Intelligent Caching Based on Popular Content in Vehicular Networks: A Deep Transfer Learning Approach

Abstract

Information-centric networking (ICN) allows data to be cached at each node in the network. It is vital in vehicular networks (VNs) to improve caching performance and reduce content delay in high-traffic scenarios. In cooperative VNs, the requested content can be cached in the base station or nearby nodes without fetching the requested content from the server. The existing content popularity approaches face challenges in predicting popular content due to a time-varying environment, resulting in popularity being changed frequently. It is hard to predict such content in highly dynamic vehicular traffic. Therefore, the current approaches are less practical in a realistic scenario. This paper proposes an intelligent caching method for massive traffic in VNs to address these issues based on deep transfer learning (DTL). The primary purpose of this study is to reduce the system cost and content delay by increasing the cache hit rate based on popular data in dynamic traffic. The proposed solution uses a collaborative cache with social interaction among clusters to share the most popular content (MPC). Furthermore, it designs a time-varying mechanism to predict content popularity in a highly dynamic environment and share the widespread knowledge with other target nodes based on DTL. In addition, a content update method is developed to address the content replacement in a cooperative cache environment. Based on thorough analysis and evaluation, similar and dissimilar contents on base stations are classified among source and target clusters. The extensive simulation and experimentation confirm that the developed work achieved better than baseline studies.