About this ebook
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.
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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.
