About the Editors xvii
List of Contributors xix
Preface xxv
Glossary xxvii
Introduction xxix
1 Spectrum Sharing Schemes for 5G and Beyond in Wireless Communication 1
Aditya Bakshi, Akhil Gupta, and Arushi Pandey
1.1 Introduction 1
1.1.1 Motivation 2
1.1.2 Literature Review 2
1.2 Spectrum Sharing Technologies 6
1.2.1 Machine Learning in Spectrum Sharing 7
1.2.2 Cooperative and Cognitive Radio Networks 9
1.2.2.1 Integration of Cooperative and Cognitive Radio Networks 10
1.2.3 Interference Mitigation Strategies 10
1.3 Case Study and Performance Evaluation 12
1.4 Future Trends and Challenges 14
1.4.1 Challenges Facing Wireless Communication 15
1.5 Conclusion 16
References 17
2 Synergizing 5G, IoT, and Deep Learning: Pioneering Technological Integration for a Connected Future 21
Ankita Sharma and Shalli Rani
2.1 Introduction 21
2.2 Security Threats on 5G Network 22
2.3 Applications of 5G 24
2.4 Advanced Intrusion Detection Systems (IDS) 25
2.5 Integration of 5G-IoT-DL 25
2.6 Security Challenges 26
2.7 Role of ML and DL in 5G at Application and Infra Level 27
2.8 Conclusion 29
References 29
3 Driving Next Generation IoT with 5G and Beyond 33
Shishir Shrivastava, Ankita Rana, and Ashu Taneja
3.1 Introduction 33
3.2 Need for Technological Advancement 35
3.3 Existing Wireless Technologies 35
3.4 Challenges in Existing Technologies 37
3.5 Towards 5G Communication 39
3.5.1 MIMO and Massive MIMO 39
3.5.2 Millimeter Wave (mmWave) Communication 42
3.5.3 Small Cells 43
3.5.4 Visible Light Communication 44
3.6 IoT and its Evolution 45
3.7 Role of 5G in IoT 46
3.8 Integration of 5G IoT with Other Technologies 47
3.8.1 Ai/ml 50
3.8.2 Cloud Computing 50
3.8.3 Fog Computing 51
3.8.4 Digital Twin 52
3.8.4.1 Digital Twin Lifecycle: From Data to Transformation 53
3.9 Techniques to Improve the Performance of Wireless Networks 55
3.10 Performance Parameters of Next Generation Wireless Systems 58
3.10.1 The Elaborate Rhythm of Performance Indicators 60
3.11 Challenges and Future Directions 60
3.12 Conclusion 61
References 62
4 Emerging Communication Paradigms for 6G IoT: Challenges and Opportunities 65
Aditya Soni, Ashu Taneja, Neeti Taneja, and Laith Abualigah
4.1 Introduction 65
4.1.1 Breakthrough 6G Technologies 68
4.1.1.1 Holographic MIMO (Multiple Input Multiple Output) 68
4.1.1.2 Intelligent Reflecting Surfaces (IRSs) 68
4.1.1.3 Cell free Massive MIMO 69
4.1.1.4 Edge Computing 70
4.1.1.5 Terahertz (THz) Communication 70
4.1.1.6 Quantum Communication 71
4.2 Internet-of-Things and its Evolution 71
4.2.1 Role of 6G IoT 71
4.2.2 6G IoT Framework 72
4.3 Enabling 6G Technologies for IoT 73
4.3.1 Convergence with Other Key Technologies 75
4.3.1.1 Advancing Beyond Sub-6 GHz Towards THz Communication 76
4.3.1.2 Artificial Intelligence and Advanced Machine Learning 76
4.3.1.3 Compressive Sensing 76
4.3.1.4 Blockchain/Distributed Ledger Technology 77
4.3.1.5 Digital Twin 77
4.3.1.6 Intelligent Edge Computing 77
4.3.1.7 Dynamic Network Slicing 78
4.3.1.8 Big Data Analytics 78
4.3.1.9 Wireless Information and Power Transfer (WIPT) 78
4.3.1.10 Backscatter Communication 79
