Proactive Content Retrieval Based on Value of Popularity in Content-Centric Internet of Vehicles

KHAN M. T. R., Jembre Y. Z., Saad M. M., Bouk S. H., Ahmed S. H., Kim D.

IEEE Transactions on Intelligent Transportation Systems, cilt.25, sa.8, ss.8514-8526, 2024 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Sayı: 8
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1109/tits.2024.3378669
  • Dergi Adı: IEEE Transactions on Intelligent Transportation Systems
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.8514-8526
  • Anahtar Kelimeler: Content-centric Internet of Vehicles (CIoV), proactive content retrieval, value of popularity (VoP), vehicular named data networks (VNDN)
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ( $VoP$ ) matrix. Considering vehicles requesting content of similar interests, we evaluate $VoP$ through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.