Popularity Prediction Caching Using Hidden Markov Model for Vehicular Content Centric Networks

Abstract

Vehicular Content Centric Network (VCCN) is proposed to cope with mobility and intermittent connectivity issues of vehicular ad hoc networks by enabling the Content Centric Network (CCN) model in vehicular networks. The ubiquitous in-network caching of VCCN allows nodes to cache contents frequently accessed data items, improving the hit ratio of content retrieval and reducing the data access delay. Furthermore, it can significantly mitigate bandwidth pressure. Therefore, it is crucial to cache more popular contents at various caching nodes. In this paper, we propose a novel cache replacement scheme named Popularity-based Content Caching (PopCC), which incorporates the future popularity of contents into our decision making. We adopt Hidden Markov Model (HMM) to predict the content popularity based on the inherent characters of the received interests, request ratio, request frequency and content priority. To evaluate the performance of our proposed scheme PopCC, we compare it with some state-of-the-art schemes in terms of cache hit, average access delay, average hop count and average storage usage. Simulations demonstrate that the proposed scheme possesses a better performance.