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Submit a ProposalAIIoT-Powered CDNs
One Line Description
Stay ahead of the curve by learning how to transform static CDNs into intelligent, AI-powered networks that deliver content faster, more securely, and with pinpoint personalization.
Audience
Academics, researchers, IT professionals, network engineers, software developers, data scientists, and business leaders working to streamline content delivery.
Academics, researchers, IT professionals, network engineers, software developers, data scientists, and business leaders working to streamline content delivery.
Description
Content Delivery Networks (CDNs) are widely used across the internet today. They play a crucial role in delivering content faster and more reliably to users around the globe. However, CDNs today face several challenges, including static caching strategies, inefficient and suboptimal traffic routing, limited content personalization, and security threats that AI, machine learning, and Internet of Things can address collaboratively. This book provides a comprehensive overview of content delivery networks and how technologies like IoT, AI, and machine learning can be integrated with CDN to enhance content delivery and communication. It focuses on the backbone and architecture of CDN systems and the role of AIIoT and modern tools for predictive behavioral analysis. Through expert insights and real-world case studies, this book provides a platform to explore the future of innovations to improve the way we distribute and consume content.
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Content Delivery Networks (CDNs) are widely used across the internet today. They play a crucial role in delivering content faster and more reliably to users around the globe. However, CDNs today face several challenges, including static caching strategies, inefficient and suboptimal traffic routing, limited content personalization, and security threats that AI, machine learning, and Internet of Things can address collaboratively. This book provides a comprehensive overview of content delivery networks and how technologies like IoT, AI, and machine learning can be integrated with CDN to enhance content delivery and communication. It focuses on the backbone and architecture of CDN systems and the role of AIIoT and modern tools for predictive behavioral analysis. Through expert insights and real-world case studies, this book provides a platform to explore the future of innovations to improve the way we distribute and consume content.
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Author / Editor Details
Tanusree Chatterjee, PhD is an Associate Professor at the Future Institute of Technology, a unit of the Techno India Group. She has published more than 18 papers in international journals and conference proceedings, co-authored two books, and holds one patent. Her current research interests include artificial intelligence, machine learning, and the Internet of Things.
Tanusree Chatterjee, PhD is an Associate Professor at the Future Institute of Technology, a unit of the Techno India Group. She has published more than 18 papers in international journals and conference proceedings, co-authored two books, and holds one patent. Her current research interests include artificial intelligence, machine learning, and the Internet of Things.
Rajib Banerjee, PhD is an Associate Professor in the School of Computer Science at the University of Petroleum and Energy Studies with more than 22 years of experience. He has published many research articles in journals and conferences, as well as several patents. His research interests include wireless sensor networks, wireless multimedia sensor networks, and low-power multimedia processing.
