# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 1 Introduction
The proposed scheme uses a joint cost optimal algorithm to optimize the cost in terms of time. The Poisson process is used for vehicular mobility prediction and Zipf popularity distribution for content caching. It reduces the response time with data availability. VENs enable vehicle-to-vehicle (V2V) and vehicles-to-infrastructure (V2I) communication. Data are cooperatively cached at RSUs and base stations and fetched from the cloud so vehicles can access the data from the nearest station [11]. The vehicular node comprises an on-board unit (OBU), and RSUs provide access to a wide range of coverage areas [12]. Both have limited storage capacity, but RSU's storage capacity is comparatively greater than OBU's. The RSU fetches the predicted content from the upper layers and then delivers it to the connected vehicles on demand, as shown in Figure 2.  
Problems in VENs are limited storage resources and rapid network topology change [13]. Pre-caching reduces the latency and increases the cache hit ratio; therefore, less bandwidth is required to access the data. Mostly content is cached on the bases of zones, freshness, and popularity. Response time increases to ensure content freshness, although some applications are not delay tolerant. Meanwhile, content popularity compromises freshness. Given work shows, that tradeoff occurs in freshness and popularity. An appropriate caching and optimization strategy can escalate the content availability to ensure the cache hit ratio and response time while considering the tradeoff factors. Moreover, to find an optimal solution to pre-cache the requested data on limited storage devices, so that data could be delivered on time to maximize the cache hit ratio. The major contributions of the paper are given as,  
* We propose a cooperative content caching method for the VEN environment. The proposed system increases the cache hit ratio and resource utilization while reducing the network delay. * We use the K-means clustering for multi-tier caching servers (base stations and road-side units) to cache the single content at multiple servers cooperatively. * We develop communication models for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I), content caching and delivery models that are further used by cuckoo search algorithm (CSA) to determine vehicle location and optimize content delivery. * We conduct extensive experiments exploiting the geo-life trajectory data to predict the requesting node's position and MovieLens dataset for user contents. Figure 2: Content caching in VENs. * The experimental results reveal that the proposed framework outperforms the traditional ant colony optimization (ACO) in cooperative and non-cooperative caching scenarios. The rest of the paper is organized as follows. Section 2 discusses the motivation behind the presented research with a gap analysis. Section 3 details the literature review. The problem statement is formulated in Section 4. In Section 5, different system models are discussed. Section 6 describes the proposed framework and techniques involved in it. The performance evaluation is described in Section 7. Section 8 concludes the paper, and finally, some limitations of the existing works compared with the proposed one, and some future research directions are discussed.