4.3.1.11 Communication-Computing-Control Convergence 79
4.4 Use Case Scenarios 80
4.4.1 Smart Healthcare 80
4.4.2 Smart Transportation 81
4.4.3 Smart Manufacturing 82
4.4.4 Smart Agriculture 83
4.4.5 Smart Classrooms 83
4.4.6 Smart Cities 84
4.5 Challenges Faced and the Solutions Offered 85
4.6 Conclusion 86
References 87
5 Securing the Internet of Things: Cybersecurity Challenges, Strategies, and Future Directions in the Era of 5G and Edge Computing 89
Geetanshi, Harshit Manocha, Himanshi Babbar, and Cherry Mangla
5.1 Introduction 89
5.1.1 History of IoT and Edge Computing in 5G 94
5.2 Literature Review 95
5.3 Applications in IoT and Edge Computing 95
5.4 Cybersecurity Management System for IoT Environments 97
5.4.1 Security Layers 97
5.5 Current Cyber Security Strategies in IoT 99
5.6 IoT Cybersecurity's Role in Reshaping Machine Learning 100
5.6.1 Role of IoT in Artificial Intelligence 101
5.7 Real Life Scenario 102
5.8 Conclusions 105
References 105
6 Autonomous Systems for 5G Networks: A Comprehensive Analysis of Features Toward Generalization and Adaptability 107
Durga Shankar Baggam and Shalli Rani
6.1 Introduction 107
6.2 Survey Method 109
6.3 Background and Related Works 113
6.3.1 Autonomous System Architecture 114
6.3.1.1 Application Layer 120
6.3.1.2 Cognitive Layer 120
6.3.1.3 Perception Layer 120
6.3.1.4 Physical Layer 120
6.3.2 Sensors 121
6.3.3 Artificial Intelligence Techniques 121
6.3.4 Intelligent Transport System (ITS) 124
6.3.5 B5G-Based Vehicular Telecommunication 125
6.4 Discussion 126
6.4.1 Environmental Uncertainties 128
6.4.2 Security Challenges and Counter Measures 129
6.5 Conclusion 129
References 130
7 Integrated Trends, Opportunities, and Challenges of 5G and Internet of Things 139
Ekta Dixit and Shalli Rani
7.1 Introduction 139
7.1.1 Overview of 5G 140
7.1.2 Evolution from 1G to 5G 141
7.1.3 5G Architecture 141
7.1.4 Overview of IoT 143
7.1.5 Features of IoT 143
7.1.5.1 Avalability 143
7.1.5.2 Mobility 143
7.1.5.3 Scalabilty 143
7.1.5.4 Security 144
7.1.5.5 Context Awareness 144
7.1.6 IoT Architecture 144
7.1.6.1 Application Layer 144
7.1.6.2 Network Layer 144
7.1.6.3 Edge Layer 145
7.2 Requirements for Integration of 5G with IoT 145
7.2.1 Integrated 5G IoT Layered Architecture 145
7.3 Opportunities of 5G integrated IoT 146
7.3.1 Smart Cities 146
7.3.2 Smart Vehicles 146
7.3.3 Device to Device Communications 147
7.3.4 Business 147
7.3.5 Satelite and Aerial Research 147
7.3.6 Video Surveillance 147
7.4 Challenges of 5G Integrated IoT 147
7.4.1 Insufficient Control over Data Storage and Usage 148
7.4.2 Scalability 148
7.4.3 Heterogeneity of 5G and IoT Data 148
7.4.4 Blockchain Processing Time 148
7.4.5 5G mm-Wave Issues 149
7.4.6 Threat Protection of 5G IoT 149
7.5 Conclusion 149
References 150
8 Advancement in Resource Allocation for Future Generation of Communications 153
Garima Chopra and Suhaib Ahmed
8.1 Introduction 153
8.2 Current Trends in Multiple Access Techniques 154
8.3 Scheduling Algorithms for 5G/Beyond 5G 155
8.4 Factors Influencing Scheduling Algorithms 158
