Markov Clustering-Based Content Placement in Roadside-Unit Caching With Deadline Constraint

Yu Ting Wang, Ting Yu Lin, Sok Ian Sou, Lo An Chen, Meng Hsun Tsai, Yean-Ru Chen, Chia Heng Tu

Research output: Contribution to journalArticlepeer-review


With the explosive growth of mobile data traffic, roadside-unit (RSU) caching is considered an effective way to offload download traffic in vehicular ad hoc networks (VANETs). Many existing works investigate the content placement of RSU caching. However, few of them consider the download deadline constraint when caching the content in the RSUs. In this paper, the main objective is to maximize the hit rate of downloading the requested content from the RSUs before the deadline expires. We propose a Markov-based mobility model and a Markov clustering-based content placement algorithm to group the RSUs into clusters and allocate the content to the cache of the RSUs in the cluster. We also investigate the impact on the cache hit rate under different simulation parameters, such as the total number of RSUs, the cache size, and the number of RSUs visited by vehicles during the download period. According to the simulations conducted, when the region of interest (RoI) is small, the MVP method increases the cache hit rate by at least 21.40% compared to the existing methods. When the RoI is large, our approach outperforms other existing methods by at least 26.16% and at most 337.77%, which significantly increases the efficiency of the download session in VANET.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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