Souvik Ganguli, PhD is an Assistant Professor at the Thapar Institute of Engineering and Technology. Patiala, India. He has published more than 50 papers in journals, conferences, and book chapters and holds 15 patents. His research interests include IoT-based control, delta operator modeling, and robust identification.
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Table of Contents
Preface
1. Introduction to AIIoT and CDNs
Priyanka Pramod Pawar, R. Kannan, R. Anand, Deepak Kumar, K. Arunprasath, A. Bhuvanesh and Tanusree Chatterjee
1.1 AIoT Explained: The Convergence of AI and IoT
1.2 The Interrelationship between AIoT and CDN
1.3 Advantages of AIoT in Content Distribution
1.4 Major Challenges in the Integration of AIoT and CDN
1.5 Optimizing Latency and Network Efficiency
1.5.1 Key Applications in Which AIIoT Can Enhance CDN Significantly
1.6 Real-Time Data Processing and Analytics
1.7 Scalability and Adaptability of AIoT-CDN Systems
1.8 AIoT and CDN Networks Security and Privacy
1.9 Future Trends in AIoT and CDN Technologies
1.10 Conclusion: Toward Smarter, Faster Digital Infrastructures
References
2. Predictive Caching Strategies Using Machine Learning for Seamless Content Access
Jagendra Singh, Monika Dandotiya, Shivani Agarwal, Kannan Srinivasan, Rahul Jadon and Guman Singh Chauhan
2.1 Introduction
2.2 Related Work
2.3 Methodology
2.3.1 Collection of Datasets
2.3.2 Data Processing
2.3.3 Machine Learning Models
2.4 Result and Discussion
2.5 Conclusion
References
3. Artificial Intelligence in Context Delivery
Vaishnavi and Navneet Kumar
3.1 Introduction
3.1.1 Context Delivery
3.1.2 Content Delivery
3.1.3 Role of Artificial Intelligence
3.1.4 AI and Machine Learning for Storage Optimization
3.1.4.1 Dynamic Decision-Making
3.1.4.2 Adapting to Evolving Access Patterns
3.1.4.3 Context-Aware Optimization
3.2 Benefits of Machine Learning for Caching
3.3 Understanding Advanced CDN Architecture
3.3.1 CDN Workflow
3.4 Mechanisms Behind Context-Aware AI Systems
3.4.1 Information/Data Collection and Analysis
3.4.2 Context Recognition
3.4.3 Delivery and Personalization
3.5 Applications of AI in Context Delivery
3.5.1 E-Commerce
3.5.2 Education
3.5.3 Healthcare
3.5.4 IoT and Smart Homes
3.6 Benefits of AI in Context Delivery
3.7 Challenges in Context Delivery (CD)
3.8 Summary and Discussion
3.9 Conclusion
Bibliography
4. Monetizing Cyberliterature through AI-Enhanced Networks in English Education System
Muthmainnah Muthmainnah, Luis Cardoso, Ahmad Al Yakin, Khristianto Khristianto, Widya Nirmalawati and Titik Wahyuningsih
4.1 Introduction
4.2 Literature Review
4.2.1 From Text to Hyperliterature through the AIIoT Network
4.2.2 The Multi-Model of Gen AI Tool in Education 7.0
4.2.3 Robotics Tutor in Learning and the Trifecta Teaching Model Process
4.3 Methodology
4.3.1 Participants
4.3.2 Instruments
4.3.3 Data Analysis
4.4 Findings and Discussions
4.5 Discussion
4.6 Conclusions
References
5. Understanding AI with IoT, Real-Time Applications in Image Dehazing
Malladi Sunder Rao and Dilip Kumar
5.1 Introduction
5.2 The Integration of AI and IoT
5.2.1 Applications of AIoT for Surveillance in Image Dehazing
5.3 Introduction to Image Dehazing
5.4 The Dehazing Process Model
5.5 General Process of Dehazing or Haze Removal Model
5.6 Datasets for the Dehazing Process
5.7 Network Section
5.8 Applications of Image Dehazing in AI IoT
5.9 Case Studies
5.9.1 Real-Time Dehazing Using Embedded Systems
5.10 Features of the PYNQ-Z2 Board
5.11 Advantages of Integrating AI with IoT
5.11.1 Real-World Applications
5.12 Application of the PYNQ-Z2 Board
5.13 Loss Function
5.14 Conclusion
References
6. Data-Driven Optimization of Content Distribution: Leveraging AI for Personalization and Scalability
Jagendra Singh, Namita Nath, Lucky Gupta, Swapna Narla, Sreekar Peddi and Dharma Teja Valivarthi
6.1 Introduction
6.2 Problem Statement
6.3 Literature Review
6.4 Methodology
6.4.1 Data Preprocessing