8.5 Resource Allocation for 5G Ultra-Dense Networks 160
8.6 Conclusion 162
References 162
9 Next-Gen Networked Healthcare: Requirements and Challenges 165
Kanica Sachdev and Brejesh Lall
9.1 Introduction 165
9.2 Applications 166
9.2.1 Remote Robotic-Assisted Surgery 167
9.2.2 Remote Diagnosis and Teleconsultation 167
9.2.3 In-Ambulance Treatment 168
9.2.4 Remote Patient Monitoring 169
9.2.5 Medical Big Data Management 170
9.2.6 Augmented Reality (AR) and Virtual Reality (VR) 170
9.2.7 Emergency Response Strategies 171
9.3 Technological Prerequisites 172
9.4 Challenges in 5G Integration in Healthcare 175
9.5 Conclusion 177
References 180
10 Dynamic Resource Orchestration for Computing, Data, and IoT in Networked Systems: A Data-Centric Approach 185
Suresh Limkar, Mohammad Alamgir Hossain, Sherif Tawfik Amin, and Yasir Ahmad
10.1 Introduction 185
10.1.1 Motivation 187
10.1.2 Objectives 187
10.2 Dynamic Resource Orchestration: Foundations 187
10.2.1 Resource Orchestration Concepts 187
10.2.2 Dynamic Resource Orchestration's Evolution 188
10.2.3 Importance of a Data-Centric Perspective 188
10.3 Computing in Networked Systems 189
10.3.1 Cloud Computing Paradigm 189
10.3.2 Edge Computing and Fog Computing 191
10.3.3 Integration of Computing Resources 192
10.4 Data-Centric Orchestration 193
10.4.1 Data-Driven Resource Allocation 193
10.4.1.1 Data-Driven Decision-Making 193
10.4.1.2 Dynamic Scaling 194
10.4.1.3 Perceptive Formulas 194
10.4.1.4 Customization and Adaptability 194
10.4.2 Data Processing and Management 194
10.4.2.1 Data Locality and Optimization 194
10.4.2.2 Techniques for Data Movement 194
10.4.2.3 Data Lifecycle Management 194
10.4.2.4 AI and Data Analytics Integration 195
10.4.3 Security and Privacy Considerations 195
10.4.3.1 Completely Encryption 195
10.4.3.2 Identity and Access Management 195
10.4.3.3 Safe Data Processing 195
10.4.3.4 Regulatory Standard Compliance 195
10.4.3.5 Privacy-Preserving Techniques 195
10.4.3.6 Audit Trails and Monitoring 196
10.5 IoT Integration 196
10.5.1 Overview of IoT Architecture 196
10.5.2 IoT Resource Orchestration Challenges 197
10.5.2.1 Device Heterogeneity 197
10.5.2.2 Scalability and Data Volume 197
10.5.2.3 Low-Latency and Real-Time Processing 197
10.5.2.4 Compatibility and Standards 197
10.5.3 Combining Data and Computing 197
10.5.3.1 Data-Centric Orchestration 198
10.5.3.2 IoT with Machine Learning and AI 198
10.5.3.3 Dynamic Resource Allocation 198
10.5.3.4 IoT Security Measures 199
10.6 Methodologies for Dynamic Resource Orchestration 200
10.6.1 Methods of Machine Learning 200
10.6.1.1 Overview of Machine Learning for Resource Management 200
10.6.1.2 Predictive Resource 200
10.6.1.3 Fault Prediction and Anomaly Detection 200
10.6.2 Methods of Optimisation 201
10.6.2.1 Introducing Resource Orchestration's Optimisation Techniques 201
10.6.3 Hybrid Models 201
10.6.3.1 Optimisation Through Machine Learning Hybrids 201
10.6.3.2 Combining Rule-Based and Learning-Based Methods: Advancing Hybrid Approaches 201
10.6.3.3 Continual Enhancement Through Responsive Feedback Mechanisms 202
