Overview
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.
Full Product Details
Author: Sita Rani (Guru Nanak Dev Engineering College, Ludhiana, Punjab, India) , Pankaj Bhambri (Guru Nanak Dev Engineering College, Ludhiana, Punjab, India) , Balamurugan Balusamy (Manipal Academy of Higher Education, Dubai Campus, Dubai) , Rishabha Malviya (Galgotias University, Greater Noida, Uttar Pradesh, India)
Publisher: John Wiley & Sons Inc
Imprint: Wiley-Scrivener
ISBN: 9781394305384
ISBN 10: 1394305389
Pages: 448
Publication Date: 03 June 2026
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Out of stock 
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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
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Sita Rani, PhD is a Professor at Guru Nanak Dev Engineering College, Ludhiana, Punjab, India with more than 20 years of experience. He has published more than 20 articles in international journals and conferences and holds five patents. Pankaj Bhambri, PhD is an Assistant Professor in the Information Technology Department at Guru Nanak Dev Engineering College, Ludhiana, Punjab, India with more than 19 years of teaching and research experience. He has more than 70 publications to his credit. Balamurugan Balusamy, PhD is at the School of Engineering and IT, Manipal Academy of Higher Education, Dubai Campus, Dubai, He has published more than 200 articles in international journals and conferences and more than 80 books. Rishabha Malviya, PhD is a Professor at Galgotias University, Greater Noida, Uttar Pradesh, India with more than 15 years of experience in pharmaceutical science. He has more than 200 publications to his credit and holds 58 patents. Seifedine Kadry, PhD is a Professor in the Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon. He has more than 200 publications and 12 authored books in computing, software engineering, and systems reliability. He serves as Editor-in-Chief of two journals and is a senior member of IEEE.
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