6.4.2 Artificial Intelligence Models
6.4.3 Simulation Environment and Experimental Setup
6.5 Result and Discussion
6.5.1 Discussion
6.6 Conclusion
References
7. Generative AI and Content Delivery Networks: Revolutionizing Content Delivery and Learning Management with AI-Driven Innovation
Kamini, Sanjay Kumar Sonker, Ashish Kumar, Manoj Kumar, Prem Prakash Agrawal and Ramendra Singh
7.1 Introduction
7.2 Problem Statement
7.3 Literature Review
7.4 Methodology
7.4.1 Preprocessing of the Dataset
7.4.2 Deployed AI Models
7.4.3 Simulation Environment and Experimental Setup
7.5 Result and Discussion
7.6 Conclusion
Bibliography
8. Adaptive Video Streaming and Content Personalization Using Generative Models
Afreen Fatima Mohammed, Vijayalakshmi Chintamaneni, Jagendra Singh, Kalyan Gattupalli, Venkata Surya Bhavana Harish Gollavilli and Surendar Rama Sitaraman
8.1 Introduction
8.2 Problem Statement
8.3 Literature Review
8.4 Methodology
8.4.1 AI Models Used
8.4.2 Preprocessing of the Dataset
8.5 Result and Discussion
8.6 Conclusion
Bibliography
9. Data Analytics and Real-Time Decision Making in Water Quality Prediction
Kajal Kumari, Sudip Kumar Sahana and Debjani Mustafi
9.1 Introduction
9.2 Related Work
9.3 Dataset Description
9.4 Methodology of Proposed Model
9.4.1 Preprocessing Stage
9.4.1.1 Handling Missing Data
9.4.1.2 Handling Temporal Features
9.4.1.3 Datetime Creation
9.4.2 Feature Extraction Stage
9.4.2.1 Differencing for Stationary
9.4.2.2 Temporal Features
9.4.2.3 Scaling and Normalization
9.4.2.4 Aggregation and Resampling
9.4.3 WQI Calculation
9.5 Experimental Setup
9.5.1 Optimize the SARIMA Model
9.5.2 Residuals Extraction
9.5.3 Data Preparation for the Transformer Model
9.5.4 Train the Transformer Model
9.5.5 Forecasting Residuals with the Transformer
9.6 Results and Discussion
9.6.1 Actual vs. Predicted Plot (Regression Line Plot)
9.6.2 Residual Analysis
9.6.3 Numerical Analysis
9.7 Conclusion
References
10. Enhancing E-Commerce Security: A Framework of Integrated Vulnerability Assessment and Data Privacy Protection
Aftab Ara
10.1 Introduction
10.2 Literature Review
10.2.1 Threats in E-Commerce
10.2.2 Mitigation Strategies
10.2.3 E-Commerce Integrated Security Solutions
10.2.4 Security Factors
10.3 Findings
10.4 Conceptual Framework for E-Commerce Security
10.5 Discussions
10.6 Implications
10.7 Conclusion
Bibliography
11. Enhancing Content Delivery Networks with Generative AI: Strategies, Applications, and Future Directions
Subhranil Das, Rashmi Kumari and Raghwendra Kishore Singh
11.1 Introduction
11.2 Fundamentals of Generative AI Techniques for CDNs
11.2.1 How GANs Work
11.2.2 Application of GANs in Generating High-Quality Media
11.2.3 GANs in Content Delivery Networks (CDNs)
11.2.4 Overview of Variational Autoencoders (VAEs) for Generating and Modifying Content
11.2.4.1 How VAEs Work
11.2.5 Applications of VAEs in Generating and Modifying Content
11.2.6 VAEs in Content Delivery Networks (CDNs)
11.2.7 Transformers in Processing and Personalizing Content at Scale
11.2.7.1 How Transformers Work
11.2.8 Applications of the Transformers in Content Processing and Personalization
11.2.9 Transformers in Content Delivery Networks (CDNs)
11.3 Intelligent Caching Strategies for CDNs with Generative AI
11.3.1 Adaptive Caching Using Predictive Generative Models
11.3.1.1 Working Mechanism
11.3.2 Load Optimization with Real-Time Data Integration
11.3.2.1 Working Mechanism
11.4 Ethical and Security Concerns in Generative AI for CDNs
11.5 Conclusion and Future Scope
Bibliography
12. An IoT-Enabled Indoor Tracking Base System (IITBS) for Human Activities Identification
Pulakesh Roy, Sushmita Chaudhari, Surabhi Solanki, Rajib Banerjee and Arindam Biswas
12.1 Introduction
12.2 Literature Review
12.3 Structure Design
12.3.1 Integration of AI Enables BLE-Based Object Tracking Using the Kalman Filter
