Search

Browse Subject Areas

For Authors

Submit a Proposal

Edge Intelligence for 6G-Enabled Industrial Internet of Things

Edited by Sita Rani, Pankaj Bhambri, Balamurugan Balusamy, Rishabha Malviya and Seifedine Kadry
Copyright: 2026   |   Expected Pub Date:2026/3/30
ISBN: 9781394305384  |  Hardcover  |  
438 pages

One Line 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.

Audience
Engineers, data scientists, researchers, and technology professionals who are involved in industrial IoT, edge computing, and emerging 6G technologies.

Description
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.

Back to Top
Author / Editor Details
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.

Back to Top

Table of Contents
Foreword
Preface
Part 1: Introduction, and Future Prospects to Edge Intelligence for 6G Enabled Industrial Internet of Things
1. Unveiling the 6G Landscape in Industrial IoT
Sita Rani and Pankaj Bhambri
1.1 Introduction
1.1.1 Evolution from 5G to 6G Technology
1.1.2 The Role of IoT in Industry 4.0
1.1.3 Importance of 6G in Enhancing Industrial IoT
1.2 Key Features of 6G Technology
1.2.1 Ultra-High Speeds
1.2.2 Ultra-Low Latency
1.2.3 Massive Connectivity
1.2.4 Advanced AI and Machine Learning Integration
1.2.5 Enhanced Reliability and Security
1.2.6 Energy Efficiency and Sustainability
1.2.7 Holographic Communication and Extended Reality (XR)
1.2.8 Global Coverage and Integration
1.2.9 Network Slicing and Customized Services
1.2.10 Quantum Communication and Computing
1.3 6G Use Cases in Industrial IoT
1.4 Challenges and Considerations in Deploying 6G for IIoT
1.5 Impact of 6G on Industry Standards and Protocols
1.6 Future Directions and Research Opportunities
1.7 Case Studies and Real-World Implementations
1.8 Conclusion
References
2. Foundations of Edge Intelligence in 6G Networks
D. Harika, C. Venkataramanan, K. Neelima and Satyam
2.1 Introduction
2.2 Key Drivers and Goals of 6G Networks
2.3 Role of Distributed Intelligence in Overcoming Traditional Limitations
2.4 Fundamental Building Blocks of Edge Intelligence in 6G
2.5 Transformative Applications Enabled by Edge Intelligence
2.5.1 R1 - Sample Complexity
2.5.2 R2 - Reliable Prediction
2.5.3 R3 - Perception-Aware Prediction
2.5.4 R4 - Multimodal Fusion
2.5.5 R5 - Beyond Visual Modality
2.5.6 R6 - Non-RF Overhead
2.5.7 R7 - Controller Connectivity
2.5.8 R8 - Stable Control
2.5.9 R9 - Scalable Control
2.6 Challenges and Enablers of Edge Intelligence
2.7 Conclusion
References
3 Advancements in Industrial Connectivity: A 6G Perspective
Kali Charan Rath, Nagavarapu Sowmya, Aditi Sharma and Brojo Kishore Mishra
3.1 Introduction
3.2 Smart Manufacturing and Communication
3.2.1 Comparison between 5G and 6G Network
3.2.2 6G Technology and Importance for Implementation
3.2.3 6G Technology and Its Significance
3.3 Manufacturing Processes Enhancement through 6G Networks
3.3.1 Case Study of Smart Manufacturing Technologies with 6G
3.4 Smart Auto Manufacturing Powered by 6G: A Case Study
3.4.1 Integration of 6G Connectivity, AI, IoT, and Edge Computing in Automobile Smart Manufacturing Optimizes Processes
3.4.2 Algorithm for Real-Time Monitoring and Control of Factory Machines and Processes (Predictive Maintenance) with the Application of 6G
3.5 Challenges and Obstacles in the Adoption of 6G Networks in Industrial Connectivity
3.6 Conclusion
