DOI: 10.35377/saucis...1778576 ISSN: 2636-8129

A Transformer-Assisted Cooperative Caching Framework with Q-Learning Optimization for Internet of Vehicles

Abdelkrim Lamraoui, Lyamine Guezouli, Kamel Barka, Mohamed Zohir Mabane, Sohaib Chine
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The Internet of Vehicles (IoV) is witnessing escalating data traffic that demands sophisticated caching to minimize retrieval latency and sustain Quality of Service. Recent cooperative approaches, notably the Cooperative Caching Strategy with Content Request Prediction (CCCRP) framework grounded in LSTM-based request prediction, struggle to capture the complex, long-range temporal dependencies inherent to dynamic vehicular environments. This paper proposes Transformer-based CCCRP (T-CCCRP), which replaces the LSTM predictor with a Transformer model leveraging self-attention to model long-range dependencies and enable efficient parallelism. The prediction module is integrated with a reinforcement learning controller to optimize cache placement jointly across vehicles and roadside units, thereby aligning predicted popularity with resource constraints. To ensure a realistic and comprehensive evaluation, the proposed framework is assessed under both small-scale and newly introduced large-scale IoV scenarios, involving up to 200 vehicles, multiple caching nodes, and expanded content libraries. The experimental setup further adapts the input sequence length of the prediction model to reflect increased temporal complexity in large-scale environments. Simulation results demonstrate that T-CCCRP consistently outperforms the original CCCRP and conventional caching strategies (LFU and LRU) across all evaluated scales.

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