TS-based Mobility-aware Multi-hierarchical Caching Model with Vehicle Clustering and Content Popularity Prediction

• Autorzy: Baghiani Radouane , Guezouli Lyamine , Korichi Ahmed

Identyfikatory

DOI

10.26636/jtit.2024.3.1616

• Języki publikacji: EN

Abstrakty

EN

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

Słowa kluczowe

EN

clustering; Internet of Vehicles; machine learning; mobile edge computing; multi-hierarchical caching; Thompson sampling learning

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2024
• Tom: nr 3
• Strony: 65--78
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Baghiani Radouane

• LINATI Laboratory, Kasdi Merbah University, Ouargla, Algeria

• https://orcid.org/0009-0001-7694-7204

autor

Guezouli Lyamine

• LEREESI Laboratory, HNS-RE2SD, Batna, Algeria

• https://orcid.org/0000-0002-7406-7633

autor

Korichi Ahmed

• LINATI Laboratory, Kasdi Merbah University, Ouargla, Algeria

• https://orcid.org/0000-0002-5741-4963

Bibliografia

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