Content Propagation for Content-Centric Networking Systems From Location-Based Social Networks

Abstract

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users’ interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%–65.6%, and the contents can be disseminated faster by about 24.6%–57.8%.