Artificial Intelligent Techniques for Wireless Communication and Networking

ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING

THE 20 CHAPTERS ADDRESS AI PRINCIPLES AND TECHNIQUES USED IN WIRELESS COMMUNICATION AND NETWORKING AND OUTLINE THEIR BENEFIT, FUNCTION, AND FUTURE ROLE IN THE FIELD. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. AUDIENCEResearchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies. R. KANTHAVEL, PhD is a Professor in the Department of Computer Engineering, King Khalid University Abha, Kingdom of Saudi Arabia. He has published more than 150 research articles in reputed journals and international conferences as well as published 10 engineering books. He specializes in communication systems engineering and information and communication engineering.

K. ANANTHAJOTHI, PhD is an assistant professor in the Department of Computer Science and Engineering at Misrimal Navajee Munoth Jain Engineering College, Chennai, India. He has published a book on "Theory of Computation and Python Programming" and holds 2 patents.

S. BALAMURUGAN, PhD is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.

R. KARTHIK GANESH, PhD is an associate professor in the Department of Computer Science and Engineering, SCAD College of Engineering and Technology, Cheranmahadevi, Tamilnadu, India. His research interests are in wireless communication, video and audio compression, image classification, and ontology techniques.

Preface xvii

1 COMPREHENSIVE AND SELF-CONTAINED INTRODUCTION TO DEEP REINFORCEMENT LEARNING 1

P. Anbalagan, S. Saravanan and R. Saminathan

1.1 Introduction 2

1.2 Comprehensive Study 3

1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning 7

1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World 9

1.5 Conclusion 12

2 IMPACT OF AI IN 5G WIRELESS TECHNOLOGIES AND COMMUNICATION SYSTEMS 15

A. Sivasundari and K. Ananthajothi

2.1 Introduction 16

2.2 Integrated Services of AI in 5G and 5G in AI 18

2.3 Artificial Intelligence and 5G in the Industrial Space 23

2.4 Future Research and Challenges of Artificial Intelligence in Mobile Networks 25

2.5 Conclusion 28

3 ARTIFICIAL INTELLIGENCE REVOLUTION IN LOGISTICS AND SUPPLY CHAIN MANAGEMENT 31

P.J. Sathish Kumar, Ratna Kamala Petla, K. Elangovan and P.G. Kuppusamy

3.1 Introduction 32

3.2 Theory--AI in Logistics and Supply Chain Market 35

3.3 Factors to Propel Business Into the Future Harnessing Automation 40

3.4 Conclusion 43

4 AN EMPIRICAL STUDY OF CROP YIELD PREDICTION USING REINFORCEMENT LEARNING 47

M. P. Vaishnnave and R. Manivannan

4.1 Introduction 47

4.2 An Overview of Reinforcement Learning in Agriculture 49

4.3 Reinforcement Learning Startups for Crop Prediction 52

4.4 Conclusion 57

5 COST OPTIMIZATION FOR INVENTORY MANAGEMENT IN BLOCKCHAIN AND CLOUD 59

C. Govindasamy, A. Antonidoss and A. Pandiaraj

5.1 Introduction 60

5.2 Blockchain: The Future of Inventory Management 62

5.3 Cost Optimization for Blockchain Inventory Management in Cloud 66

5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud 71

5.5 Conclusion 72

6 REVIEW OF DEEP LEARNING ARCHITECTURES USED FOR IDENTIFICATION AND CLASSIFICATION OF PLANT LEAF DISEASES 75

G. Gangadevi and C. Jayakumar

6.1 Introduction 75

6.2 Literature Review 76

6.3 Proposed Idea 82

6.4 Reference Gap 86

6.5 Conclusion 87

7 GENERATING ART AND MUSIC USING DEEP NEURAL NETWORKS 91

A. Pandiaraj, S. Lakshmana Prakash, R. Gopal and P. Rajesh Kanna

7.1 Introduction 91

7.2 Related Works 92

7.3 System Architecture 94

7.4 System Development 96

7.5 Algorithm-LSTM 100

7.6 Result 100

7.7 Conclusions 101

8 DEEP LEARNING ERA FOR FUTURE 6G WIRELESS COMMUNICATIONS--THEORY, APPLICATIONS, AND CHALLENGES 105

S.K.B. Sangeetha and R. Dhaya

8.1 Introduction 106

8.2 Study of Wireless Technology 108

8.3 Deep Learning Enabled 6G Wireless Communication 113

8.4 Applications and Future Research Directions 117

9 ROBUST COOPERATIVE SPECTRUM SENSING TECHNIQUES FOR A PRACTICAL FRAMEWORK EMPLOYING COGNITIVE RADIOS IN 5G NETWORKS 121

J. Banumathi, S.K.B. Sangeetha and R. Dhaya

9.1 Introduction 122

9.2 Spectrum Sensing in Cognitive Radio Networks 122

9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments 124

