# UAV-Assisted Caching Strategy Based on Content Cache Pricing in Vehicular Networks
## 1 Introduction  
With the rapid development of vehicular networks, more and more in-vehicle applications are enriching the functions of vehicles, such as intelligent transportation, in-vehicle entertainment, in-vehicle office, road safety, and driverless applications. Vehicles need to obtain a variety of information from the outside world, such as traffic information, entertainment information, and real-time news, in order to provide users with a better driving experience. However, the number of vehicles is increasing dramatically and the information that users want to obtain is becoming more and more diverse, which leads to a dramatic increase in the communication load of the vehicular network. In addition, due to the high-speed mobility of vehicles, the topology of the vehicular network changes rapidly, making the communication links of vehicles easily interrupted, which leads to a degradation of the user's quality of experience. In order to reduce the workload of vehicular networks and improve the communication quality of the networks, caching techniques have been introduced in vehicular networks. Caching technology in vehicular networks is to cache content at various nodes such as RSUs and vehicles at the edge of the network, so that vehicles can receive the demanded content directly from surrounding nodes, thus effectively reducing the communicationdistance for requesting vehicles to receive content and reducing the traffic load on content servers and networks. The more common caching strategies currently applied in in-vehicle networks can be categorized into non-cooperative caching [1; 2] and cooperative caching [3; 4; 5; 6], which reduce the impact of vehicle mobility on the in-vehicle network and improve the quality of service. However, the above studies only determine the caching strategy from the perspective of the caching node, aiming at reducing the traffic load on the network or enhancing the user's quality of experience, and do not consider the overhead caused by caching as well as the benefits gained. In order to reduce request latency and save overhead, NO often chooses to rent some contents from remote CP at a cost and cache them on the network's edge devices, so a reasonable rental price and caching decision will benefit both the NO and the CP. Most of the current papers that study the caching problem from an economic perspective do not utilize flexible air mobile caching devices for assistance, such as UAVs. Vehicle users in the vehicular network have high-speed mobility and have a short contact time with the cache nodes with fixed positions on the ground; the introduction of UAVs with caching function in the vehicular network can effectively solve this problem and reduce the average request latency of vehicle users. In summary, existing caching strategies in in-vehicle networks do not simultaneously consider the overhead, revenue, or air resources utilization in the delivery process, so this paper designs a UAV-assisted caching strategy based on content cache pricing in vehicular networks. Specifically, we consider a traffic scenario consisting of a CP, an NO, and multiple mobile subscribers. The CP rents some popular content to the NO for profit, while the NO puts these contents into its RSU's local cache to save on costly backhaul transmission overhead and latency. However, CP and NO are both selfish and their interests conflict with each other because they have opposite expectations on content pricing. Therefore, we model the competition between the VP and MNO as a Stackelberg game to jointly maximize the profits of the VP and MNO. Meanwhile, a UAV with caching capability is utilized to further reduce the request latency and increase the utility of the NO. The innovative points of this paper are as follows:  
(1) A UAV-assisted vehicular network caching model considering the economic relationship between CP and NO is constructed. CP leases some popular contents to NO for benefits and NO caches these leased contents in RSU to save expensive backhaul transmission overhead and latency.