12.3.2 Implementation of an Effective, Reliable Data Transmission System
12.3.2.1 Structure of the Client-Server Interface
12.3.2.2 The Inception of Content Delivery Network (CDN)
12.3.3 Server-End Script
12.3.4 Workflow Schematic
12.4 Outcomes and Performance
12.5 Conclusion
References
13. AI and IoT-Driven Machine Learning in CDNs: Advancing Diagnostic Precision in Breast Cancer Screening
Shubhranshu Gorai, Saibal Majumder, Ritik Maity, Mehuli Lahiri, Sovan Bhattacharya, Dola Sinha, Suchandra Banerjee and Chandan Bandyopadhyay
13.1 Introduction
13.2 Dataset Description
13.2.1 Data Preprocessing
13.3 Methodology
13.3.1 Data Acquisition and Exploration
13.3.2 Feature Extraction and Selection
13.3.3 Model Development and Training
13.3.4 Model Optimization and Evaluation
13.4 Machine Learning Models
13.5 Performance Metrics
13.6 Results and Discussions
13.7 Conclusion
References
14. Rethinking Cybersocialization in Posthumanist: An Evolution of University Curriculum Based on AIIoT Automation
Ahmad Al Yakin, Ali Said Al Matari, Abd. Ghofur, Muthmainnah Muthmainnah, Ahmed J. Obaid and Souvik Ganguli
14.1 Introduction
14.2 Literature Review
14.2.1 Cybersocialization and AIIoT in the Curriculum
14.2.2 Expanded Learning Outcomes with the AIIoT Network Ecosystem
14.2.3 Revolutionized Artificial Intelligence and the Internet of Things in Education
14.3 Methodology
14.3.1 Participants
14.3.2 Data Analysis
14.4 Findings and Discussion
14.5 Discussion
14.6 Conclusions
Bibliography
15. Future Trends and Innovation Education 7.0 by Adopting AIIoT Networks
Besse Darmawati, Ade Mulyanah, Adri Adri, Ratnawati Ratnawati, Resti Nurfaidah,
Muthmainnah Muthmainnah and Souvik Ganguli
15.1 Introduction
15.2 Literature Review
15.2.1 AIIoT Network in Education: A New Center Study Design
15.2.2 GenAI Tools in EFL Education 7.0
15.2.3 Smart Learning Curriculum with AIIoT Network
15.3 Research Methodology
15.3.1 Participants
15.3.2 Instrument
15.3.3 Procedure
15.4 Findings and Discussion
15.4.1 Findings
15.4.2 Discussion
15.5 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. Introduction to AIIoT and CDNs
Priyanka Pramod Pawar, R. Kannan, R. Anand, Deepak Kumar, K. Arunprasath, A. Bhuvanesh and Tanusree Chatterjee
1.1 AIoT Explained: The Convergence of AI and IoT
1.2 The Interrelationship between AIoT and CDN
1.3 Advantages of AIoT in Content Distribution
1.4 Major Challenges in the Integration of AIoT and CDN
1.5 Optimizing Latency and Network Efficiency
1.5.1 Key Applications in Which AIIoT Can Enhance CDN Significantly
1.6 Real-Time Data Processing and Analytics
1.7 Scalability and Adaptability of AIoT-CDN Systems
1.8 AIoT and CDN Networks Security and Privacy
1.9 Future Trends in AIoT and CDN Technologies
1.10 Conclusion: Toward Smarter, Faster Digital Infrastructures
References
2. Predictive Caching Strategies Using Machine Learning for Seamless Content Access
Jagendra Singh, Monika Dandotiya, Shivani Agarwal, Kannan Srinivasan, Rahul Jadon and Guman Singh Chauhan
2.1 Introduction
2.2 Related Work
2.3 Methodology
2.3.1 Collection of Datasets
2.3.2 Data Processing
2.3.3 Machine Learning Models
2.4 Result and Discussion
2.5 Conclusion
References
3. Artificial Intelligence in Context Delivery
Vaishnavi and Navneet Kumar
3.1 Introduction
3.1.1 Context Delivery
3.1.2 Content Delivery
3.1.3 Role of Artificial Intelligence
3.1.4 AI and Machine Learning for Storage Optimization
3.1.4.1 Dynamic Decision-Making
3.1.4.2 Adapting to Evolving Access Patterns
3.1.4.3 Context-Aware Optimization
3.2 Benefits of Machine Learning for Caching
3.3 Understanding Advanced CDN Architecture
3.3.1 CDN Workflow
3.4 Mechanisms Behind Context-Aware AI Systems
3.4.1 Information/Data Collection and Analysis
3.4.2 Context Recognition
3.4.3 Delivery and Personalization
3.5 Applications of AI in Context Delivery
3.5.1 E-Commerce
3.5.2 Education
3.5.3 Healthcare
