Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Journal of Telecommunications and Information Technology

DOI: 10.26636/jtit.2024.3.1616

Authors: Radouane Baghiani, Lyamine Guezouli, Ahmed Korichi

Institutions: University of Ouargla, Higher National School of Renewable Energies, Environment & Sustainable Development

Language: English

Topic: Caching and Content Delivery

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4402011183

FWCI:

Cites: 29

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Related to: 10