Data Dissemination and Caching Based on Deep Reinforcement Learning in the Vehicular Edge Networks

The vehicle edge network leverages mobile edge computing (MEC) to provide low-latency data and intelligent services. A key challenge is how to efficiently utilize limited resources to disseminate data and promptly respond to service requests. Existing methods, which cache data at the edge to reduce access delay, only consider single-source data distribution. However, applications like object detection and trajectory prediction involve multiple data copies across edges and vehicles. The destination vehicle can retrieve data from multiple sources, requiring optimal source selection. To tackle these challenges, we formulate a joint optimization problem for data dissemination and caching from multiple sources to multiple destinations, considering data timeliness and request deadlines to maximize request success rates. Specifically, we transform the problem into a minimum Steiner tree problem and propose a multi-agent reinforcement learning algorithm to determine source selection and caching strategies. Extensive experiments demonstrate that our approach significantly improves request success rates compared to benchmarks.