Edge Intelligence for 6G-Enabled Industrial Internet of Things

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.

• Foreword xxiPreface xxiiiPart 1: Introduction, and Future Prospects to Edge Intelligence for 6G Enabled Industrial Internet of Things 11 Unveiling the 6G Landscape in Industrial IoT 3Sita Rani and Pankaj Bhambri1.1 Introduction 41.1.1 Evolution from 5G to 6G Technology 41.1.2 The Role of IoT in Industry 4.0 41.1.3 Importance of 6G in Enhancing Industrial IoT 61.2 Key Features of 6G Technology 61.2.1 Ultra-High Speeds 71.2.2 Ultra-Low Latency 71.2.3 Massive Connectivity 71.2.4 Advanced AI and Machine Learning Integration 71.2.5 Enhanced Reliability and Security 71.2.6 Energy Efficiency and Sustainability 71.2.7 Holographic Communication and Extended Reality (XR) 71.2.8 Global Coverage and Integration 81.2.9 Network Slicing and Customized Services 81.2.10 Quantum Communication and Computing 81.3 6G Use Cases in Industrial IoT 81.4 Challenges and Considerations in Deploying 6G for IIoT 111.5 Impact of 6G on Industry Standards and Protocols 131.6 Future Directions and Research Opportunities 151.7 Case Studies and Real-World Implementations 171.8 Conclusion 18References 192 Foundations of Edge Intelligence in 6G Networks 23D. Harika, C. Venkataramanan, K. Neelima and Satyam2.1 Introduction 242.2 Key Drivers and Goals of 6G Networks 242.3 Role of Distributed Intelligence in Overcoming Traditional Limitations 262.4 Fundamental Building Blocks of Edge Intelligence in 6G 292.5 Transformative Applications Enabled by Edge Intelligence 302.5.1 R1 - Sample Complexity 312.5.2 R2 - Reliable Prediction 312.5.3 R3 - Perception-Aware Prediction 312.5.4 R4 - Multimodal Fusion 312.5.5 R5 - Beyond Visual Modality 312.5.6 R6 - Non-RF Overhead 322.5.7 R7 - Controller Connectivity 322.5.8 R8 - Stable Control 322.5.9 R9 - Scalable Control 322.6 Challenges and Enablers of Edge Intelligence 332.7 Conclusion 36References 363 Advancements in Industrial Connectivity: A 6G Perspective 39Kali Charan Rath, Nagavarapu Sowmya, Aditi Sharma and Brojo Kishore Mishra3.1 Introduction 403.2 Smart Manufacturing and Communication 413.2.1 Comparison between 5G and 6G Network 423.2.2 6G Technology and Importance for Implementation 423.2.3 6G Technology and Its Significance 433.3 Manufacturing Processes Enhancement through 6G Networks 453.3.1 Case Study of Smart Manufacturing Technologies with 6G 463.4 Smart Auto Manufacturing Powered by 6G: A Case Study 483.4.1 Integration of 6G Connectivity, AI, IoT, and Edge Computing in Automobile Smart Manufacturing Optimizes Processes 513.4.2 Algorithm for Real-Time Monitoring and Control of Factory Machines and Processes (Predictive Maintenance) with the Application of 6G 543.5 Challenges and Obstacles in the Adoption of 6G Networks in Industrial Connectivity 563.6 Conclusion 633.6.1 Future Scope of Work 63References 644 Security Paradigm for 6G-Enabled IIoT Ecosystems 67Rachna Rana and Pankaj Bhambri4.1 Introduction 684.2 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)? 744.3 Why is Security Relevant to IIoT? 744.3.1 Protection of Systems 754.3.2 Information Protection 754.3.3 Crime Prevention 754.3.4 Cost Savings 754.3.5 Enhanced Productivity 754.4 Which Technologies Underpin IIoT Security? 754.4.1 Devices and Sensors 754.4.2 Encryption of Data 764.4.3 Authentication 764.4.4 These Security Measures Keep an Eye on the Digital World 764.4.5 Updates and Patches 764.4.6 Remote Monitoring 764.4.7 Environmental Response 764.4.8 Behavioral Analysis 764.4.9 Machine Learning 774.4.10 Redundancy 774.4.11 Periodic Audits 774.5 Why are IIoT Security Standards Needed? 