3.5.4 IoT and Smart Homes
3.6 Benefits of AI in Context Delivery
3.7 Challenges in Context Delivery (CD)
3.8 Summary and Discussion
3.9 Conclusion
Bibliography
4. Monetizing Cyberliterature through AI-Enhanced Networks in English Education System
Muthmainnah Muthmainnah, Luis Cardoso, Ahmad Al Yakin, Khristianto Khristianto, Widya Nirmalawati and Titik Wahyuningsih
4.1 Introduction
4.2 Literature Review
4.2.1 From Text to Hyperliterature through the AIIoT Network
4.2.2 The Multi-Model of Gen AI Tool in Education 7.0
4.2.3 Robotics Tutor in Learning and the Trifecta Teaching Model Process
4.3 Methodology
4.3.1 Participants
4.3.2 Instruments
4.3.3 Data Analysis
4.4 Findings and Discussions
4.5 Discussion
4.6 Conclusions
References
5. Understanding AI with IoT, Real-Time Applications in Image Dehazing
Malladi Sunder Rao and Dilip Kumar
5.1 Introduction
5.2 The Integration of AI and IoT
5.2.1 Applications of AIoT for Surveillance in Image Dehazing
5.3 Introduction to Image Dehazing
5.4 The Dehazing Process Model
5.5 General Process of Dehazing or Haze Removal Model
5.6 Datasets for the Dehazing Process
5.7 Network Section
5.8 Applications of Image Dehazing in AI IoT
5.9 Case Studies
5.9.1 Real-Time Dehazing Using Embedded Systems
5.10 Features of the PYNQ-Z2 Board
5.11 Advantages of Integrating AI with IoT
5.11.1 Real-World Applications
5.12 Application of the PYNQ-Z2 Board
5.13 Loss Function
5.14 Conclusion
References
6. Data-Driven Optimization of Content Distribution: Leveraging AI for Personalization and Scalability
Jagendra Singh, Namita Nath, Lucky Gupta, Swapna Narla, Sreekar Peddi and Dharma Teja Valivarthi
6.1 Introduction
6.2 Problem Statement
6.3 Literature Review
6.4 Methodology
6.4.1 Data Preprocessing
6.4.2 Artificial Intelligence Models
6.4.3 Simulation Environment and Experimental Setup
6.5 Result and Discussion
6.5.1 Discussion
6.6 Conclusion
References
7. Generative AI and Content Delivery Networks: Revolutionizing Content Delivery and Learning Management with AI-Driven Innovation
Kamini, Sanjay Kumar Sonker, Ashish Kumar, Manoj Kumar, Prem Prakash Agrawal and Ramendra Singh
7.1 Introduction
7.2 Problem Statement
7.3 Literature Review
7.4 Methodology
7.4.1 Preprocessing of the Dataset
7.4.2 Deployed AI Models
7.4.3 Simulation Environment and Experimental Setup
7.5 Result and Discussion
7.6 Conclusion
Bibliography
8. Adaptive Video Streaming and Content Personalization Using Generative Models
Afreen Fatima Mohammed, Vijayalakshmi Chintamaneni, Jagendra Singh, Kalyan Gattupalli, Venkata Surya Bhavana Harish Gollavilli and Surendar Rama Sitaraman
8.1 Introduction
8.2 Problem Statement
8.3 Literature Review
8.4 Methodology
8.4.1 AI Models Used
8.4.2 Preprocessing of the Dataset
8.5 Result and Discussion
8.6 Conclusion
Bibliography
9. Data Analytics and Real-Time Decision Making in Water Quality Prediction
Kajal Kumari, Sudip Kumar Sahana and Debjani Mustafi
9.1 Introduction
9.2 Related Work
9.3 Dataset Description
9.4 Methodology of Proposed Model
9.4.1 Preprocessing Stage
9.4.1.1 Handling Missing Data
9.4.1.2 Handling Temporal Features
9.4.1.3 Datetime Creation
9.4.2 Feature Extraction Stage
9.4.2.1 Differencing for Stationary
9.4.2.2 Temporal Features
9.4.2.3 Scaling and Normalization
9.4.2.4 Aggregation and Resampling
9.4.3 WQI Calculation
9.5 Experimental Setup
9.5.1 Optimize the SARIMA Model
9.5.2 Residuals Extraction
9.5.3 Data Preparation for the Transformer Model
9.5.4 Train the Transformer Model
9.5.5 Forecasting Residuals with the Transformer
9.6 Results and Discussion
9.6.1 Actual vs. Predicted Plot (Regression Line Plot)
9.6.2 Residual Analysis
9.6.3 Numerical Analysis
9.7 Conclusion
References
10. Enhancing E-Commerce Security: A Framework of Integrated Vulnerability Assessment and Data Privacy Protection
Aftab Ara
10.1 Introduction
10.2 Literature Review
10.2.1 Threats in E-Commerce
