Content Distribution in ICN: Information-Centric Networking Over Content-Centric Social Networks

Abstract

The proliferation of digital gadgets and the popularity of social networking have led to many people documenting and broadcasting their private lives online. Content sharing has become one of the most common online activities, yet it is rarely considered during the design phase of a website. Information-centric networking (ICN) is a novel architecture developed to handle the rapidly evolving Internet usage patterns. ICN optimizes information exchange by treating content as the fundamental component of networking functions. This study presents a method that enhances ICN's core functionality by leveraging content identification at the network layer. This enables the selective retrieval of material parts already stored in ICN's in-network caches, allowing information producers to offload some content processing tasks to the network. The findings show reduced network core traffic and storage usage. To address content push issues, precise user descriptions and support for the big data nature of both individuals and content are necessary. Content-centric networking (CCN) offers a solution with lower connection costs and faster download times. By using named content, CCN facilitates real-time interest tracking, enabling the study of new content and enhancing online learning algorithms. This approach results in efficient outcomes in the big data context, avoiding additional network strain and overcoming the cold start problem.