774.6 What Steps Can Network Administrators and CISOs Take to Secure Their Networks and Devices? 774.6.1 Byos Secure Gateway Edge has the Following Advantages 784.7 What Makes IIoT Security Different from IoT Security? 784.8 Security Benefits of IIoT 784.8.1 Data Security 784.8.2 Stops Interruptions 804.8.3 Guarantees Security 804.8.4 Preserves Credibility 804.8.5 Privacy-Protecting 804.8.6 Stops Unauthorized Entry 814.8.7 Protects Vital Infrastructure 814.8.8 Lowers Danger 814.9 Case Study 1: Agricultural Cost Reduction 814.10 Conclusion and Future Scope 824.10.1 Advanced Threat Protection 824.10.2 Real-Time Monitoring 824.10.3 Advances in Encryption 824.10.4 Scalable Solutions 824.10.5 User-Friendly Interfaces 824.10.6 Combining Machine Learning and Artificial Intelligence 834.10.7 Assurance of Compliance 83References 835 Machine Learning Dynamics in 6G Industrial Environments 85Naina Agrawal, J. Jayashree and J. Vijayashree5.1 Introduction 865.2 Foundations of 6G Technology 905.2.1 Overview of 6G Capabilities 905.2.2 Integration of AI and Machine Learning into 6G Networks 905.2.3 Key Features Making 6G Suitable for Industrial Applications 925.3 Machine Learning Algorithms in Industrial Environments 925.3.1 Exploration of Machine Learning Algorithms 925.3.2 Real-World Applications of Machine Learning 935.3.3 Case Studies Illustrating Machine Learning Success Stories 945.4 Real-Time Data Processing and Edge Computing 965.4.1 Significance of Real-Time Data Processing 965.4.2 Role of Edge Computing in Industrial Environments 975.4.3 Diagrams Illustrating 6G-Enabled Industrial System with Edge Computing 985.5 Predictive Maintenance and Fault Detection 1025.5.1 Utilizing Machine Learning for Predictive Maintenance 1025.5.2 Fault Detection Algorithms for Industrial Processes 1035.5.3 Case Studies Showcasing Predictive Maintenance Success Stories 1055.6 Autonomous Systems and Robotics 1065.6.1 Integration of Machine Learning into Autonomous Systems 1065.6.2 Robotics Empowered by 6G Connectivity and Machine Learning 1085.6.3 Diagrams Illustrating Communication Network in 6G-Enabled Autonomous Systems 1105.7 Security and Privacy Concerns 1135.7.1 Addressing Security Challenges in 6G-Enabled Industrial Environments 1135.7.2 Privacy Considerations in Machine Learning Applications 1145.7.3 Strategies for Ensuring Data Security and Privacy 1155.8 Conclusion 1165.9 Future Prospects 116References 1176 Wireless Infrastructure for Robust 6G IIoT Connectivity 121Boudhayan Bhattacharya and Arpan Kisore Sarbadhikari6.1 Introduction 1226.2 Key Features and Expectations of 6G Technology 1236.3 Unique Requirements of IIoT Applications 1246.4 Wireless Infrastructure Components for IIoT 1246.4.1 Edge Computing 1246.4.1.1 Key Concepts and Architecture 1256.4.1.2 Key Benefits 1256.4.2 Architecture: Fog Layers and Nodes 1276.4.2.1 Key Concepts and Architecture 1276.4.2.2 Key Benefits: Key Benefits for IIoT Include 1286.5 Advanced Communication Protocols 1296.5.1 Edge 5G NR (New Radio) 1296.5.1.1 Key Features of 5G NR 1296.5.1.2 Deployment and Implementation 1306.5.2 Time-Sensitive Networking (TSN) 1316.5.2.1 Key Features of TSN 1316.5.2.2 Deployment & Implementation 1326.5.3 Low Power Wide Area Networks (LPWANs) 1346.5.3.1 Key Features of LPWAN 1346.5.3.2 Deployment