Personalized fog caching using predictive models of user mobility and preferences

The proliferation of video streaming and mobile applications has intensified the demand for low-latency, high-throughput content delivery. Fog computing offers a promising solution by enabling content caching closer to end users at intermediary nodes, known as fog nodes, i.e., fog caching. However, existing fog caching strategies often fail to adapt to the rapid dynamics of user mobility and evolving content preferences, particularly on platforms like Instagram, YouTube, and TikTok. This paper presents a novel personalized fog caching strategy that proactively caches content by jointly predicting user mobility and application usage patterns. Leveraging a deep learning architecture built on transformer encoders, our strategy is to anticipate both the fog nodes users will connect to and the applications they are likely to access. It combines high-resolution spatio-temporal trajectory data with historical app usage behavior to drive fine-grained content placement across multi-tier fog networks. Simulation results demonstrate that our approach significantly improves cache hit rates and reduces retrieval latency compared to traditional popularity-based techniques.