Description
Master the shift from centralized clouds to the network’s edge with this essential guide, providing real-world case studies and 6G strategies to build faster, more reliable industrial systems.
6G, the next generation of wireless communication technology, will enable unparalleled connectivity and data transfer speeds with ultra-reliable, low-latency transmission. This means better processing and decision-making in real-time. Instead of storing and processing the user’s data in a centralized cloud, edge intelligence allows users to process data locally, at the network’s periphery. With 6G-enabled IIoT, data from industrial devices and sensors can be handled locally, resulting in lower latency and faster response times for mission-critical applications. This book introduces edge intelligence and the 6G-enabled industrial Internet of Things ecosystem. It offers practical guidance and fosters a deeper understanding of how edge intelligence can be integrated with 6G-enabled IIoT applications and frameworks in a modern industrial environment. Through case studies and real-life examples, it will explore the complexities associated with real-life implementations for industrial applications, making it an invaluable resource in today’s digitally industrial ecosystem.
Readers will find the volume:
- Provides a clear overview of edge intelligence and 6G-enabled IIoT integration;
- Bridges the gap between theoretical concepts and real-life industrial use cases;
- Includes real-world case studies to illustrate practical applications;
- Offers strategies to overcome industrial implementation challenges.
Audience
Engineers, data scientists, researchers, and technology professionals who are involved in industrial IoT, edge computing, and emerging 6G technologies.
Table of Contents
Foreword xxi
Preface xxiii
Part 1: Introduction, and Future Prospects to Edge Intelligence for 6G Enabled Industrial Internet of Things 1
1 Unveiling the 6G Landscape in Industrial IoT 3
Sita Rani and Pankaj Bhambri
1.1 Introduction 4
1.1.1 Evolution from 5G to 6G Technology 4
1.1.2 The Role of IoT in Industry 4.0 4
1.1.3 Importance of 6G in Enhancing Industrial IoT 6
1.2 Key Features of 6G Technology 6
1.2.1 Ultra-High Speeds 7
1.2.2 Ultra-Low Latency 7
1.2.3 Massive Connectivity 7
1.2.4 Advanced AI and Machine Learning Integration 7
1.2.5 Enhanced Reliability and Security 7
1.2.6 Energy Efficiency and Sustainability 7
1.2.7 Holographic Communication and Extended Reality (XR) 7
1.2.8 Global Coverage and Integration 8
1.2.9 Network Slicing and Customized Services 8
1.2.10 Quantum Communication and Computing 8
1.3 6G Use Cases in Industrial IoT 8
1.4 Challenges and Considerations in Deploying 6G for IIoT 11
1.5 Impact of 6G on Industry Standards and Protocols 13
1.6 Future Directions and Research Opportunities 15
1.7 Case Studies and Real-World Implementations 17
1.8 Conclusion 18
References 19
2 Foundations of Edge Intelligence in 6G Networks 23
D. Harika, C. Venkataramanan, K. Neelima and Satyam
2.1 Introduction 24
2.2 Key Drivers and Goals of 6G Networks 24
2.3 Role of Distributed Intelligence in Overcoming Traditional Limitations 26
2.4 Fundamental Building Blocks of Edge Intelligence in 6G 29
2.5 Transformative Applications Enabled by Edge Intelligence 30
2.5.1 R1 - Sample Complexity 31
