"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 understandingthe 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"--
This book offers the latest advances and results in the fields of machine learning and deep learning for wireless communications and provides positive discussions on the challenges and prospects. It includes a broad spectrum in understanding the improvements motivated by specific constraints posed by wireless communications.
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.
| Preface |
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vii | |
| Editors |
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ix | |
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Chapter 1 Overview of Machine Learning and Deep Learning Approaches |
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1 | (10) |
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Chapter 2 ML and DL Approaches for Intelligent Wireless Sensor Networks |
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11 | (30) |
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Chapter 3 Machine Learning-Based Optimal Wi-Fi HaLow Standard for Dense IoT Networks |
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41 | (18) |
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Chapter 4 Energy Efficiency Optimization in Clustered Wireless Sensor Networks via Machine Learning Algorithms |
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59 | (20) |
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Chapter 5 Machine Learning Approaches in Big Data Analytics Optimization for Wireless Sensor Networks |
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79 | (18) |
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Chapter 6 Improved Video Steganography for Secured Communication Using Clustering and Chaotic Mapping |
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97 | (20) |
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Chapter 7 Target Prophecy in an Underwater Environment Using a KNN Algorithm |
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117 | (14) |
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Chapter 8 A Model for Evaluating Trustworthiness Using Behaviour and Recommendation in Cloud Computing Integrated with Wireless Sensor Networks |
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131 | (22) |
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Chapter 9 Design of Wireless Sensor Networks Using Fog Computing for the Optimal Provisioning of Analytics as a Service |
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153 | (22) |
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Rajalakshmi Shenbaga Moorthy |
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Chapter 10 DLA-RL: Distributed Link Aware-Reinforcement Learning Algorithm for Delay-Sensitive Networks |
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175 | (14) |
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Chapter 11 Deep Learning-Based Modulation Detector for an MIMO System |
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189 | (12) |
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Arumbu Vanmathi Neduncheran |
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Siddhartha Dhar Choudhury |
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Chapter 12 Deep Learning with an LSTM-Based Defence Mechanism for DDoS Attacks in WSNs |
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201 | (24) |
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Chapter 13 A Knowledge Investigation Framework for Crowdsourcing Analysis for e-Commerce Networks |
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225 | (24) |
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Chapter 14 Intelligent Stackelberg Game Theory with Threshold-Based VM Allocation Strategy for Detecting Malicious Co-Resident Virtual Nodes in Cloud Computing Networks |
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249 | (20) |
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| Index |
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269 | |
K. Suganthi received her B.E. degree in Computer Science and Engineering from Madras University, Masters in Systems Engineering and Operations Research and Ph.D. in Wireless Sensor Network from the Anna University. She is currently working as Assistant Professor Senior in the School of Electronics Engineering (SENSE) at Vellore Institute of Technology, Chennai campus, India since 2016. She is the author of about 20 scientific publications on journals and International conferences. Her research interests include Wireless Sensor Network, Internet of Things, Data Analytics and AI.
R. Karthik obtained his Doctoral degree from Vellore Institute of Technology, India and Masters degree from Anna University, India. Currently, He serves as Senior Assistant Professor in the Research Center for Cyber Physical Systems, Vellore Institute of Technology, Chennai. His research interest includes Deep Learning, Computer Vision, Digital Image Processing, and Medical Image Analysis. He has published around 32 papers in peer reviewed journals and conferences. He is an active reviewer for journals published by Elsevier, IEEE Springer and Nature.
G. Rajesh is working as an Assistant professor, Department of Information Technology of Anna University, Chennai, India. He completed his PhD from Anna University, Chennai in wireless sensor networks. He has around 12 years of teaching and research experience. His area of research interest includes wireless sensor networks and its IoT applications, software engineering and computational optimization. He published more than 20 research papers in journals and conferences.
Peter Ho Chiung Ching received his Ph.D. in Information Technology from the Faculty of Computing and Informatics, Multimedia University. His doctoral research work was on the performance evaluation of multimodal biometric systems using fusion techniques. Dr. Ho is a Senior Member of the Institute of Electrical and Electronics Engineers. Dr. Ho has published a number of peer reviewed papers related to location intelligence, multimodal biometrics, action recognition and text mining. He is currently an Adjunct Senior Research Fellow in the Department of Computing and Information Systems, School of Science and Technology, Sunway University.
