# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 2 Related Works
### Non-Cooperative Caching Strategy  
In recent years, edge caching has played an important role in in-vehicle networks. Many existing articles design caching strategies by exploiting various properties of the content. For example, [PERSON] et al. [10] proposed a caching strategy for vehicular named data networks. The strategy considers three metrics, freshness of the content, popularity and the distance between the cache location and the cache's current location. [PERSON] et al. [11] uncover the relevance of content and space by investigating various areas in a city. Edge caching strategies in the Internet of Vehicles (IoV) can also be designed by analyzing the traffic characteristics of roads; modeling the mobility of vehicles has been introduced in models that consider the quality of service of caching. [PERSON] et al. [7] considered the effect of vehicle speed on the optimal caching decision and obtained the gain of content-active caching in roadside units (RSUs) to find the optimal caching strategy. Graph neural network [12] is a deep learning method which can deal with graph structure data and can represent many complex relationships. The graph neural network in the Internet of Vehicles can use the dynamic topological relationship between vehicles to learn the characteristics and behavior of vehicles, so as to perform tasks such as prediction, control, and optimization and realize real-time information update and transmission. [PERSON] et al. [13] proposed a mobile edge caching strategy based on the spatiotemporal graph convolution model (STCC) by mining the spatiotemporal features of content popularity and then using heuristic strategies to minimize content access delays. [PERSON] et al. [14] designed a caching decision based on the simplex algorithm based on the prediction of the Spatio-Temporal Graph Neural Network(STGNN) and performed edge computing on the edge server in the 6G Internet of Vehicles to achieve rapid response to delay-sensitive tasks. [PERSON] et al. [2018] developed a caching model to optimize network energy efficiency by using mobile vehicles on the edge. Through nonlinear technology and Lyapunov optimization theory, an online caching scheme was proposed to minimize network energy consumption, but the effect of cache file size was ignored. [PERSON] et al. [2018] proposed a security query scheme for the distribution of vehicle fog data. Reversible matrix is used to construct data requests from different vehicles to break the relationship between specific data requests and their original vehicles. This scheme significantly reduces the communication overhead on the premise of ensuring the security goal. Based on the problem of a large number of short video files, [PERSON] et al. [2018] proposed a cache scheme considering the QoE of vehicle Internet users and optimized the cache content by establishing a user interest class model. In [2018], in order to solve the delay-sensitive service, the authors jointly considered communication, caching, and computing to optimize the cache content retrieval delay, but they ignored the selection process of cache content when considering cache. Although these studies have made contributions to edge caching, most of them do not take into account the cooperation between edge nodes, which can easily cause the problem of cache redundancy, greatly waste edge storage space, and make it difficult to provide good cache services.