9.4 Cooperative Sensing Among Cognitive Radios 125

9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems 128

9.6 Spectrum Agile Radios: Utilization and Sensing Architectures 128

9.7 Some Fundamental Limits on Cognitive Radio 130

9.8 Cooperative Strategies and Capacity Theorems for Relay Networks 131

9.9 Research Challenges in Cooperative Communication 133

9.10 Conclusion 135

10 NATURAL LANGUAGE PROCESSING 139

S. Meera and S. Geerthik

10.1 Introduction 139

10.2 Conclusions 152

References 152

11 CLASS LEVEL MULTI-FEATURE SEMANTIC SIMILARITY-BASED EFFICIENT MULTIMEDIA BIG DATA RETRIEVAL 155

D. Sujatha, M. Subramaniam and A. Kathirvel

11.1 Introduction 156

11.2 Literature Review 158

11.3 Class Level Semantic Similarity-Based Retrieval 159

11.4 Results and Discussion 164

12 SUPERVISED LEARNING APPROACHES FOR UNDERWATER SCALAR SENSORY DATA MODELING WITH DIURNAL CHANGES 175

J.V. Anand, T.R. Ganesh Babu, R. Praveena and K. Vidhya

12.1 Introduction 176

12.2 Literature Survey 176

12.3 Proposed Work 177

12.4 Results 180

12.5 Conclusion and Future Work 190

13 MULTI-LAYER UAV AD HOC NETWORK ARCHITECTURE, PROTOCOL AND SIMULATION 193

Kamlesh Lakhwani, Tejpreet Singh and Orchu Aruna

13.1 Introduction 194

13.2 Background 196

13.3 Issues and Gap Identified 197

13.4 Main Focus of the Chapter 198

13.5 Mobility 199

13.6 Routing Protocol 201

13.7 High Altitude Platforms (HAPs) 202

13.8 Connectivity Graph Metrics 204

13.9 Aerial Vehicle Network Simulator (AVENs) 206

13.10 Conclusion 207

14 ARTIFICIAL INTELLIGENCE IN LOGISTICS AND SUPPLY CHAIN 211

Jeyaraju Jayaprakash

14.1 Introduction to Logistics and Supply Chain 212

14.2 Recent Research Avenues in Supply Chain 217

14.3 Importance and Impact of AI 222

14.4 Research Gap of AI-Based Supply Chain 224

15 HEREDITARY FACTOR-BASED MULTI-FEATURED ALGORITHM FOR EARLY DIABETES DETECTION USING MACHINE LEARNING 235

S. Deepajothi, R. Juliana, S.K. Aruna and R. Thiagarajan

15.1 Introduction 236

15.2 Literature Review 237

15.3 Objectives of the Proposed System 244

15.4 Proposed System 245

15.5 HIVE and R as Evaluation Tools 246

15.6 Decision Trees 247

15.7 Results and Discussions 250

15.8 Conclusion 252

16 ADAPTIVE AND INTELLIGENT OPPORTUNISTIC ROUTING USING ENHANCED FEEDBACK MECHANISM 255

V. Sharmila, K. Mandal, Shankar Shalani and P. Ezhumalai

16.1 Introduction 255

16.2 Related Study 258

16.3 System Model 259

16.4 Experiments and Results 264

16.5 Conclusion 267

17 ENABLING ARTIFICIAL INTELLIGENCE AND CYBER SECURITY IN SMART MANUFACTURING 269

R. Satheesh Kumar, G. Keerthana, L. Murali, S. Chidambaranathan, C.D. Premkumar

and R. Mahaveerakannan

17.1 Introduction 270

17.2 New Development of Artificial Intelligence 271

17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing 271

17.4 Current Status and Problems of Green Manufacturing 272

17.5 Artificial Intelligence for Green Manufacturing 276

17.6 Detailed Description of Common Encryption Algorithms 280

17.7 Current and Future Works 282

17.8 Conclusion 283

18 DEEP LEARNING IN 5G NETWORKS 287

G. Kavitha, P. Rupa Ezhil Arasi and G. Kalaimani

18.1 5G Networks 287

18.2 Artificial Intelligence and 5G Networks 291

18.3 Deep Learning in 5G Networks 293

19 EIDR UMPIRING SECURITY MODELS FOR WIRELESS SENSOR NETWORKS 299

A. Kathirvel, S. Navaneethan and M. Subramaniam

19.1 Introduction 299

19.2 A Review of Various Routing Protocols 302

19.3 Scope of Chapter 307

19.4 Conclusions and Future Work 311

20 ARTIFICIAL INTELLIGENCE IN WIRELESS COMMUNICATION 317

Prashant Hemrajani, Vijaypal Singh Dhaka, Manoj Kumar Bohra and Amisha Kirti Gupta

20.1 Introduction 318

20.2 Artificial Intelligence: A Grand Jewel Mine 318

20.3 Wireless Communication: An Overview 320

20.4 Wireless Revolution 320

20.5 The Present Times 321

20.6 Artificial Intelligence in Wireless Communication 321

20.7 Artificial Neural Network 324

20.8 The Deployment of 5G 326

20.9 Looking Into the Features of 5G 327

20.10 AI and the Internet of Things (IoT) 328

20.11 Artificial Intelligence in Software-Defined Networks (SDN) 329

20.12 Artificial Intelligence in Network Function Virtualization 331

20.13 Conclusion 332

References 332

Index 335