2.5.2 R2 - Reliable Prediction 31
2.5.3 R3 - Perception-Aware Prediction 31
2.5.4 R4 - Multimodal Fusion 31
2.5.5 R5 - Beyond Visual Modality 31
2.5.6 R6 - Non-RF Overhead 32
2.5.7 R7 - Controller Connectivity 32
2.5.8 R8 - Stable Control 32
2.5.9 R9 - Scalable Control 32
2.6 Challenges and Enablers of Edge Intelligence 33
2.7 Conclusion 36
References 36
3 Advancements in Industrial Connectivity: A 6G Perspective 39
Kali Charan Rath, Nagavarapu Sowmya, Aditi Sharma and Brojo Kishore Mishra
3.1 Introduction 40
3.2 Smart Manufacturing and Communication 41
3.2.1 Comparison between 5G and 6G Network 42
3.2.2 6G Technology and Importance for Implementation 42
3.2.3 6G Technology and Its Significance 43
3.3 Manufacturing Processes Enhancement through 6G Networks 45
3.3.1 Case Study of Smart Manufacturing Technologies with 6G 46
3.4 Smart Auto Manufacturing Powered by 6G: A Case Study 48
3.4.1 Integration of 6G Connectivity, AI, IoT, and Edge Computing in Automobile Smart Manufacturing Optimizes Processes 51
3.4.2 Algorithm for Real-Time Monitoring and Control of Factory Machines and Processes (Predictive Maintenance) with the Application of 6G 54
3.5 Challenges and Obstacles in the Adoption of 6G Networks in Industrial Connectivity 56
3.6 Conclusion 63
3.6.1 Future Scope of Work 63
References 64
4 Security Paradigm for 6G-Enabled IIoT Ecosystems 67
Rachna Rana and Pankaj Bhambri
4.1 Introduction 68
4.2 Therefore, What Exactly is Industrial Internet of Things Security? In What Ways Does It Propel Digital Transformation to Shift Business Models and Boost Organizational Effectiveness? Is this a Way Out? How Can Businesses Make the Most of these Advancements to Achieve Their Goals? What Exactly is Industrial Internet of Things Security (IIoT)? 74
4.3 Why is Security Relevant to IIoT? 74
4.3.1 Protection of Systems 75
4.3.2 Information Protection 75
4.3.3 Crime Prevention 75
4.3.4 Cost Savings 75
4.3.5 Enhanced Productivity 75
4.4 Which Technologies Underpin IIoT Security? 75
4.4.1 Devices and Sensors 75
4.4.2 Encryption of Data 76
4.4.3 Authentication 76
4.4.4 These Security Measures Keep an Eye on the Digital World 76
4.4.5 Updates and Patches 76
4.4.6 Remote Monitoring 76
4.4.7 Environmental Response 76
4.4.8 Behavioral Analysis 76
4.4.9 Machine Learning 77
4.4.10 Redundancy 77
4.4.11 Periodic Audits 77
4.5 Why are IIoT Security Standards Needed? 77
4.6 What Steps Can Network Administrators and CISOs Take to Secure Their Networks and Devices? 77
4.6.1 Byos Secure Gateway Edge has the Following Advantages 78
4.7 What Makes IIoT Security Different from IoT Security? 78
4.8 Security Benefits of IIoT 78
4.8.1 Data Security 78
4.8.2 Stops Interruptions 80
4.8.3 Guarantees Security 80
4.8.4 Preserves Credibility 80
4.8.5 Privacy-Protecting 80
4.8.6 Stops Unauthorized Entry 81
4.8.7 Protects Vital Infrastructure 81
4.8.8 Lowers Danger 81
4.9 Case Study 1: Agricultural Cost Reduction 81
4.10 Conclusion and Future Scope 82
4.10.1 Advanced Threat Protection 82
4.10.2 Real-Time Monitoring 82
4.10.3 Advances in Encryption 82
4.10.4 Scalable Solutions 82
4.10.5 User-Friendly Interfaces 82
4.10.6 Combining Machine Learning and Artificial Intelligence 83
4.10.7 Assurance of Compliance 83
References 83
5 Machine Learning Dynamics in 6G Industrial Environments 85
Naina Agrawal, J. Jayashree and J. Vijayashree
