A Comprehensive Review of Named Data Networking: Architectural Principles, Techniques, Applications, Challenges and AI Integration

Abstract

Named Data Networking (NDN) paradigm is a novel approach to architecturally designing the internet. Instead of the traditional focus on the endpoints of the data, it will allow end-to-end communication based on the data itself. As a result, the NDN paradigm will provide significantly greater flexibility, scalability, and security compared to today’s IP-based paradigm. This paper examines the fundamental elements of NDN, including the primary aspects of naming, routing, and caching that are essential to effective dissemination of data and distribution of content within distributed networks across multiple application domains. In addition to the examination of these elements, this paper reviews potential implementations of NDN within application domains such as IoT,
multimedia streaming, vehicle networks, and smart city networks. While there are many challenges associated with implementing NDN applications, such as complexities of naming, scaling routing tables, and developing policies for privacy and cache management, this paper also reviews the use of artificial intelligence (AI) to improve NDN. Specifically, this paper reviews how AI can be applied to solve problems such as intelligent caching, adaptive forwarding, intrusion detection, etc. Additionally, this paper reviews how AI can improve network optimization using AI-driven NDN and demonstrates how AI-driven NDN can be used to create the next generation of intelligent and secure Internet architectures.