10.6.3.4 Harnessing the Power of Adaptive Model Switching 202
10.7 Case Studies 202
10.7.1 Practical Applications 202
10.7.1.1 Aws 202
10.7.1.2 Autoscaling of Kubernetes Horizontal Pods 202
10.7.2 Achievements and Insights Acquired 203
10.7.2.1 Netflix: Using Machine Learning to Deliver Content 203
10.7.2.2 Google's Expansion of Kubernetes: Enhancing Scalability 203
10.7.2.3 Achieving Dynamic Scalability with AWS Auto Scaling: An Airbnb Success Story 203
10.8 Conclusion 204
References 204
11 Cognitive Cellular Networks: Empowering Future Connectivity Through Artificial Intelligence 209
Mohammad Alamgir Hossain, Suresh Limkar, Sherif Tawfik Amin, and Yasir Ahmad
11.1 Introduction 209
11.1.1 Background 209
11.1.2 Key Objectives of the Chapter 210
11.2 Foundations of Cognitive Cellular Networks 211
11.2.1 Architecture of Cellular Networks 211
11.2.2 Radio Technologies Induced by Cognition 211
11.2.3 Artificial Intelligence Integration 212
11.3 AI Algorithms for Network Optimization 213
11.3.1 Machine Learning Models for Predictive Analysis 213
11.3.1.1 Machine Learning in Resource Allocation 213
11.3.1.2 Predictive Analytics for Traffic Management 213
11.3.1.3 Reinforcement Learning for Self-Optimizing Networks 213
11.3.1.4 Anomaly Detection to Strengthen Security 214
11.3.1.5 Artificial Neural Networks for Dynamic Optimization 214
11.3.1.6 Combining Genetic Algorithms with Cross-Layer Optimization 214
11.3.2 Spectrum Utilization and Management 214
11.3.2.1 Dynamic Spectrum Access 214
11.3.2.2 Brain CRT 215
11.3.2.3 Enhancing Spectrum Management with AI-Powered Solutions to Combat Interference 215
11.3.2.4 Achieving Regulatory Compliance in Spectrum Sharing 215
11.4 Reinforcement Learning in Autonomous Network Management 215
11.4.1 Essential Guidelines for Mastering Reinforcement Learning 216
11.4.2 Adaptive Decision-Making in Dynamic Environments 217
11.4.2.1 Time-Based Learning and the Trade-Off Between Exploration and Exploitation 217
11.4.2.2 Dynamic Approaches to State Representation and Policy Adaptation 218
11.4.3 Case Studies on Autonomous Network Management 218
11.5 Applications of Cognitive Cellular Networks 219
11.5.1 Upgraded Mobile Broadband 220
11.5.2 Massive Machine-Type Communication 220
11.5.3 Ultra-reliable Low-Latency Communication 221
11.5.4 Use Cases and Practical Implementations 221
11.6 Challenges and Future Directions 222
11.6.1 Scalability and Standardization 222
11.6.2 Future Trends in Cognitive Cellular Networks 222
11.7 Conclusion 223
References 224
12 Enhancing Scalability and Performance in Networked Applications Through Smart Computing Resource Allocation 227
Araddhana Arvind Deshmukh, Shailesh Pramod Bendale, Sheela Hundekari, Abhijit Chitre, Kirti Wanjale, Amol Dhumane, Garima Chopra, and Shalli Rani
12.1 Introduction 227
12.1.1 Scope and Objectives 229
12.1.2 Objectives 229
12.1.2.1 Key Goals of This Study 229
12.2 Foundations of Smart Computing Resource Allocation 230
12.2.1 Key Concepts in Resource Allocation 232
12.2.1.1 Dynamic Resource Allocation 232
12.2.1.2 Artificial Intelligence (AI) in Resource Management 232