10.2.2 Mitigation Strategies
10.2.3 E-Commerce Integrated Security Solutions
10.2.4 Security Factors
10.3 Findings
10.4 Conceptual Framework for E-Commerce Security
10.5 Discussions
10.6 Implications
10.7 Conclusion
Bibliography
11. Enhancing Content Delivery Networks with Generative AI: Strategies, Applications, and Future Directions
Subhranil Das, Rashmi Kumari and Raghwendra Kishore Singh
11.1 Introduction
11.2 Fundamentals of Generative AI Techniques for CDNs
11.2.1 How GANs Work
11.2.2 Application of GANs in Generating High-Quality Media
11.2.3 GANs in Content Delivery Networks (CDNs)
11.2.4 Overview of Variational Autoencoders (VAEs) for Generating and Modifying Content
11.2.4.1 How VAEs Work
11.2.5 Applications of VAEs in Generating and Modifying Content
11.2.6 VAEs in Content Delivery Networks (CDNs)
11.2.7 Transformers in Processing and Personalizing Content at Scale
11.2.7.1 How Transformers Work
11.2.8 Applications of the Transformers in Content Processing and Personalization
11.2.9 Transformers in Content Delivery Networks (CDNs)
11.3 Intelligent Caching Strategies for CDNs with Generative AI
11.3.1 Adaptive Caching Using Predictive Generative Models
11.3.1.1 Working Mechanism
11.3.2 Load Optimization with Real-Time Data Integration
11.3.2.1 Working Mechanism
11.4 Ethical and Security Concerns in Generative AI for CDNs
11.5 Conclusion and Future Scope
Bibliography
12. An IoT-Enabled Indoor Tracking Base System (IITBS) for Human Activities Identification
Pulakesh Roy, Sushmita Chaudhari, Surabhi Solanki, Rajib Banerjee and Arindam Biswas
12.1 Introduction
12.2 Literature Review
12.3 Structure Design
12.3.1 Integration of AI Enables BLE-Based Object Tracking Using the Kalman Filter
12.3.2 Implementation of an Effective, Reliable Data Transmission System
12.3.2.1 Structure of the Client-Server Interface
12.3.2.2 The Inception of Content Delivery Network (CDN)
12.3.3 Server-End Script
12.3.4 Workflow Schematic
12.4 Outcomes and Performance
12.5 Conclusion
References
13. AI and IoT-Driven Machine Learning in CDNs: Advancing Diagnostic Precision in Breast Cancer Screening
Shubhranshu Gorai, Saibal Majumder, Ritik Maity, Mehuli Lahiri, Sovan Bhattacharya, Dola Sinha, Suchandra Banerjee and Chandan Bandyopadhyay
13.1 Introduction
13.2 Dataset Description
13.2.1 Data Preprocessing
13.3 Methodology
13.3.1 Data Acquisition and Exploration
13.3.2 Feature Extraction and Selection
13.3.3 Model Development and Training
13.3.4 Model Optimization and Evaluation
13.4 Machine Learning Models
13.5 Performance Metrics
13.6 Results and Discussions
13.7 Conclusion
References
14. Rethinking Cybersocialization in Posthumanist: An Evolution of University Curriculum Based on AIIoT Automation
Ahmad Al Yakin, Ali Said Al Matari, Abd. Ghofur, Muthmainnah Muthmainnah, Ahmed J. Obaid and Souvik Ganguli
14.1 Introduction
14.2 Literature Review
14.2.1 Cybersocialization and AIIoT in the Curriculum
14.2.2 Expanded Learning Outcomes with the AIIoT Network Ecosystem
14.2.3 Revolutionized Artificial Intelligence and the Internet of Things in Education
14.3 Methodology
14.3.1 Participants
14.3.2 Data Analysis
14.4 Findings and Discussion
14.5 Discussion
14.6 Conclusions
Bibliography
15. Future Trends and Innovation Education 7.0 by Adopting AIIoT Networks
Besse Darmawati, Ade Mulyanah, Adri Adri, Ratnawati Ratnawati, Resti Nurfaidah,
Muthmainnah Muthmainnah and Souvik Ganguli
15.1 Introduction
15.2 Literature Review
15.2.1 AIIoT Network in Education: A New Center Study Design
15.2.2 GenAI Tools in EFL Education 7.0
15.2.3 Smart Learning Curriculum with AIIoT Network
15.3 Research Methodology
15.3.1 Participants
15.3.2 Instrument
15.3.3 Procedure
15.4 Findings and Discussion
15.4.1 Findings
15.4.2 Discussion
15.5 Conclusion
References
About the Editors
Index
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