5.1 Introduction 86
5.2 Foundations of 6G Technology 90
5.2.1 Overview of 6G Capabilities 90
5.2.2 Integration of AI and Machine Learning into 6G Networks 90
5.2.3 Key Features Making 6G Suitable for Industrial Applications 92
5.3 Machine Learning Algorithms in Industrial Environments 92
5.3.1 Exploration of Machine Learning Algorithms 92
5.3.2 Real-World Applications of Machine Learning 93
5.3.3 Case Studies Illustrating Machine Learning Success Stories 94
5.4 Real-Time Data Processing and Edge Computing 96
5.4.1 Significance of Real-Time Data Processing 96
5.4.2 Role of Edge Computing in Industrial Environments 97
5.4.3 Diagrams Illustrating 6G-Enabled Industrial System with Edge Computing 98
5.5 Predictive Maintenance and Fault Detection 102
5.5.1 Utilizing Machine Learning for Predictive Maintenance 102
5.5.2 Fault Detection Algorithms for Industrial Processes 103
5.5.3 Case Studies Showcasing Predictive Maintenance Success Stories 105
5.6 Autonomous Systems and Robotics 106
5.6.1 Integration of Machine Learning into Autonomous Systems 106
5.6.2 Robotics Empowered by 6G Connectivity and Machine Learning 108
5.6.3 Diagrams Illustrating Communication Network in 6G-Enabled Autonomous Systems 110
5.7 Security and Privacy Concerns 113
5.7.1 Addressing Security Challenges in 6G-Enabled Industrial Environments 113
5.7.2 Privacy Considerations in Machine Learning Applications 114
5.7.3 Strategies for Ensuring Data Security and Privacy 115
5.8 Conclusion 116
5.9 Future Prospects 116
References 117
6 Wireless Infrastructure for Robust 6G IIoT Connectivity 121
Boudhayan Bhattacharya and Arpan Kisore Sarbadhikari
6.1 Introduction 122
6.2 Key Features and Expectations of 6G Technology 123
6.3 Unique Requirements of IIoT Applications 124
6.4 Wireless Infrastructure Components for IIoT 124
6.4.1 Edge Computing 124
6.4.1.1 Key Concepts and Architecture 125
6.4.1.2 Key Benefits 125
6.4.2 Architecture: Fog Layers and Nodes 127
6.4.2.1 Key Concepts and Architecture 127
6.4.2.2 Key Benefits: Key Benefits for IIoT Include 128
6.5 Advanced Communication Protocols 129
6.5.1 Edge 5G NR (New Radio) 129
6.5.1.1 Key Features of 5G NR 129
6.5.1.2 Deployment and Implementation 130
6.5.2 Time-Sensitive Networking (TSN) 131
6.5.2.1 Key Features of TSN 131
6.5.2.2 Deployment & Implementation 132
6.5.3 Low Power Wide Area Networks (LPWANs) 134
6.5.3.1 Key Features of LPWAN 134
6.5.3.2 Deployment and Implementation 135
6.5.3.3 Common LPWAN Technologies 138
6.6 Practical Use Cases and Industry Examples 139
6.6.1 Predictive Maintenance 139
6.6.2 Smart Manufacturing 139
6.6.3 Supply Chain Optimization 139
6.7 Integration of 6G Capabilities 140
6.7.1 Faster Data Transmission 140
6.7.2 Improved Network Reliability 140
6.7.3 Enhanced Security Measures 140
6.8 Coexistence and Interoperability 140
6.8.1 Coexistence of Multiple Wireless Technologies 140
6.8.2 Interoperability Challenges 140
6.8.3 Importance of Standardization 141
6.9 Conclusion 141
References 141
7 Future Horizons: Emerging Trends in Edge Intelligence for IIoT 143
J. Vigneshwari, K. Geetha, P. Senthamizh Pavai and L. Maria Suganthi
7.1 Introduction- An Outline on IIoT 144
7.2 Significance of IIoT 145
7.2.1 IIoT vs IoT 146
7.3 Future of IIoT 147
7.4 Edge Intelligence 149
7.4.1 Edge AI for Autonomous Decision-Making 149