and Implementation 1356.5.3.3 Common LPWAN Technologies 1386.6 Practical Use Cases and Industry Examples 1396.6.1 Predictive Maintenance 1396.6.2 Smart Manufacturing 1396.6.3 Supply Chain Optimization 1396.7 Integration of 6G Capabilities 1406.7.1 Faster Data Transmission 1406.7.2 Improved Network Reliability 1406.7.3 Enhanced Security Measures 1406.8 Coexistence and Interoperability 1406.8.1 Coexistence of Multiple Wireless Technologies 1406.8.2 Interoperability Challenges 1406.8.3 Importance of Standardization 1416.9 Conclusion 141References 1417 Future Horizons: Emerging Trends in Edge Intelligence for IIoT 143J. Vigneshwari, K. Geetha, P. Senthamizh Pavai and L. Maria Suganthi7.1 Introduction- An Outline on IIoT 1447.2 Significance of IIoT 1457.2.1 IIoT vs IoT 1467.3 Future of IIoT 1477.4 Edge Intelligence 1497.4.1 Edge AI for Autonomous Decision-Making 1497.4.2 Artificial Intelligence (AI) and Machine Learning (ML) 1517.5 The 4.0 Technology 1527.5.1 The 4.0 Solution 1527.6 Challenges and Considerations for Adopting IIoT Trends 1537.7 6G and Future Horizons 1557.8 Benefits of Investing in IIoT 1567.8.1 Planning and Implementation of IIoT 1577.9 Conclusion 158References 159Part 2: Advances and Applications of Edge Intelligence for 6G Enabled Industrial Internet of Things 1638 Connecting the 6G Autonomous Worlds with Real Time Edge Intelligence (Autonomous Vehicle) 165Hemant Kumar Saini8.1 Introduction 1668.2 Evolutions 1688.2.1 1G Communication 1688.2.2 2G Communication 1698.2.3 3G Communication 1698.2.4 4G Communication 1708.2.5 5G Generation 1708.2.6 6G Communication 1718.3 Issues in 6G Edges 1718.4 6G with Edge 1738.5 Edge Intelligence with Autonomous Vehicle 1758.6 Forthcoming Edge Driven AI Based 6G in Autonomous Vehicular Applications 1768.7 Future Perspective of Edge Intelligence in Vehicles 177References 1789 Performance Improvement of 6G Internet of Things Using Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 181B.N. Chandrashekhar and V. Geetha9.1 Introduction 1829.1.1 Edge Computing with AI 1829.1.2 HPC Infrastructure 1839.1.2.1 Multicore Architecture 1849.1.2.2 Many-Core Architecture 1859.1.2.3 Hybrid [CPU+GPU] Architecture 1869.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 1879.2.1 Overview of Converged HPC Infrastructure and Edge AI 1889.2.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI 1909.2.3 Innovation in 6G IOT 1919.3 Performance Optimization 1939.3.1 AI-Based Intra-Node and Internode Communication on CPUs and GPUs-Based HPC Infrastructure 1939.3.2 Optimal Workload Distribution 1949.3.3 Evaluation of Performance 196References 19610 Embedding Privacy into Industrial IoT System 199N. Ambika10.1 Introduction 20010.2 Background 20610.3 Literature Survey 20710.4 Previous System 20910.5 Proposed System 21010.6 Analysis of the Work 21210.7 Simulation 21310.8 Future Scope 21410.9 Conclusion 214References 21511 Exploring Novel Directions in Edge Intelligence for Industrial Internet of Things (IIoT) 217T. Thangarasan, R. Keerthana, J. Nagaraj, S. Vani and R.M. Dilip Charaan11.1 Introduction to the Internet of Things 21811.1.1 Key Components of IoT 21811.1.2 Applications of IoT 21811.1.3 Challenges of IoT 21911.2 Industrial Internet of Things (IIoT) 21911.2.1 Key Components of IIoT 21911.2.2 Applications of IIoT 22011.2.3 Benefits of IIoT 22111.2.4 Challenges of IIoT 22111.3 Decentralized Edge Intelligence Ecosystems 22111.3.1 Components 22211.3.2 Benefits 22211.3.3 Real-Time Anomaly