Deep Reinforcement Learning-Based Mobile-Aware Edge Cooperative Caching Scheme in the Internet of Vehicles

The number of computation-intensive apps and content requests within the Internet of Vehicles (IoV) has dramatically expanded with the swift advancement of 5G communication technologies and next-generation Internet technology. To address the issue of excessive response latency caused by the surge in data traffic, edge caching (EC) technology has proven to be effective. However, accurately predicting the popularity of content in different regions is challenging due to the high degree of mobility of the vehicles. Additionally, as vehicles typically do not remain in the same region for a long period of time, making the content popularity within a region highly dynamic. To address this issue, we propose a deep reinforcement learning-based mobile-aware edge cooperative caching scheme (MAECCD). Our approach utilizes an AutoEncoder to capture user features from local vehicle user data and accurately forecast content popularity in both vehicle-local and edge regions. Furthermore, the MAECCD incorporates a cooperation model between edge nodes to optimize cache utilization and employs techniques in Deep Reinforcement Learning (DRL) to refresh cached content and reduce transmission delay. Experimental results indicate that our MAECCD scheme outperforms other baseline algorithms in terms of cache hit ratio and content transmission delay.