection and editorial matter, Muhammad Khalil Afzal, Muhammad Ateeq, and Sung Won Kim;
individual chapters, the contributors
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Library of Congress Cataloging‑in‑Publication Data
Names: Afzal, Muhammad Khalil, editor. | Ateeq, Muhammad, editor. | Kim, Sung Won, editor.
Title: Data-driven intelligence in wireless networks : concepts, solutions, and applications /
edited by Muhammad Khalil Afzal, Muhammad Ateeq, and Sung Won Kim.
Description: First edition. | Boca Raton : CRC Press, 2023. | Includes bibliographical references. |
Identifiers: LCCN 2022041502 (print) | LCCN 2022041503 (ebook) | ISBN 9781032100371
(hardback) | ISBN 9781032107738 (paperback) | ISBN 9781003216971 (ebook)
Subjects: LCSH: Wireless communication systems--Design and construction--Data
processing. | Decision making. | Big data. | Artificial intelligence.
Classification: LCC TK5103.2 .D3157 2023 (print) | LCC TK5103.2 (ebook) | DDC
621.3820285/63--dc23/eng/20221115
LC record available at https://lccn.loc.gov/2022041502
LC ebook record available at https://lccn.loc.gov/2022041503
ISBN: 978-1-032-10037-1 (hbk)
ISBN: 978-1-032-10773-8 (pbk)
ISBN: 978-1-003-21697-1 (ebk)
DOI: 10.1201/9781003216971
Typeset in Times
by KnowledgeWorks Global Ltd.
Contents
Preface .....................................................................................................................vii
Acknowledgments ....................................................................................................ix
Editor Biographies ....................................................................................................xi
Contributors ...........................................................................................................xiii
PART I Data-Driven Wireless Networks:
Design and Applications
Chapter 1 Data-Driven Wireless Networks: A Perspective ..................................3
Muhammad Ateeq and Muhammad Khalil Afzal
Chapter 2 A Collaborative Data-Driven Intelligence for Future
Wireless Networks .............................................................................11
Rashid Ali and Hyung Seok Kim
Chapter 3 Federated Learning Technique in Enabling Data-Driven
Design for Wireless Communication .................................................23
Ahmad Arsalan, Tariq Umer, and Rana Asif Rehman
Chapter 4 Application of Wireless Network Data Driven using Edge
Computing and Deep Learning in Intelligent Transportation ...........55
Zhihan Lv
Chapter 5 Data-Driven Agriculture and Role of AI in Smart Farming ..............79
El Mehdi Ouafiq, Rachid Saadane, and Abdellah Chehri
PART II Data-Driven Techniques and
Security Issues in Wireless Networks
Chapter 6 Data-Driven Techniques and Security Issues in
Wireless Networks ...........................................................................107
Mamoon M. Saeed, Elmustafa Sayed Ali, and Rashid A. Saeed
v
vi Contents
Chapter 7 Data-Driven Techniques for Intrusion Detection in
Wireless Networks ...........................................................................155
Lina Elmoiz Alatabani, Elmustafa Sayed Ali,
and Rashid A. Saeed
PART III Advanced Topics in Data-Driven
Intelligence for Wireless Networks
Chapter 8 Policy-based Data Analytic for Software Defined
Wireless Sensor Networks ................................................................189
Rashid Amin, Mudassar Hussain, Saima Bibi,
and Ayesha Sabir
Chapter 9 Data-Driven Coexistence in Next-Generation
Heterogeneous Cellular Networks ....................................................213
Salman Saadat
Chapter 10 Programming Languages, Tools, and Techniques ...........................237
Muhammad Ateeq and Muhammad Khalil Afzal
Index ......................................................................................................................249
Preface
An evolving concept called data-driven intelligence is a model for a new viewpoint
on gathering vision from a vast pool of data. Data-driven techniques put robust
importance on the large dataset to solve a specific problem. There are more than
370 million Internet users worldwide. The number of unique mobile users is almost
5 billion, with the total number of mobile connections exceeding 8 billion. This
indicates that wireless communication is a prevalent field. Wireless networks can
show random interactions between algorithms from multiple protocol layers, inter-
actions between multiple devices, and hardware-specific effects. Different wireless
technologies including mobile cellular, fixed line, Wi-Fi, and others are widely used
for diverse communication purposes. It is estimated that the majority of users access
the Internet via mobile devices. Internet traffic is dominated by multimedia content,
and its proportion is ever increasing. This increases the quality of service/quality
of experience requirements. Optimizing multiple, often conflicting goals according
to different application requirements is of fundamental importance in this context.
With the spreading of the Internet and broadening of its capacity, data is available
in abundance. Moreover, more wireless data can be collected from wireless test-
bed facilities. Many fields benefit from data to optimize decisions. Many data sets
related to the performance and security of different wireless networks, i.e., wireless
sensor networks and the Internet of Things, are publicly available. There has been
a growing trend to benefit from data-driven techniques like machine learning to
improve decision-making, management, performance, and security issues in wire-
less networks.
This edited book aims to deliver knowledge that can highlight the importance of
data-driven techniques to solve wireless communication problems. As a next step,
the solution to those problems, using various data-driven techniques, primarily from
machine learning (supervised, unsupervised, and reinforcement), deep learning, fed-
erated learning, and artificial intelligence, are presented.
The chapters of this book are authored by several international researchers. This
book is composed of 10 chapters that can be read based on the interest of the reader
without having to read the entire book. These chapters were carefully selected after a
rigorous review. This book is an excellent reference for computer scientists, research-
ers, and developers, who wish to contribute to the domain of data-driven intelligence
in the field of wireless networks and related areas. We tried to include sufficient
details and provide the necessary background information in each chapter to help
the readers easily understand the content. We hope readers will enjoy this book and
hope it will help graduate students who are interested in working on the domain of
data-driven intelligence in their research.
vii

Acknowledgments
We would like to express our gratitude to everyone who participated in this book and
made this book a reality. We would especially like to acknowledge the hard work of
the authors.
We would also like to acknowledge the efforts of the reviewers, whose valuable
comments enabled us to select these chapters out of the many we received and whose
help improved the overall quality of the chapters presented in this book. Special
thanks to Dr. Wazir Zada Khan, University of Wah, Pakistan; Dr. Yousaf Bin Zikria,
Yeungnam University, South Korea; Muhammad Islam, Swinburne University
of Technology, Australia; Dr. Zulqarnain, Yeungnam University, South Korea;
Dr. Salman Saadat, Military College, Oman; Dr. Zeeshan Kaleem, COMSATS
University Islamabad, Wah Campus; Dr. Latif Ullah Khan, Kyung Hee University,
South Korea; Naqqash Dilshad, Sejong University, South Korea; and Ahsan Saleem,
Concordia University, Canada.
Lastly, we are very grateful to the editorial team at Taylor & Francis for their
support throughout the publishing of this book. Special Thanks to our editor Marc
Gutierrez, for his great support and encouragement. We would also like to thank
Sarahjayne Smith from Taylor & Francis, and Deepanshu from KnowledgeWorks
Global Ltd., for managing the production process of this book.
Dr. Muhammad Khalil Afzal
Dr. Muhammad Ateeq
Dr. Sung Won Kim
ix