# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 2 Related Work  
In terms of caching strategies, the undifferentiated caching strategy proposed by [1] means that the content is cached at each network node through which it passes. The caching with probability strategy proposed by [2] means that a network node caches the content that passes through that node with some fixed probability. These non-cooperativecaching strategies make the content have a high redundancy in the network, resulting in a wastage of caching resources. In cooperative caching strategies, multiple nodes in the network cooperate with each other to effectively reduce the redundancy of content in the network. The authors of [3] identified and formulated the problem of maximizing the average cache hit rate considering the time-varying topology of the network, vehicle mobility, user preferences, and the limited cache capacity of the RSU. A cache update policy based on learning automata is designed to determine the appropriate content to be cached in the RSU. The authors of [4] proposed a file partitioning and grouping scheme in a distributed coding cache system that designs a static single-server system for heterogeneous file transfers. The authors of [5] designed a collaborative edge caching scheme based on location and popular content and proposed an optimal collaborative content layout for macro base stations and RSUs to reduce transmission latency and service cost. Ref. [6] proposed an active caching scheme for parked vehicles that uses parked vehicles to cache data in advance at appropriate times and locations so that users can receive the data as they pass by. In order to study the issue of caching from an economic perspective, the authors of [7] considered a concave pricing mechanism where CPs are charged through a concave price function of the time that content is cached in the CP cache. The proposed concave pricing mechanism is analyzed theoretically and provides a solution for CPs to optimally choose the length of time to stay in the ISP cache. The authors of [8] considered transcoding between different versions of video and the base station considered the energy consumption value when caching video clips; then, based on the different uses of caching, base station computational resources, and backhaul links by the in-vehicle users, they proposed a network resource pricing algorithm to improve the flexibility of the utilization of in-vehicle network resources. Ref. [9] focused on the complex relationship between competition and cooperation among multiple CPs and solved a non-cooperative game model based on game theory to study the interaction between caching and pricing strategies of ICN entities. By establishing the optimal utility function of each entity, the optimal cache share of ISP (internet service provider) and the optimal pricing of ISP and CP are obtained. The authors of [10] proposed a joint video pricing and cache placement strategy by considering the heterogeneity of video file sizes and the classical law of demand in the field of economics, maximizing the profit of both CP and NO in the case of non-cooperative base station caching and cooperative base station caching. The authors of [11] focused on caching and transaction models for vehicular networks and proposed a generic cache valuation and online pricing framework to achieve the goals of incentive compatibility, personal rationality, privacy preservation, computational efficiency, and utility maximization. In terms of utilizing UAVs, the authors of [12] optimized the caching and computational resource allocation of the network by optimizing the trajectory and flight altitude of the UAVs. In [13], an active caching scheme was designed in which UAVs were dispatched to provide content delivery services to vehicular users in a specific area. Ref. [14] considered content delivery in terrestrial networks for low earth orbit (LEO) satellites and cache-assisted UAV communications.