3.6.1 Future Scope of Work
References
4. Security Paradigm for 6G-Enabled IIoT Ecosystems
Rachna Rana and Pankaj Bhambri
4.1 Introduction
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)?
4.3 Why is Security Relevant to IIoT?
4.3.1 Protection of Systems
4.3.2 Information Protection
4.3.3 Crime Prevention
4.3.4 Cost Savings
4.3.5 Enhanced Productivity
4.4 Which Technologies Underpin IIoT Security?
4.4.1 Devices and Sensors
4.4.2 Encryption of Data
4.4.3 Authentication
4.4.4 These Security Measures Keep an Eye on the Digital World
4.4.5 Updates and Patches
4.4.6 Remote Monitoring
4.4.7 Environmental Response
4.4.8 Behavioral Analysis
4.4.9 Machine Learning
4.4.10 Redundancy
4.4.11 Periodic Audits
4.5 Why are IIoT Security Standards Needed?
4.6 What Steps Can Network Administrators and CISOs Take to Secure Their Networks and Devices?
4.6.1 Byos Secure Gateway Edge has the Following Advantages
4.7 What Makes IIoT Security Different from IoT Security?
4.8 Security Benefits of IIoT
4.8.1 Data Security
4.8.2 Stops Interruptions
4.8.3 Guarantees Security
4.8.4 Preserves Credibility
4.8.5 Privacy-Protecting
4.8.6 Stops Unauthorized Entry
4.8.7 Protects Vital Infrastructure
4.8.8 Lowers Danger
4.9 Case Study 1: Agricultural Cost Reduction
4.10 Conclusion and Future Scope
4.10.1 Advanced Threat Protection
4.10.2 Real-Time Monitoring
4.10.3 Advances in Encryption
4.10.4 Scalable Solutions
4.10.5 User-Friendly Interfaces
4.10.6 Combining Machine Learning and Artificial Intelligence
4.10.7 Assurance of Compliance
References
5. Machine Learning Dynamics in 6G Industrial Environments
Naina Agrawal, J. Jayashree and J. Vijayashree
5.1 Introduction
5.2 Foundations of 6G Technology
5.2.1 Overview of 6G Capabilities
5.2.2 Integration of AI and Machine Learning into 6G Networks
5.2.3 Key Features Making 6G Suitable for Industrial Applications
5.3 Machine Learning Algorithms in Industrial Environments
5.3.1 Exploration of Machine Learning Algorithms
5.3.2 Real-World Applications of Machine Learning
5.3.3 Case Studies Illustrating Machine Learning Success Stories
5.4 Real-Time Data Processing and Edge Computing
5.4.1 Significance of Real-Time Data Processing
5.4.2 Role of Edge Computing in Industrial Environments
5.4.3 Diagrams Illustrating 6G-Enabled Industrial System with Edge Computing
5.5 Predictive Maintenance and Fault Detection
5.5.1 Utilizing Machine Learning for Predictive Maintenance
5.5.2 Fault Detection Algorithms for Industrial Processes
5.5.3 Case Studies Showcasing Predictive Maintenance Success Stories
5.6 Autonomous Systems and Robotics
5.6.1 Integration of Machine Learning into Autonomous Systems
5.6.2 Robotics Empowered by 6G Connectivity and Machine Learning
5.6.3 Diagrams Illustrating Communication Network in 6G-Enabled Autonomous Systems
5.7 Security and Privacy Concerns
5.7.1 Addressing Security Challenges in 6G-Enabled Industrial Environments
5.7.2 Privacy Considerations in Machine Learning Applications
5.7.3 Strategies for Ensuring Data Security and Privacy
5.8 Conclusion
5.9 Future Prospects
References
6. Wireless Infrastructure for Robust 6G IIoT Connectivity
Boudhayan Bhattacharya and Arpan Kisore Sarbadhikari
6.1 Introduction
6.2 Key Features and Expectations of 6G Technology
6.3 Unique Requirements of IIoT Applications
6.4 Wireless Infrastructure Components for IIoT
6.4.1 Edge Computing
