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.
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 AI, IoT, and other 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.
Table of Contents:
Preface xv
1 Introduction to AIIoT and CDNs 1
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 2
1.2 The Interrelationship between AIoT and CDN 5
1.3 Advantages of AIoT in Content Distribution 7
1.4 Major Challenges in the Integration of AIoT and CDN 10
1.5 Optimizing Latency and Network Efficiency 13
1.6 Real-Time Data Processing and Analytics 16
1.7 Scalability and Adaptability of AIoT-CDN Systems 18
1.8 AIoT and CDN Networks Security and Privacy 20
1.9 Future Trends in AIoT and CDN Technologies 22
1.10 Conclusion: Toward Smarter, Faster Digital Infrastructures 26
2 Predictive Caching Strategies Using Machine Learning for Seamless Content Access 31
Jagendra Singh, Monika Dandotiya, Shivani Agarwal, Kannan Srinivasan, Rahul Jadon and Guman Singh Chauhan
2.1 Introduction 32
2.2 Related Work 33
2.3 Methodology 35
2.4 Result and Discussion 43
2.5 Conclusion 49
3 Artificial Intelligence in Context Delivery 53
Vaishnavi and Navneet Kumar
3.1 Introduction 54
3.2 Benefits of Machine Learning for Caching 58
3.3 Understanding Advanced CDN Architecture 59
3.4 Mechanisms Behind Context-Aware AI Systems 61
3.5 Applications of AI in Context Delivery 61
3.6 Benefits of AI in Context Delivery 62
3.7 Challenges in Context Delivery (CD) 63
3.8 Summary and Discussion 63
3.9 Conclusion 66
4 Monetizing Cyberliterature through AI-Enhanced Networks in English Education System 69
Muthmainnah Muthmainnah, Luis Cardoso, Ahmad Al Yakin, Khristianto Khristianto, Widya Nirmalawati and Titik Wahyuningsih
4.1 Introduction 70
4.2 Literature Review 72
4.3 Methodology 80
4.4 Findings and Discussions 83
4.5 Discussion 90
4.6 Conclusions 94
5 Understanding AI with IoT, Real-Time Applications in Image Dehazing 101
Malladi Sunder Rao and Dilip Kumar
5.1 Introduction 101
5.2 The Integration of AI and IoT 103
5.3 Introduction to Image Dehazing 105
5.4 The Dehazing Process Model 108
5.5 General Process of Dehazing or Haze Removal Model 109
5.6 Datasets for the Dehazing Process 112
5.7 Network Section 112
5.8 Applications of Image Dehazing in AI IoT 113
5.9 Case Studies 114
5.10 Features of the PYNQ-Z2 Board 115
5.11 Advantages of Integrating AI with IoT 116
5.12 Application of the PYNQ-Z2 Board 117
5.13 Loss Function 118
5.14 Conclusion 120
6 Data-Driven Optimization of Content Distribution: Leveraging AI for Personalization and Scalability 123
Jagendra Singh, Namita Nath, Lucky Gupta, Swapna Narla, Sreekar Peddi and Dharma Teja Valivarthi
6.1 Introduction 124
6.2 Problem Statement 125viii Contents
6.3 Literature Review 126
6.4 Methodology 128
6.5 Result and Discussion 136
6.6 Conclusion 143
7 Generative AI and Content Delivery Networks: Revolutionizing Content Delivery and Learning Management with AI-Driven Innovation 147
Kamini, Sanjay Kumar Sonker, Ashish Kumar, Manoj Kumar, Prem Prakash Agrawal and Ramendra Singh
7.1 Introduction 148
7.2 Problem Statement 149
7.3 Literature Review 150
7.4 Methodology 151
7.5 Result and Discussion 161
7.6 Conclusion 168
8 Adaptive Video Streaming and Content Personalization Using Generative Models 171
Afreen Fatima Mohammed, Vijayalakshmi Chintamaneni, Jagendra Singh, Kalyan Gattupalli, Venkata Surya Bhavana Harish Gollavilli and Surendar Rama Sitaraman
8.1 Introduction 172
8.2 Problem Statement 173
8.3 Literature Review 174
8.4 Methodology 175
8.5 Result and Discussion 184
8.6 Conclusion 190
9 Data Analytics and Real-Time Decision Making in Water Quality Prediction 193
Kajal Kumari, Sudip Kumar Sahana and Debjani Mustafi
9.1 Introduction 194
9.2 Related Work 195
9.3 Dataset Description 196
9.4 Methodology of Proposed Model 198
9.5 Experimental Setup 203
9.6 Results and Discussion 204
9.7 Conclusion 208
10 Enhancing E-Commerce Security: A Framework of Integrated Vulnerability Assessment and Data Privacy Protection 213
Aftab Ara
10.1 Introduction 214
10.2 Literature Review 214
10.3 Findings 220
10.4 Conceptual Framework for E-Commerce Security 222x Contents
10.5 Discussions 224
10.6 Implications 225
10.7 Conclusion 226
11 Enhancing Content Delivery Networks with Generative AI: Strategies, Applications, and Future Directions 231
Subhranil Das, Rashmi Kumari and Raghwendra Kishore Singh
11.1 Introduction 232
11.2 Fundamentals of Generative AI Techniques for CDNs 234
11.3 Intelligent Caching Strategies for CDNs with Generative AI 244
11.4 Ethical and Security Concerns in Generative AI for CDNs 250
11.5 Conclusion and Future Scope 253
12 An IoT-Enabled Indoor Tracking Base System (IITBS) for Human Activities Identification 259
Pulakesh Roy, Sushmita Chaudhari, Surabhi Solanki, Rajib Banerjee and Arindam Biswas
12.1 Introduction 260
12.2 Literature Review 261
12.3 Structure Design 265
12.4 Outcomes and Performance 270
12.5 Conclusion 275
13 AI and IoT-Driven Machine Learning in CDNs: Advancing Diagnostic Precision in Breast Cancer Screening 279
Shubhranshu Gorai, Saibal Majumder, Ritik Maity, Mehuli Lahiri, Sovan Bhattacharya, Dola Sinha, Suchandra Banerjee and Chandan Bandyopadhyay
13.1 Introduction 280
13.2 Dataset Description 283
13.3 Methodology 286
13.4 Machine Learning Models 288
13.5 Performance Metrics 289
13.6 Results and Discussions 291
13.7 Conclusion 303
14 Rethinking Cybersocialization in Posthumanist: An Evolution of University Curriculum Based on AIIoT Automation 307
Ahmad Al Yakin, Ali Said Al Matari, Abd. Ghofur, Muthmainnah Muthmainnah, Ahmed J. Obaid and Souvik Ganguli
14.1 Introduction 308
14.2 Literature Review 310
14.3 Methodology 320
14.4 Findings and Discussion 321
14.5 Discussion 328
14.6 Conclusions 333
15 Future Trends and Innovation Education 7.0 by Adopting AIIoT Networks 341
Besse Darmawati, Ade Mulyanah, Adri Adri, Ratnawati Ratnawati, Resti Nurfaidah, Muthmainnah Muthmainnah and Souvik Ganguli
15.1 Introduction 342
15.2 Literature Review 344
15.3 Research Methodology 354
15.4 Findings and Discussion 356
15.5 Conclusion 365
References 366
Index 375
About the Author :
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.