Detection and Predictive Maintenance 22311.3.3.1 Real-Time Anomaly Detection 22311.3.3.2 Technologies Used 22311.3.3.3 Predictive Maintenance 22411.3.4 Benefits 22411.3.5 Challenges 22411.3.6 Applications 22511.4 Federated Learning for Edge Devices 22511.4.1 Key Concepts 22511.4.2 Benefits 22611.4.3 Challenges 22611.4.4 Applications 22611.4.5 How it Works 22711.4.6 Example Workflow 22711.4.7 Key Algorithms 22711.4.8 Technical Considerations 22711.5 Energy-Efficient Edge Computing 22811.5.1 Key Strategies 22811.5.2 Technologies and Techniques 22911.5.3 Benefits 22911.5.4 Challenges 23011.5.5 Applications 23011.5.6 Example Approaches 23111.6 Integration of Augmented Reality (AR) and Virtual Reality (VR) 23111.6.1 Key Concepts 23111.6.2 Integration of AR and VR 23211.6.3 Applications 23211.6.4 Benefits 23311.6.5 Challenges 23311.6.6 Future Trends 23411.7 Edge-Based Data Fusion 23411.7.1 Key Components 23411.7.2 Applications 23511.7.3 Benefits 23611.7.4 Challenges 23611.7.5 Implementation Strategies 23711.7.6 Future Trends 23711.8 Distributed Edge Intelligence Marketplaces 23811.8.1 Key Concepts 23811.8.2 Components 23811.8.3 Benefits 23911.8.4 Challenges 23911.8.5 Potential Applications 24011.8.6 Implementation Strategies 24011.8.7 Future Trends 24111.9 Edge-to-Cloud Orchestration 24211.9.1 Key Components 24211.9.2 Benefits 24311.9.3 Challenges 24311.9.4 Use Cases 24411.9.5 Implementation Strategies 24511.9.6 Future Trends 24511.10 Conclusion 246References 24712 6G Network: Integrating Wireless Networks and Machine Learning for Connected Edge Intelligence 249B. Prabha, V. Praveen and M.R. Santhoosh12.1 Introduction 25012.1.1 Definition and Importance of Edge Intelligence in the 6G Context 25012.2 Evolution of Wireless Networks for Edge Intelligence 25212.2.1 Historical Perspective: From 1G to 6G and the Evolution of Edge Computing 25212.2.2 Key Technological Advancements Enabling Edge Intelligence in 6G Networks 25312.3 Challenges in Integrating AI with Wireless Networks 25512.3.1 Latency and Real-Time Processing Requirements 25512.3.2 Energy Efficiency and Resource Optimization 25612.3.3 Privacy and Security Concerns in Edge AI Systems 25612.4 Machine Learning Models for Edge Computing 25712.4.1 Overview of Decentralized Machine Learning Algorithms 25712.4.2 Model Compression and Optimization Techniques for Edge Devices 25812.4.3 Federated Learning and Collaborative Intelligence at the Edge 25912.5 Design Principles for Edge AI Systems in 6G 26012.5.1 Scalable Architecture for Edge AI Deployment 26012.5.2 Service-Driven Resource Allocation and Management 26112.5.3 Edge-to-Cloud Continuum: Balancing Computation between Edge and Central Servers 26312.6 Applications and Use Cases of Edge Intelligence in 6G Networks 26312.6.1 Smart Cities and IoT Applications Leveraging Edge AI 26412.6.2 Autonomous Vehicles and Intelligent Transportation Systems 26412.6.3 Healthcare, Industry 4.0, and Other Verticals Benefiting from Edge Intelligence 26512.6.3.1 Healthcare 26512.6.3.2 Industry 4.0 26612.7 Future Directions and Emerging Trends 26612.7.1 Predictions for the Evolution of Edge Intelligence beyond 6G 26612.7.2 Integration of Quantum Computing, Blockchain, and Other Emerging Technologies with Edge AI 26712.8 Conclusion 267References 26813 Securing the Hyper-Connected World: Security, Privacy and Research Challenges in IoT 271Gagneet Kaur, Komal Singh, Pankaj Bhambri and Sandeep Kumar Singla13.1 Introduction 27213.1.1 