6.4.1.1 Key Concepts and Architecture
6.4.1.2 Key Benefits
6.4.2 Architecture: Fog Layers and Nodes
6.4.2.1 Key Concepts and Architecture
6.4.2.2 Key Benefits: Key Benefits for IIoT Include
6.5 Advanced Communication Protocols
6.5.1 Edge 5G NR (New Radio)
6.5.1.1 Key Features of 5G NR
6.5.1.2 Deployment and Implementation
6.5.2 Time-Sensitive Networking (TSN)
6.5.2.1 Key Features of TSN
6.5.2.2 Deployment & Implementation
6.5.3 Low Power Wide Area Networks (LPWANs)
6.5.3.1 Key Features of LPWAN
6.5.3.2 Deployment and Implementation
6.5.3.3 Common LPWAN Technologies
6.6 Practical Use Cases and Industry Examples
6.6.1 Predictive Maintenance
6.6.2 Smart Manufacturing
6.6.3 Supply Chain Optimization
6.7 Integration of 6G Capabilities
6.7.1 Faster Data Transmission
6.7.2 Improved Network Reliability
6.7.3 Enhanced Security Measures
6.8 Coexistence and Interoperability
6.8.1 Coexistence of Multiple Wireless Technologies
6.8.2 Interoperability Challenges
6.8.3 Importance of Standardization
6.9 Conclusion
References
7. Future Horizons: Emerging Trends in Edge Intelligence for IIoT
J. Vigneshwari, K. Geetha, P. Senthamizh Pavai and L. Maria Suganthi
7.1 Introduction- An Outline on IIoT
7.2 Significance of IIoT
7.2.1 IIoT vs IoT
7.3 Future of IIoT
7.4 Edge Intelligence
7.4.1 Edge AI for Autonomous Decision-Making
7.4.2 Artificial Intelligence (AI) and Machine Learning (ML)
7.5 The 4.0 Technology
7.5.1 The 4.0 Solution
7.6 Challenges and Considerations for Adopting IIoT Trends
7.7 6G and Future Horizons
7.8 Benefits of Investing in IIoT
7.8.1 Planning and Implementation of IIoT
7.9 Conclusion
References
Part 2: Advances and Applications of Edge Intelligence for 6G Enabled Industrial Internet of Things
8. Connecting the 6G Autonomous Worlds with Real Time Edge Intelligence (Autonomous Vehicle)
Hemant Kumar Saini
8.1 Introduction
8.2 Evolutions
8.2.1 1G Communication
8.2.2 2G Communication
8.2.3 3G Communication
8.2.4 4G Communication
8.2.5 5G Generation
8.2.6 6G Communication
8.3 Issues in 6G Edges
8.4 6G with Edge
8.5 Edge Intelligence with Autonomous Vehicle
8.6 Forthcoming Edge Driven AI Based 6G in Autonomous Vehicular Applications
8.7 Future Perspective of Edge Intelligence in Vehicles
References
9. Performance Improvement of 6G Internet of Things Using Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI
B.N. Chandrashekhar and V. Geetha
9.1 Introduction
9.1.1 Edge Computing with AI
9.1.2 HPC Infrastructure
9.1.2.1 Multicore Architecture
9.1.2.2 Many-Core Architecture
9.1.2.3 Hybrid [CPU+GPU] Architecture
9.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI
9.2.1 Overview of Converged HPC Infrastructure and Edge AI
9.2.2 Proposed Converged Super Hybrid [CPU+GPU] HPC Infrastructure and Edge AI
9.2.3 Innovation in 6G IOT
9.3 Performance Optimization
9.3.1 AI-Based Intra-Node and Internode Communication on CPUs and GPUs-Based HPC Infrastructure
9.3.2 Optimal Workload Distribution
9.3.3 Evaluation of Performance
References
10. Embedding Privacy into Industrial IoT System
N. Ambika
10.1 Introduction
10.2 Background
10.3 Literature Survey
10.4 Previous System
10.5 Proposed System
10.6 Analysis of the Work
10.7 Simulation
10.8 Future Scope
10.9 Conclusion
References
11. Exploring Novel Directions in Edge Intelligence for Industrial Internet of Things (IIoT)
T. Thangarasan, R. Keerthana, J. Nagaraj, S. Vani and R.M. Dilip Charaan
