Title: Popularity prediction caching based on logistic regression in vehicular content centric networks

Authors: Kai Yao; Zhaoyang Li; Lin Yao; Kuijun Lang

Addresses: Shenyang University of Technology Library, Shenyang University of Technology, China ' School of Software, Dalian University of Technology, China ' School of Software, Dalian University of Technology, China ' Shenyang Aircraft Design and Research Institute, Aviation Industry Corporation of China, China

Abstract: To improve the network performance caused by mobility and sporadic connectivity in the vehicular network, vehicular content centric network (VCCN) is proposed by applying CCN into the vehicular network. The open in-network caching of CCN makes nodes cache contents cooperatively to facilitate information access. To improve the network performance such as access delay and hit ratio, road side units (RSUs) should try to cache more popular contents and provide better service for mobile users. This paper aims to propose a novel cache replacement policy - popularity prediction content caching (PPCC) for VCCN. In PPCC, we incorporate the future popularity of contents into our decision making. By learning the popularity of contents, we propose a cache replacement method based on logistic regression for RSUs in order to store those frequently access contents. The input data are related to the inherent characters of the received interests and the output is the predicted content popularity which guarantees that only popular contents are cached in the network infrastructures (i.e., RSUs). Simulation evaluations demonstrate that our scheme is very effective with higher cache hit, lower access latency and higher caching efficiency compared to other state-of-the-art schemes.

Keywords: vehicular content centric network; VCCN; cache policy; logistic regression; popularity prediction.

DOI: 10.1504/IJAHUC.2020.110821

International Journal of Ad Hoc and Ubiquitous Computing, 2020 Vol.35 No.3, pp.150 - 161

Received: 24 Mar 2020
Accepted: 08 May 2020
Published online: 29 Oct 2020 *

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