Content Placement and Edge Collaborative Caching Scheme Based on Deep Reinforcement Learning for Internet of Vehicles

With the rapid development of Internet of Vehicles technology, communication and data exchange between vehicles have become an important part of modern traffic management. A content placement and edge collaborative caching solution based on deep reinforcement learning is proposed in this paper, aiming to address the data processing and storage challenges faced by Internet of Vehicles systems. Utilizing the collaborative caching between smart vehicles and roadside units employs deep reinforcement learning methods to find and design a collaborative caching solution for the Internet of Vehicles edge. It uses content segmentation technology to divide and cache content fragments in advance to reduce the central server load and network pressure, thereby adapting to the randomness of vehicle mobility and communication duration. The experimental results show that the proposed scheme can effectively reduce the load on the central server, reduce network latency, and improve cache hit rate, providing a flexible and efficient solution for real-time communication and data exchange in the Internet of Vehicles system.