11.1 Introduction to the Internet of Things
11.1.1 Key Components of IoT
11.1.2 Applications of IoT
11.1.3 Challenges of IoT
11.2 Industrial Internet of Things (IIoT)
11.2.1 Key Components of IIoT
11.2.2 Applications of IIoT
11.2.3 Benefits of IIoT
11.2.4 Challenges of IIoT
11.3 Decentralized Edge Intelligence Ecosystems
11.3.1 Components
11.3.2 Benefits
11.3.3 Real-Time Anomaly Detection and Predictive Maintenance
11.3.3.1 Real-Time Anomaly Detection
11.3.3.2 Technologies Used
11.3.3.3 Predictive Maintenance
11.3.4 Benefits
11.3.5 Challenges
11.3.6 Applications
11.4 Federated Learning for Edge Devices
11.4.1 Key Concepts
11.4.2 Benefits
11.4.3 Challenges
11.4.4 Applications
11.4.5 How it Works
11.4.6 Example Workflow
11.4.7 Key Algorithms
11.4.8 Technical Considerations
11.5 Energy-Efficient Edge Computing
11.5.1 Key Strategies
11.5.2 Technologies and Techniques
11.5.3 Benefits
11.5.4 Challenges
11.5.5 Applications
11.5.6 Example Approaches
11.6 Integration of Augmented Reality (AR) and Virtual Reality (VR)
11.6.1 Key Concepts
11.6.2 Integration of AR and VR
11.6.3 Applications
11.6.4 Benefits
11.6.5 Challenges
11.6.6 Future Trends
11.7 Edge-Based Data Fusion
11.7.1 Key Components
11.7.2 Applications
11.7.3 Benefits
11.7.4 Challenges
11.7.5 Implementation Strategies
11.7.6 Future Trends
11.8 Distributed Edge Intelligence Marketplaces
11.8.1 Key Concepts
11.8.2 Components
11.8.3 Benefits
11.8.4 Challenges
11.8.5 Potential Applications
11.8.6 Implementation Strategies
11.8.7 Future Trends
11.9 Edge-to-Cloud Orchestration
11.9.1 Key Components
11.9.2 Benefits
11.9.3 Challenges
11.9.4 Use Cases
11.9.5 Implementation Strategies
11.9.6 Future Trends
11.10 Conclusion
References
12. 6G Network: Integrating Wireless Networks and Machine Learning for Connected Edge Intelligence
B. Prabha, V. Praveen and M.R. Santhoosh
12.1 Introduction
12.1.1 Definition and Importance of Edge Intelligence in the 6G Context
12.2 Evolution of Wireless Networks for Edge Intelligence
12.2.1 Historical Perspective: From 1G to 6G and the Evolution of Edge Computing
12.2.2 Key Technological Advancements Enabling Edge Intelligence in 6G Networks
12.3 Challenges in Integrating AI with Wireless Networks
12.3.1 Latency and Real-Time Processing Requirements
12.3.2 Energy Efficiency and Resource Optimization
12.3.3 Privacy and Security Concerns in Edge AI Systems
12.4 Machine Learning Models for Edge Computing
12.4.1 Overview of Decentralized Machine Learning Algorithms
12.4.2 Model Compression and Optimization Techniques for Edge Devices
12.4.3 Federated Learning and Collaborative Intelligence at the Edge
12.5 Design Principles for Edge AI Systems in 6G
12.5.1 Scalable Architecture for Edge AI Deployment
12.5.2 Service-Driven Resource Allocation and Management
12.5.3 Edge-to-Cloud Continuum: Balancing Computation between Edge and Central Servers
12.6 Applications and Use Cases of Edge Intelligence in 6G Networks
12.6.1 Smart Cities and IoT Applications Leveraging Edge AI
12.6.2 Autonomous Vehicles and Intelligent Transportation Systems
12.6.3 Healthcare, Industry 4.0, and Other Verticals Benefiting from Edge Intelligence
12.6.3.1 Healthcare
12.6.3.2 Industry 4.0
12.7 Future Directions and Emerging Trends
12.7.1 Predictions for the Evolution of Edge Intelligence beyond 6G
12.7.2 Integration of Quantum Computing, Blockchain, and Other Emerging Technologies with Edge AI
