๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

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

โœ Scribed by Muhammad Khalil Afzal, Muhammad Ateeq, Sung Won Kim


Publisher
CRC Press
Year
2023
Tongue
English
Leaves
267
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


This book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, 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 heterogeneous 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.

โœฆ Table of Contents


Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
Editor Biographies
Contributors
PART I: Data-Driven Wireless Networks: Design and Applications
Chapter 1: Data-Driven Wireless Networks: A Perspective
Chapter 2: A Collaborative Data-Driven Intelligence for Future Wireless Networks
Chapter 3: Federated Learning Technique in Enabling Data-Driven Design for Wireless Communication
Chapter 4: Application of Wireless Network Data Driven using Edge Computing and Deep Learning in Intelligent Transportation
Chapter 5: Data-Driven Agriculture and Role of AI in Smart Farming
PART II: Data-Driven Techniques and Security Issues in Wireless Networks
Chapter 6: Data-Driven Techniques and Security Issues in Wireless Networks
Chapter 7: Data-Driven Techniques for Intrusion Detection in Wireless Networks
PART III: Advanced Topics in Data-Driven Intelligence for Wireless Networks
Chapter 8: Policy-based Data Analytic for Software Defined Wireless Sensor Networks
Chapter 9: Data-Driven Coexistence in Next-Generation Heterogeneous Cellular Networks
Chapter 10: Programming Languages, Tools, and Techniques
Index


๐Ÿ“œ SIMILAR VOLUMES


Intelligent Data-Driven Modelling and Op
โœ B Rajanarayan Prusty (editor), Neeraj Gupta (editor), Kishore Bingi (editor), Ra ๐Ÿ“‚ Library ๐Ÿ“… 2024 ๐Ÿ› CRC Press ๐ŸŒ English

<p><span>This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret t

Applications of computational intelligen
โœ Doloc, Cris ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› Wiley ๐ŸŒ English

"The objective of this book is to introduce the reader to the field of Computational Finance using the framework of Machine Learning as a tool of scientific inquiry. It is an attempt to integrate these two topics: how to use Machine Learning as the tool of choice in solving topical problems in Com

Applications of Computational Intelligen
โœ Cris Doloc ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› Wiley ๐ŸŒ English

<i>"Life on earth is filled with many mysteries, but perhaps the most challenging of these is the nature of Intelligence."</i><br /><br />- Prof. Terrence J. Sejnowski, Computational Neurobiologist<br /><br />The main objective of this book is to create awareness about both the promises and the form

Applications of computational intelligen
โœ Doloc, Cris ๐Ÿ“‚ Library ๐Ÿ“… 2020 ๐Ÿ› Wiley ๐ŸŒ English

"The objective of this book is to introduce the reader to the field of Computational Finance using the framework of Machine Learning as a tool of scientific inquiry. It is an attempt to integrate these two topics: how to use Machine Learning as the tool of choice in solving topical problems in Compu