Mobility and Context-Aware Precaching Strategy Using Spatial-Temporal Informer for Vehicular Service

With the rapid development of vehicle-to-everything technology, vehicular edge caching has emerged as a crucial component for managing frequently accessed content at the network’s edge. However, due to vehicles’ high mobility, it is challenging to determine where and which content needs to be cached. To address this issue, a mobility and context-aware precache strategy is proposed to proactively prefetch and replace content in two steps. First, by integrating the traffic features from vehicles and roads, a spatial-temporal informer-based model is designed to predict long-term vehicle trajectories. Subsequently, a proactive context-aware precache strategy is proposed. By analyzing the context of different cache types, the required content can be further accurately estimated according to the cache type and workload. Extensive simulations based on real-world mobility scenarios are conducted to validate the performance of the proposed method. The results show that the proposed method can improve prediction accuracy and cache hit rate by 34.56% and 18.89%, and reduce mean response time and total energy cost by 6.1% and 2.65% compared to the existing precaching methods.