# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 1 Introduction  
Due to the rapid development of network technologies, wireless devices have been widely adopted over the past few decades, and people mostly rely on them. The devices are very large in numbers and need to be connected for reliable communication, especially in peer-to-peer (P2P) applications, where nodes are sharing the data instead of centralized servers [1]. The vehicular network provides vehicle-to-vehicle communications, but it isn't easy to fully satisfy the users in distributed and highly dynamic networks without accurately predicting requested data and nodes [1, 2]. VEN uses dedicated short-range communications to ensure reliable data delivery in minimum time spam, and data are shared using Wi-Fi or cellular networks. Vehicular communication is performed by installing on-board units (OBUs) on vehicles [3]. The whole network architecture is based on the number of layers. The top layer is the cloud network (CN), connected to MECs through back-haul links that are further connected to RSUs. Roadside units are deployed near roads based on particular vehicular regions and managed by base stations [3], as shown in Figure 1.  
As the vehicle passes through many RSUs in high mobility, complete content cannot be accessed from a single caching node [1]. Due to the highly dynamic vehicular network, caching the complete contents at a single RSU is problematic, as the time between RSU and the end node is tens of seconds [4]. Hence content placement and delivery are two separate issues in a short time. Users only demand more secure and efficient data access without being interested in content delivery location [5]. The vehicle fetches data from RSUs and shares the traffic information (traffic safety and management) with the other vehicles. If the content is not cached at RSU, it is fetched from the base station (BS), thus increasing the fetching cost and delay. Due to limited storage capacity, computational resources, and high mobility, pre-caching is required. Caching increases content availability and reduces the request response time; however, caching has numerous challenges that affect the network performance such as network topology, caching placement, delivery on time, and optimal route [6]. In [7], cost-optimal cooperative caching is proposed, considering the finite archive capacity and bandwidth, to achieve content popularity with freshness. The proposed scheme in [7] optimizes content placement and delivery with the cooperation of BS, RSU, and smart vehicles. Double time scale Markov optimization is used, as content updating time is much faster than the vehicle's movement. Data are updated before placement in the content delivery process by vehicle scheduling (routing table management) and allocating bandwidth on different time scales. Vehicles that recently entered in coverage area will remain for a long time in the zone; therefore, it is more reliable to store and share more data in Adhoc mode. A tradeoff occurs in response time and content freshness in high-mobility networks. To address this problem, the content can be cached at RSU and then updated periodically, considering the optimal parameters [8]. Although to ensure the delivery of fresh content, resources are consumed. In [9], the authors optimized the throughput and energy  
Figure 1: Vehicular edge network architecture. consumption by caching the content in vehicular environments using cellular networks. The data is cached on distributed vehicles that serve as task schedulers for user requests to minimize the response time. However, the given scheme lacks cooperation regarding data sharing among vehicles and servers, which affects the content freshness. Study [10] considers the cache deployment problem in long-term evolution vehicle-to-infrastructure (LTE-V2I) networks with highly dynamic VENs that jointly optimize the cache size with maximum average download percentage, considering the following parameters: vehicle speed, arrival rate, and content popularity.