Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking Systems

✍ Scribed by K. Suganthi, R. Karthik, G. Rajesh, Peter Ho Chiung Ching

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 285
• Series: Big Data for Industry 4.0
• Edition: 1
• Category: Library

✦ Synopsis

This book offers the latest advances and results in the fields of Machine Learning and Deep Learning for Wireless Communication and provides positive and critical discussions on the challenges and prospects. It provides a broad spectrum in understanding the improvements in Machine Learning and Deep Learning that are motivating by the specific constraints posed by wireless networking systems.

The book offers an extensive overview on intelligent Wireless Communication systems and its underlying technologies, research challenges, solutions, and case studies. It provides information on intelligent wireless communication systems and its models, algorithms and applications.

The book is written as a reference that offers the latest technologies and research results to various industry problems.

✦ Table of Contents

Cover
Half Title
Series
Title
Copyright
Contents
Preface
Editors
Chapter 1 Overview of Machine Learning and Deep Learning Approaches
Chapter 2 ML and DL Approaches for Intelligent Wireless Sensor Networks
Chapter 3 Machine Learning-Based Optimal Wi­Fi HaLow Standard for Dense IoT Networks
Chapter 4 Energy Efficiency Optimization in Clustered Wireless Sensor Networks via Machine Learning Algorithms
Chapter 5 Machine Learning Approaches in Big Data Analytics Optimization for Wireless Sensor Networks
Chapter 6 Improved Video Steganography for Secured Communication Using Clustering and Chaotic Mapping
Chapter 7 Target Prophecy in an Underwater Environment Using a KNN Algorithm
Chapter 8 A Model for Evaluating Trustworthiness Using Behaviour and Recommendation in Cloud Computing Integrated with Wireless Sensor Networks
Chapter 9 Design of Wireless Sensor Networks Using Fog Computing for the Optimal Provisioning of Analytics as a Service
Chapter 10 DLA­RL: Distributed Link Aware­Reinforcement Learning Algorithm for Delay­Sensitive Networks
Chapter 11 Deep Learning-Based Modulation Detector for an MIMO System
Chapter 12 Deep Learning with an LSTM­Based Defence Mechanism for DDoS Attacks in WSNs
Chapter 13 A Knowledge Investigation Framework for Crowdsourcing Analysis for e­Commerce Networks
Chapter 14 Intelligent Stackelberg Game Theory with Threshold-Based VM Allocation Strategy for Detecting Malicious Co­Resident Virtual Nodes in Cloud Computing Networks
Index