Security Framework for Privacy & Security in a Hyper-Connected World 27313.2 Security Attacks & Open Challenges 27413.2.1 Smart Buildings 27413.2.2 Healthcare Industry 27513.3 Solutions & Security Architecture for Healthcare Industry 27713.3.1 Confidentiality Risks 27713.3.2 Availability Risks 27813.3.3 Integrity Risks 27813.4 Automotive IoT 27813.4.1 Vulnerabilities 27813.4.2 Safety Measures 27913.5 Issues of Risks Arise in Key Security Principles of Security Architecture 28013.6 Solutions for Issues of Risks Arise in Key Security Principles of Security Architecture 281References 28214 Edge-to-Cloud Synergy: Enhancing IIoT Capabilities 285Cynthia Jayapal, K. Ulagapriya, K.V.M. Shree and A. Poonguzhali14.1 Introduction 28614.1.1 Foundations of Industrial IoT 28714.1.1.1 Evolution of Industry IoT 28814.1.1.2 Components of IIoT Ecosystem 28814.1.1.3 Role of IIoT in Industrial Transformation 29014.1.2 Understanding Edge Computing 29214.1.2.1 Overview of Edge Computing 29214.1.2.2 Need of Edge Computing for IIoT Applications 29214.1.2.3 Operational Benefits of Edge Computing 29314.1.2.4 Edge Computing Architectures 29314.1.3 Cloud Computing 29414.1.3.1 Overview of Cloud Computing 29414.1.3.2 Cloud Services for Industrial Applications and Their Impact on IIoT 29514.1.3.3 Benefits and Challenges of Cloud Integration 29514.1.4 Synergizing Edge and Cloud Technologies 29614.1.4.1 Conceptual Framework of Edge-to-Cloud Synergy 29614.1.4.2 Integrating Edge and Cloud for Enhanced Performance 29714.1.4.3 Achieving Optimal Balance in IoT Operations 29814.1.5 Steps in Edge-to-Cloud Integration 29914.1.5.1 Data Collection from Edge Devices 29914.1.5.2 Data Filtering, Aggregation, and Compression 30014.1.5.3 Edge Intelligence with Machine Learning Algorithms 30114.1.5.4 Establishing Edge-Cloud Connectivity 30214.1.5.5 Real-Time Monitoring and Control 30314.1.5.6 Enabling Real-Time Decision-Making 30414.1.6 6G Terahertz Communication Revolution 30414.1.6.1 Introduction to 6G Terahertz Communication 30414.1.6.2 Framework for Using Edge Intelligence in the 6G Industrial Internet of Things (IIoT) 30514.1.6.3 Implications and Advantages in IIoT 30614.1.6.4 Challenges and Solutions in Implementing Edge Intelligence for 6G IIoT 30714.1.7 Digital Twins for Real-Time Monitoring 30914.1.7.1 Digital Twins 30914.1.7.2 Integration of Digital Twin and IIoT 30914.1.7.3 Framework for Digital Twin in IIoT 31014.1.8 Blockchain for Data Security and Integrity 31214.1.8.1 Blockchain for IIoT Data Security and Integrity 31214.1.8.2 Overview of Blockchain Technology 31214.1.8.3 Need for Blockchain in IIoT 31314.1.8.4 Smart Contract and DApp 31314.1.8.5 Benefits of the Use of Blockchain in IIoT 31414.1.9 Conclusion 31414.1.9.1 Recapitulation of Key Findings 31514.1.9.2 Future Trends and Emerging Technologies 315References 31715 Advancing Industrial Intelligence: Leveraging Optimized Edge Devices With Large Language Model Concepts 321S. Sathishkumar, R. Devi Priya, K. Karthika and A. Menaka15.1 Introduction 32215.1.1 The Evolution of Industrial Intelligence 32215.1.1.1 From Traditional Manufacturing to Industry 4.0 32315.1.2 Understanding Edge Computing 32315.1.2.1 Defining Edge Computing 32315.1.2.2 The Conceptual Framework 32415.1.2.3 Key Components and Architecture 32415.1.3 Enabling Technologies 32415.1.3.1 Internet of Things (IoT) in Industrial Context 32515.1.3.2 Artificial Intelligence (AI) Paradigms 32615.1.4 Challenges and Opportunities 32815.1.4.1 