7.4.2 Artificial Intelligence (AI) and Machine Learning (ML) 151
7.5 The 4.0 Technology 152
7.5.1 The 4.0 Solution 152
7.6 Challenges and Considerations for Adopting IIoT Trends 153
7.7 6G and Future Horizons 155
7.8 Benefits of Investing in IIoT 156
7.8.1 Planning and Implementation of IIoT 157
7.9 Conclusion 158
References 159
Part 2: Advances and Applications of Edge Intelligence for 6G Enabled Industrial Internet of Things 163
8 Connecting the 6G Autonomous Worlds with Real Time Edge Intelligence (Autonomous Vehicle) 165
Hemant Kumar Saini
8.1 Introduction 166
8.2 Evolutions 168
8.2.1 1G Communication 168
8.2.2 2G Communication 169
8.2.3 3G Communication 169
8.2.4 4G Communication 170
8.2.5 5G Generation 170
8.2.6 6G Communication 171
8.3 Issues in 6G Edges 171
8.4 6G with Edge 173
8.5 Edge Intelligence with Autonomous Vehicle 175
8.6 Forthcoming Edge Driven AI Based 6G in Autonomous Vehicular Applications 176
8.7 Future Perspective of Edge Intelligence in Vehicles 177
References 178
9 Performance Improvement of 6G Internet of Things Using Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 181
B.N. Chandrashekhar and V. Geetha
9.1 Introduction 182
9.1.1 Edge Computing with AI 182
9.1.2 HPC Infrastructure 183
9.1.2.1 Multicore Architecture 184
9.1.2.2 Many-Core Architecture 185
9.1.2.3 Hybrid [CPU+GPU] Architecture 186
9.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 187
9.2.1 Overview of Converged HPC Infrastructure and Edge AI 188
9.2.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 190
9.2.3 Innovation in 6G IOT 191
9.3 Performance Optimization 193
9.3.1 AI-Based Intra-Node and Internode Communication on CPUs and GPUs-Based HPC Infrastructure 193
9.3.2 Optimal Workload Distribution 194
9.3.3 Evaluation of Performance 196
References 196
10 Embedding Privacy into Industrial IoT System 199
N. Ambika
10.1 Introduction 200
10.2 Background 206
10.3 Literature Survey 207
10.4 Previous System 209
10.5 Proposed System 210
10.6 Analysis of the Work 212
10.7 Simulation 213
10.8 Future Scope 214
10.9 Conclusion 214
References 215
11 Exploring Novel Directions in Edge Intelligence for Industrial Internet of Things (IIoT) 217
T. Thangarasan, R. Keerthana, J. Nagaraj, S. Vani and R.M. Dilip Charaan
11.1 Introduction to the Internet of Things 218
11.1.1 Key Components of IoT 218
11.1.2 Applications of IoT 218
11.1.3 Challenges of IoT 219
11.2 Industrial Internet of Things (IIoT) 219
11.2.1 Key Components of IIoT 219
11.2.2 Applications of IIoT 220
11.2.3 Benefits of IIoT 221
11.2.4 Challenges of IIoT 221
11.3 Decentralized Edge Intelligence Ecosystems 221
11.3.1 Components 222
11.3.2 Benefits 222
11.3.3 Real-Time Anomaly Detection and Predictive Maintenance 223
11.3.3.1 Real-Time Anomaly Detection 223
11.3.3.2 Technologies Used 223
11.3.3.3 Predictive Maintenance 224
11.3.4 Benefits 224
11.3.5 Challenges 224
11.3.6 Applications 225
11.4 Federated Learning for Edge Devices 225
11.4.1 Key Concepts 225
11.4.2 Benefits 226
11.4.3 Challenges 226
11.4.4 Applications 226
11.4.5 How it Works 227
11.4.6 Example Workflow 227
11.4.7 Key Algorithms 227
11.4.8 Technical Considerations 227
11.5 Energy-Efficient Edge Computing 228
11.5.1 Key Strategies 228
11.5.2 Technologies and Techniques 229
11.5.3 Benefits 229
11.5.4 Challenges 230
11.5.5 Applications 230
11.5.6 Example Approaches 231
