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.
Audience
Researchers, 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.
Table of Contents
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