12.2.1.3 Using Real-Time Analytics to Track Performance 232
12.2.1.4 Scalability and Elasticity Measures 232
12.2.1.5 Mechanisms of Adaptive Learning 233
12.2.1.6 Security-Driven Resource Allocation 233
12.2.2 The Evolution of Scalability and Performance in Networked Applications 233
12.2.2.1 Conventional Static Resource Allocation 233
12.2.2.2 The Arise of Scalability Issues 233
12.2.2.3 The Cloud Paradigm and Dynamic Resource Allocation 234
12.2.2.4 Using Smart Computing to Allocate Intelligent Resources 234
12.2.2.5 Real-Time Adaptation and Predictive Scaling 234
12.2.2.6 Scalability Beyond Traditionally Assigned Limitations 234
12.2.2.7 Automation and Autonomy's Role 234
12.3 Dynamic Resource Allocation Strategies 235
12.3.1 Static vs. Dynamic Resource Allocation 237
12.3.1.1 Static Resource Allocation 237
12.3.1.2 Dynamic Resource Allocation 237
12.3.2 Adaptive Resource Allocation Algorithms 237
12.3.3 Machine Learning Approaches in Resource Allocation 238
12.4 Intelligent Load Balancing Techniques 238
12.4.1 Load Balancing in Networked Environments 239
12.4.2 Importance of Load Balancing in Scalability 240
12.4.2.1 Load Balancing with Machine Learning 240
12.4.2.2 Adaptive Load Balancing Algorithms 240
12.5 Real-Time Monitoring and Feedback Mechanisms 241
12.5.1 Proactive Monitoring for Allocation of Resources 241
12.5.2 Decision-Making and Feedback Loops 241
12.5.3 Real-Time Monitoring 242
12.6 Case Studies and Best Practices 243
12.6.1 Cloud-Based Resource Allocation 243
12.6.2 Edge Computing and Resource Optimization 243
12.6.3 High-Performance Computing (HPC) Environments 244
12.7 Security and Privacy Considerations 244
12.7.1 Ensuring Security in Resource Allocation 244
12.7.1.1 Overview of Security 244
12.7.2 Privacy Issues with Wise Resource Distribution 245
12.7.2.1 Overview of Privacy 245
12.7.3 Balancing Security and Performance 245
12.7.3.1 Understanding the Art of Balancing Responsibilities 245
12.8 Future Trends and Emerging Technologies 246
12.8.1 Resource Allocation and Edge AI 246
12.8.1.1 Understanding the Basics of Edge AI 246
12.8.2 Implications for Quantum Computing 246
12.8.2.1 A Comprehensive Look at the World of Quantum Computing 246
12.8.3 Allocating Resources with Blockchain 247
12.8.3.1 Overview of Blockchain 247
12.9 Conclusion 248
References 248
13 5G-Enabled Fusion: Navigating the Future Landscape of Cloud Computing, Internet of Things, and Recommender Systems 251
Sheetal Sharma
13.1 Basics of Cloud Computing 251
13.2 Internet of Things 254
13.3 5G Technology 257
13.4 Recommender System 258
13.5 Conclusion 262
References 262
14 Confluence of Cellular IoT and Data Science for Smart Application using 5G 267
Shruti and Shalli Rani
14.1 Introduction 267
14.2 Data Science and Cellular IoT 270
14.3 Research Problems in Data Science for Cellular IoT 272
14.4 Sensors in Cellular IoT Smart Farming 273
14.5 Related Work 275
14.6 Data Science for Agriculture 277
14.7 Challenges Faced by Cellular IoT Application in Data Science 278
14.8 Proposed Model and its Discussion 280
14.9 Conclusion 281
References 282
Index 285