11.6 Integration of Augmented Reality (AR) and Virtual Reality (VR) 231
11.6.1 Key Concepts 231
11.6.2 Integration of AR and VR 232
11.6.3 Applications 232
11.6.4 Benefits 233
11.6.5 Challenges 233
11.6.6 Future Trends 234
11.7 Edge-Based Data Fusion 234
11.7.1 Key Components 234
11.7.2 Applications 235
11.7.3 Benefits 236
11.7.4 Challenges 236
11.7.5 Implementation Strategies 237
11.7.6 Future Trends 237
11.8 Distributed Edge Intelligence Marketplaces 238
11.8.1 Key Concepts 238
11.8.2 Components 238
11.8.3 Benefits 239
11.8.4 Challenges 239
11.8.5 Potential Applications 240
11.8.6 Implementation Strategies 240
11.8.7 Future Trends 241
11.9 Edge-to-Cloud Orchestration 242
11.9.1 Key Components 242
11.9.2 Benefits 243
11.9.3 Challenges 243
11.9.4 Use Cases 244
11.9.5 Implementation Strategies 245
11.9.6 Future Trends 245
11.10 Conclusion 246
References 247
12 6G Network: Integrating Wireless Networks and Machine Learning for Connected Edge Intelligence 249
B. Prabha, V. Praveen and M.R. Santhoosh
12.1 Introduction 250
12.1.1 Definition and Importance of Edge Intelligence in the 6G Context 250
12.2 Evolution of Wireless Networks for Edge Intelligence 252
12.2.1 Historical Perspective: From 1G to 6G and the Evolution of Edge Computing 252
12.2.2 Key Technological Advancements Enabling Edge Intelligence in 6G Networks 253
12.3 Challenges in Integrating AI with Wireless Networks 255
12.3.1 Latency and Real-Time Processing Requirements 255
12.3.2 Energy Efficiency and Resource Optimization 256
12.3.3 Privacy and Security Concerns in Edge AI Systems 256
12.4 Machine Learning Models for Edge Computing 257
12.4.1 Overview of Decentralized Machine Learning Algorithms 257
12.4.2 Model Compression and Optimization Techniques for Edge Devices 258
12.4.3 Federated Learning and Collaborative Intelligence at the Edge 259
12.5 Design Principles for Edge AI Systems in 6G 260
12.5.1 Scalable Architecture for Edge AI Deployment 260
12.5.2 Service-Driven Resource Allocation and Management 261
12.5.3 Edge-to-Cloud Continuum: Balancing Computation between Edge and Central Servers 263
12.6 Applications and Use Cases of Edge Intelligence in 6G Networks 263
12.6.1 Smart Cities and IoT Applications Leveraging Edge AI 264
12.6.2 Autonomous Vehicles and Intelligent Transportation Systems 264
12.6.3 Healthcare, Industry 4.0, and Other Verticals Benefiting from Edge Intelligence 265
12.6.3.1 Healthcare 265
12.6.3.2 Industry 4.0 266
12.7 Future Directions and Emerging Trends 266
12.7.1 Predictions for the Evolution of Edge Intelligence beyond 6G 266
12.7.2 Integration of Quantum Computing, Blockchain, and Other Emerging Technologies with Edge AI 267
12.8 Conclusion 267
References 268
13 Securing the Hyper-Connected World: Security, Privacy and Research Challenges in IoT 271
Gagneet Kaur, Komal Singh, Pankaj Bhambri and Sandeep Kumar Singla
13.1 Introduction 272
13.1.1 Security Framework for Privacy & Security in a Hyper-Connected World 273
13.2 Security Attacks & Open Challenges 274
13.2.1 Smart Buildings 274
13.2.2 Healthcare Industry 275
13.3 Solutions & Security Architecture for Healthcare Industry 277
13.3.1 Confidentiality Risks 277
13.3.2 Availability Risks 278
13.3.3 Integrity Risks 278
13.4 Automotive IoT 278
13.4.1 Vulnerabilities 278
13.4.2 Safety Measures 279
13.5 Issues of Risks Arise in Key Security Principles of Security Architecture 280