12.8 Conclusion
References
13. Securing the Hyper-Connected World: Security, Privacy and Research Challenges in IoT
Gagneet Kaur, Komal Singh, Pankaj Bhambri and Sandeep Kumar Singla
13.1 Introduction
13.1.1 Security Framework for Privacy & Security in a Hyper-Connected World
13.2 Security Attacks & Open Challenges
13.2.1 Smart Buildings
13.2.2 Healthcare Industry
13.3 Solutions & Security Architecture for Healthcare Industry
13.3.1 Confidentiality Risks
13.3.2 Availability Risks
13.3.3 Integrity Risks
13.4 Automotive IoT
13.4.1 Vulnerabilities
13.4.2 Safety Measures
13.5 Issues of Risks Arise in Key Security Principles of Security Architecture
13.6 Solutions for Issues of Risks Arise in Key Security Principles of Security Architecture
References
14. Edge-to-Cloud Synergy: Enhancing IIoT Capabilities
Cynthia Jayapal, K. Ulagapriya, K.V.M. Shree and A. Poonguzhali
14.1 Introduction
14.1.1 Foundations of Industrial IoT
14.1.1.1 Evolution of Industry IoT
14.1.1.2 Components of IIoT Ecosystem
14.1.1.3 Role of IIoT in Industrial Transformation
14.1.2 Understanding Edge Computing
14.1.2.1 Overview of Edge Computing
14.1.2.2 Need of Edge Computing for IIoT Applications
14.1.2.3 Operational Benefits of Edge Computing
14.1.2.4 Edge Computing Architectures
14.1.3 Cloud Computing
14.1.3.1 Overview of Cloud Computing
14.1.3.2 Cloud Services for Industrial Applications and Their Impact on IIoT
14.1.3.3 Benefits and Challenges of Cloud Integration
14.1.4 Synergizing Edge and Cloud Technologies
14.1.4.1 Conceptual Framework of Edge-to-Cloud Synergy
14.1.4.2 Integrating Edge and Cloud for Enhanced Performance
14.1.4.3 Achieving Optimal Balance in IoT Operations
14.1.5 Steps in Edge-to-Cloud Integration
14.1.5.1 Data Collection from Edge Devices
14.1.5.2 Data Filtering, Aggregation, and Compression
14.1.5.3 Edge Intelligence with Machine Learning Algorithms
14.1.5.4 Establishing Edge-Cloud Connectivity
14.1.5.5 Real-Time Monitoring and Control
14.1.5.6 Enabling Real-Time Decision-Making
14.1.6 6G Terahertz Communication Revolution
14.1.6.1 Introduction to 6G Terahertz Communication
14.1.6.2 Framework for Using Edge Intelligence in the 6G Industrial Internet of Things (IIoT)
14.1.6.3 Implications and Advantages in IIoT
14.1.6.4 Challenges and Solutions in Implementing Edge Intelligence for 6G IIoT
14.1.7 Digital Twins for Real-Time Monitoring
14.1.7.1 Digital Twins
14.1.7.2 Integration of Digital Twin and IIoT
14.1.7.3 Framework for Digital Twin in IIoT
14.1.8 Blockchain for Data Security and Integrity
14.1.8.1 Blockchain for IIoT Data Security and Integrity
14.1.8.2 Overview of Blockchain Technology
14.1.8.3 Need for Blockchain in IIoT
14.1.8.4 Smart Contract and DApp
14.1.8.5 Benefits of the Use of Blockchain in IIoT
14.1.9 Conclusion
14.1.9.1 Recapitulation of Key Findings
14.1.9.2 Future Trends and Emerging Technologies
References
15. Advancing Industrial Intelligence: Leveraging Optimized Edge Devices With Large Language Model Concepts
S. Sathishkumar, R. Devi Priya, K. Karthika and A. Menaka
15.1 Introduction
15.1.1 The Evolution of Industrial Intelligence
15.1.1.1 From Traditional Manufacturing to Industry 4.0
15.1.2 Understanding Edge Computing
15.1.2.1 Defining Edge Computing
15.1.2.2 The Conceptual Framework
15.1.2.3 Key Components and Architecture
15.1.3 Enabling Technologies
