# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work
It accepts the solution if it is better, otherwise, it relies on the probability that causes weak exploitation. SA often converges very slowly, and often finds global optimal solutions at the expense of a large number of function evaluations. The study [24] focuses on the distribution of multimedia and large-size files in high-mobility VANETs, by caching the updated contents at RSUs. Three algorithms are used to retrieve the files at RSUs: (1) optimal one: reduces downloading time by exhaustive searching but computational complexity will increase; (2) sub-optimal: allocates the most requesting files (using [PERSON]'s distribution) by doing substitution frequently; (3) greedy algorithm: fully occupies the RSUs using current best caching after clearing it. The complexity of these algorithms is calculated with the number of RSUs, storage capacity of an RSU, and vehicle speed. Requesting vehicle's entrance time is calculated first, and then the large file is divided into small parts. Then, distributed in RSUs cooperatively; that are deployed in the same sequence. In this way, the average download time of the vehicle for large files will reduce until and unless the vehicle remains in the same direction. The result shows that the average delay reduces by 70% as compared to no caching in RSU. As compared to other networks, available resources in VENs are limited. Therefore, proper utilization of these resources is compulsory; that can be done by clustering of nodes. Study [25] uses the nature of whales for cluster optimization. The given study outperforms grey wolf optimization (GWO) and ant lion optimization (ALO) in the number of cluster heads, transmission range and size of search space, and number of nodes [26]. Cooperative multi-tier edge caching is proposed, in which location-based and popular contents are fetched cooperatively from servers in [12]. Large files are scrambled first using fountain code and then cached at RSU/BS, based on optimal location and data segments. Cached contents are analyzed by the downloading rate and the time a vehicle takes to complete the coverage area of a server. The knapsack problem is formulated for content placement and the ant colony optimization algorithm (ACO) is used for optimality. These components lead to the shortest path with a rapid solution and distributed computation to avoid premature convergence. More ants give the best optimal solution in case of local search. It finds the optimal route, but the shortest path causes increased congestion. Moreover, it is not suitable for large search spaces. According to the study [15], a vehicle fetches the contents from RSU or BS at a time, similarly, the information-centric network (ICN) relies on the host-centric model, which reduces congestion and improves delivery regarding data storage. Moreover, ICN has a simple configuration with data-level security. Proactive caching with mobility prediction (PCMP) is proposed using ICN, based on named data network (NDN) architecture to download the content. At first, it calculates the number of chunks to download and cache the data at the next predicted RSU. Long short-time memory (LSTM) is used to predict the next RSU in any nearest direction. In the LSTM model, Euclidean distance is calculated for more accurate prediction in the current and next RSU. RSU selects the next hop and sends the pre-requested chunks to it. The study outer performs in delay, hop selection, and cache utilization, but focuses only on RSU's prediction and caching, while there is a lack of V2V caching. In [27], the optimized link state routing (OLSR) protocol is used to moderate the performance of VANET. A routing table is maintained for all routes. The harmony search (HS) algorithm is used to configure the parameters of OLSR by coupling two stages: roulette wheel selection and tournament selection. It sends a hello message among all nodes; after receiving a hello message, each node creates its network topology independently and runs the Dijkstra algorithm to select the shortest route towards the destination.