13.6 Solutions for Issues of Risks Arise in Key Security Principles of Security Architecture 281
References 282
14 Edge-to-Cloud Synergy: Enhancing IIoT Capabilities 285
Cynthia Jayapal, K. Ulagapriya, K.V.M. Shree and A. Poonguzhali
14.1 Introduction 286
14.1.1 Foundations of Industrial IoT 287
14.1.1.1 Evolution of Industry IoT 288
14.1.1.2 Components of IIoT Ecosystem 288
14.1.1.3 Role of IIoT in Industrial Transformation 290
14.1.2 Understanding Edge Computing 292
14.1.2.1 Overview of Edge Computing 292
14.1.2.2 Need of Edge Computing for IIoT Applications 292
14.1.2.3 Operational Benefits of Edge Computing 293
14.1.2.4 Edge Computing Architectures 293
14.1.3 Cloud Computing 294
14.1.3.1 Overview of Cloud Computing 294
14.1.3.2 Cloud Services for Industrial Applications and Their Impact on IIoT 295
14.1.3.3 Benefits and Challenges of Cloud Integration 295
14.1.4 Synergizing Edge and Cloud Technologies 296
14.1.4.1 Conceptual Framework of Edge-to-Cloud Synergy 296
14.1.4.2 Integrating Edge and Cloud for Enhanced Performance 297
14.1.4.3 Achieving Optimal Balance in IoT Operations 298
14.1.5 Steps in Edge-to-Cloud Integration 299
14.1.5.1 Data Collection from Edge Devices 299
14.1.5.2 Data Filtering, Aggregation, and Compression 300
14.1.5.3 Edge Intelligence with Machine Learning Algorithms 301
14.1.5.4 Establishing Edge-Cloud Connectivity 302
14.1.5.5 Real-Time Monitoring and Control 303
14.1.5.6 Enabling Real-Time Decision-Making 304
14.1.6 6G Terahertz Communication Revolution 304
14.1.6.1 Introduction to 6G Terahertz Communication 304
14.1.6.2 Framework for Using Edge Intelligence in the 6G Industrial Internet of Things (IIoT) 305
14.1.6.3 Implications and Advantages in IIoT 306
14.1.6.4 Challenges and Solutions in Implementing Edge Intelligence for 6G IIoT 307
14.1.7 Digital Twins for Real-Time Monitoring 309
14.1.7.1 Digital Twins 309
14.1.7.2 Integration of Digital Twin and IIoT 309
14.1.7.3 Framework for Digital Twin in IIoT 310
14.1.8 Blockchain for Data Security and Integrity 312
14.1.8.1 Blockchain for IIoT Data Security and Integrity 312
14.1.8.2 Overview of Blockchain Technology 312
14.1.8.3 Need for Blockchain in IIoT 313
14.1.8.4 Smart Contract and DApp 313
14.1.8.5 Benefits of the Use of Blockchain in IIoT 314
14.1.9 Conclusion 314
14.1.9.1 Recapitulation of Key Findings 315
14.1.9.2 Future Trends and Emerging Technologies 315
References 317
15 Advancing Industrial Intelligence: Leveraging Optimized Edge Devices With Large Language Model Concepts 321
S. Sathishkumar, R. Devi Priya, K. Karthika and A. Menaka
15.1 Introduction 322
15.1.1 The Evolution of Industrial Intelligence 322
15.1.1.1 From Traditional Manufacturing to Industry 4.0 323
15.1.2 Understanding Edge Computing 323
15.1.2.1 Defining Edge Computing 323
15.1.2.2 The Conceptual Framework 324
15.1.2.3 Key Components and Architecture 324
15.1.3 Enabling Technologies 324
15.1.3.1 Internet of Things (IoT) in Industrial Context 325
15.1.3.2 Artificial Intelligence (AI) Paradigms 326
15.1.4 Challenges and Opportunities 328
15.1.4.1 Computational Resource Constraints 328
15.1.4.2 Security Considerations 330
15.1.5 Industrial Applications 331
15.1.5.1 Predictive Maintenance 331
15.1.5.2 Quality Control and Assurance 332
15.1.5.3 Supply Chain Management 332