15.1.3.1 Internet of Things (IoT) in Industrial Context
15.1.3.2 Artificial Intelligence (AI) Paradigms
15.1.4 Challenges and Opportunities
15.1.4.1 Computational Resource Constraints
15.1.4.2 Security Considerations
15.1.5 Industrial Applications
15.1.5.1 Predictive Maintenance
15.1.5.2 Quality Control and Assurance
15.1.5.3 Supply Chain Management
15.2 Proposed Architecture/System for Industrial Edge Computing
15.2.1 Introduction
15.2.2 Key Components and Architecture
15.3 Conclusion
References
16. Advancing Edge Intelligence: The Role and Future in 6G Networks
L. Maria Suganthi, P. Senthamizh Pavai, K. Geetha and J. Vigneshwari
16.1 Introduction
16.2 What is 6G Networks?
16.3 Key Characteristics of 6G Networks
16.4 Technological Innovations Driving 6G
16.5 Challenges and Opportunities in 6G Development
16.6 Applications and Implications of 6G Networks
16.7 The Role of AI in 6G Networks
16.8 Security and Privacy Enhancements in 6G Networks
16.9 What is Edge Intelligence?
16.10 AI Chips for Edge Devices - Transforming Localized Processing and Intelligence
16.11 Edge Intelligence in 6G Networks
16.12 Key Components of Edge Intelligence in 6G Networks
16.13 The Role of Edge Intelligence in 6G Networks
16.14 Security and Privacy in Edge Intelligence
16.14.1 Introduction to Security and Privacy in Edge Intelligence
16.14.2 Threat Landscape for Edge Intelligence
16.14.3 AI-Driven Security Solutions for Edge Intelligence
16.14.4 Data Privacy Concerns and Solutions
16.14.5 Secure Edge Device Management
16.14.6 Encryption and Data Integrity
16.14.7 Zero Trust Architecture in Edge Networks
16.14.8 Blockchain for Enhanced Security and Privacy
16.14.9 Federated Learning and Collaborative AI
16.14.10 Case Studies: Security and Privacy Best Practices
16.14.11 Future Directions in Security and Privacy for Edge Intelligence
16.15 The Future of Edge Intelligence in 6G Networks
16.16 Advantages of Edge Intelligence
16.17 Challenges in Edge Intelligence
16.18 Conclusion
References
17. Optimizing Edge Devices for Industrial Intelligence
Tharun Satla, Srikanth Jannu, Pankaj Bhambri and Chaitanya Thuppari
17.1 Introduction
17.1.1 Overview of OOA
17.1.2 Organization
17.2 Related Work
17.3 System Models
17.3.1 Network Models
17.3.2 Energy Models
17.4 Proposed Work
17.4.1 OOA Based Cluster Head Selection
17.4.1.1 Initialization
17.4.1.2 Phase 1: Exploration
17.4.1.3 Phase 2: Exploitation
17.4.1.4 OOA Representation
17.4.2 Derivation of Fitness Functions
17.4.2.1 Sink Distance
17.4.2.2 Residual Energy
17.4.2.3 Intra-Cluster Distance
17.4.3 Cluster Formation
17.4.4 An Illustration
17.5 Simulation Results
17.5.1 Residual Energy
17.5.2 Network Lifetime
17.5.3 Number of Alive Nodes
17.6 Conclusion
Acknowledgement
References
18. 6G Enabled Industrial Internet of Medical Things: Prospective, Development and Challenges
Meetali Chauhan and Sita Rani
18.1 Introduction
18.2 Literature Survey
18.3 6G Technology
18.4 Role of 6G Technology towards Healthcare
18.5 6G Based IIoMT Applications
18.5.1 Holographic Communication
18.5.2 Augmented Reality and Virtual Reality
18.5.3 Haptic Internet
18.5.4 Sample Reader Sensors
18.5.5 Intelligent Wearable Devices
18.5.6 Hospital to Home Services
18.5.7 Telesurgery
18.6 Challenges and Future Perspective
18.6.1 Challenges for 6G Technology
18.6.2 Future Perspective
18.7 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page