Computational Resource Constraints 32815.1.4.2 Security Considerations 33015.1.5 Industrial Applications 33115.1.5.1 Predictive Maintenance 33115.1.5.2 Quality Control and Assurance 33215.1.5.3 Supply Chain Management 33215.2 Proposed Architecture/System for Industrial Edge Computing 33315.2.1 Introduction 33315.2.2 Key Components and Architecture 33315.3 Conclusion 335References 33616 Advancing Edge Intelligence: The Role and Future in 6G Networks 339L. Maria Suganthi, P. Senthamizh Pavai, K. Geetha and J. Vigneshwari16.1 Introduction 34016.2 What is 6G Networks? 34016.3 Key Characteristics of 6G Networks 34116.4 Technological Innovations Driving 6G 34216.5 Challenges and Opportunities in 6G Development 34416.6 Applications and Implications of 6G Networks 34516.7 The Role of AI in 6G Networks 34516.8 Security and Privacy Enhancements in 6G Networks 34716.9 What is Edge Intelligence? 35016.10 AI Chips for Edge Devices - Transforming Localized Processing and Intelligence 35016.11 Edge Intelligence in 6G Networks 35116.12 Key Components of Edge Intelligence in 6G Networks 35216.13 The Role of Edge Intelligence in 6G Networks 35316.14 Security and Privacy in Edge Intelligence 35416.14.1 Introduction to Security and Privacy in Edge Intelligence 35416.14.2 Threat Landscape for Edge Intelligence 35416.14.3 AI-Driven Security Solutions for Edge Intelligence 35416.14.4 Data Privacy Concerns and Solutions 35516.14.5 Secure Edge Device Management 35516.14.6 Encryption and Data Integrity 35616.14.7 Zero Trust Architecture in Edge Networks 35616.14.8 Blockchain for Enhanced Security and Privacy 35616.14.9 Federated Learning and Collaborative AI 35716.14.10 Case Studies: Security and Privacy Best Practices 35716.14.11 Future Directions in Security and Privacy for Edge Intelligence 35716.15 The Future of Edge Intelligence in 6G Networks 35816.16 Advantages of Edge Intelligence 35916.17 Challenges in Edge Intelligence 36116.18 Conclusion 361References 36217 Optimizing Edge Devices for Industrial Intelligence 365Tharun Satla, Srikanth Jannu, Pankaj Bhambri and Chaitanya Thuppari17.1 Introduction 36617.1.1 Overview of OOA 36717.1.2 Organization 36817.2 Related Work 36817.3 System Models 36917.3.1 Network Models 36917.3.2 Energy Models 37017.4 Proposed Work 37017.4.1 OOA Based Cluster Head Selection 37117.4.1.1 Initialization 37117.4.1.2 Phase 1: Exploration 37217.4.1.3 Phase 2: Exploitation 37317.4.1.4 OOA Representation 37417.4.2 Derivation of Fitness Functions 37417.4.2.1 Sink Distance 37417.4.2.2 Residual Energy 37517.4.2.3 Intra-Cluster Distance 37517.4.3 Cluster Formation 37617.4.4 An Illustration 37617.5 Simulation Results 37917.5.1 Residual Energy 38017.5.2 Network Lifetime 38117.5.3 Number of Alive Nodes 38117.6 Conclusion 382Acknowledgement 383References 38318 6G Enabled Industrial Internet of Medical Things: Prospective, Development and Challenges 387Meetali Chauhan and Sita Rani18.1 Introduction 38818.2 Literature Survey 39018.3 6G Technology 39218.4 Role of 6G Technology towards Healthcare 39418.5 6G Based IIoMT Applications 39618.5.1 Holographic Communication 39618.5.2 Augmented Reality and Virtual Reality 39718.5.3 Haptic Internet 39718.5.4 Sample Reader Sensors 39818.5.5 Intelligent Wearable Devices 39818.5.6 Hospital to Home Services 39818.5.7 Telesurgery 39918.6 Challenges and Future Perspective 39918.6.1 Challenges for 6G Technology 39918.6.2 Future Perspective 40018.7 Conclusion 402References 402Index 407