15.2 Proposed Architecture/System for Industrial Edge Computing 333
15.2.1 Introduction 333
15.2.2 Key Components and Architecture 333
15.3 Conclusion 335
References 336
16 Advancing Edge Intelligence: The Role and Future in 6G Networks 339
L. Maria Suganthi, P. Senthamizh Pavai, K. Geetha and J. Vigneshwari
16.1 Introduction 340
16.2 What is 6G Networks? 340
16.3 Key Characteristics of 6G Networks 341
16.4 Technological Innovations Driving 6G 342
16.5 Challenges and Opportunities in 6G Development 344
16.6 Applications and Implications of 6G Networks 345
16.7 The Role of AI in 6G Networks 345
16.8 Security and Privacy Enhancements in 6G Networks 347
16.9 What is Edge Intelligence? 350
16.10 AI Chips for Edge Devices - Transforming Localized Processing and Intelligence 350
16.11 Edge Intelligence in 6G Networks 351
16.12 Key Components of Edge Intelligence in 6G Networks 352
16.13 The Role of Edge Intelligence in 6G Networks 353
16.14 Security and Privacy in Edge Intelligence 354
16.14.1 Introduction to Security and Privacy in Edge Intelligence 354
16.14.2 Threat Landscape for Edge Intelligence 354
16.14.3 AI-Driven Security Solutions for Edge Intelligence 354
16.14.4 Data Privacy Concerns and Solutions 355
16.14.5 Secure Edge Device Management 355
16.14.6 Encryption and Data Integrity 356
16.14.7 Zero Trust Architecture in Edge Networks 356
16.14.8 Blockchain for Enhanced Security and Privacy 356
16.14.9 Federated Learning and Collaborative AI 357
16.14.10 Case Studies: Security and Privacy Best Practices 357
16.14.11 Future Directions in Security and Privacy for Edge Intelligence 357
16.15 The Future of Edge Intelligence in 6G Networks 358
16.16 Advantages of Edge Intelligence 359
16.17 Challenges in Edge Intelligence 361
16.18 Conclusion 361
References 362
17 Optimizing Edge Devices for Industrial Intelligence 365
Tharun Satla, Srikanth Jannu, Pankaj Bhambri and Chaitanya Thuppari
17.1 Introduction 366
17.1.1 Overview of OOA 367
17.1.2 Organization 368
17.2 Related Work 368
17.3 System Models 369
17.3.1 Network Models 369
17.3.2 Energy Models 370
17.4 Proposed Work 370
17.4.1 OOA Based Cluster Head Selection 371
17.4.1.1 Initialization 371
17.4.1.2 Phase 1: Exploration 372
17.4.1.3 Phase 2: Exploitation 373
17.4.1.4 OOA Representation 374
17.4.2 Derivation of Fitness Functions 374
17.4.2.1 Sink Distance 374
17.4.2.2 Residual Energy 375
17.4.2.3 Intra-Cluster Distance 375
17.4.3 Cluster Formation 376
17.4.4 An Illustration 376
17.5 Simulation Results 379
17.5.1 Residual Energy 380
17.5.2 Network Lifetime 381
17.5.3 Number of Alive Nodes 381
17.6 Conclusion 382
Acknowledgement 383
References 383
18 6G Enabled Industrial Internet of Medical Things: Prospective, Development and Challenges 387
Meetali Chauhan and Sita Rani
18.1 Introduction 388
18.2 Literature Survey 390
18.3 6G Technology 392
18.4 Role of 6G Technology towards Healthcare 394
18.5 6G Based IIoMT Applications 396
18.5.1 Holographic Communication 396
18.5.2 Augmented Reality and Virtual Reality 397
18.5.3 Haptic Internet 397
18.5.4 Sample Reader Sensors 398
18.5.5 Intelligent Wearable Devices 398
18.5.6 Hospital to Home Services 398
18.5.7 Telesurgery 399
18.6 Challenges and Future Perspective 399
18.6.1 Challenges for 6G Technology 399
18.6.2 Future Perspective 400
18.7 Conclusion 402
References 402
Index 407
