# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work  
In this section, we provide a comprehensive literature review on caching concepts that highlight the part of content popularity, challenges, and cache requirements for VEN, a brief assessment of the optimal pathfinding, using meta-heuristic optimization techniques and pre-caching using cooperative content caching technique. Proposed solutions of different researchers have also been explained and briefly analyzed. The schemes discussed in the literature review are pre-caching content based on popularity and route optimization techniques. Pre-caching improves performance and reduces response time for delay-sensitive applications in vehicular networks. The problem is how to make pre-caching more effective. In [17], the authors consider a large-scale infrastructure to improve the edge system capacity. Vehicular caching solves the given problem efficiently by caching mobile data on distributed vehicles to improve the system throughput and energy consumption. In [18], the proposedscheme preaches the contents based on road topology and zone information. Pre caching zone selection (PCZS) algorithm is proposed using pre-caching node selection (PCNS) under the NS-3 platform to achieve service reliability with improved hit ratio, average delay, and mobility prediction. The algorithm finds the vehicle's motion and sojourn time (time to live in a zone) based on past data so that more data can be cached on a vehicle having a long sojourn time. This way, caching vehicles that stay in a zone for a long time can serve more neighboring vehicles within a minimum period. RSU-based edge caching scheme is proposed in [19] using cross entropy content placement algorithm to minimize the load of the back-haul network. Only popular data is cached in RSU using Zipf distribution. If the requested content is unavailable, then the current RSU fetches the content from neighboring RSUs. Hence data are cached at RSUs cooperatively in case of non-availability of requested data; it is retrieved from the linking base station. Poisson distribution is used to predict the arrival of vehicles in the coverage area of RSUs. The coverage area of RSU is further divided into several zones, considering the vehicle's velocity and traffic density. Different transmission rates are allocated to these zones. A zone at the RSU's coverage area's entrance caches more data. The proposed scheme reduces the Latency while improving the cache utilization; as a result, it gives higher throughput. In [20], a cooperative peer-to-peer (P2P) caching scheme is proposed to share the information among vehicles rather than fetching it again and again from RSU to reduce the response time. Markov chain is used to model the randomly changing behavior of vehicular networks with three states [21]. Probability is calculated to replace the existing data with newly arrived data based on the time consumed in the waiting state and the frequency of accessing data in a given time. The result outperforms in terms of congestion, delay, and hit ratio; however, it has a lack of server (V2I) cooperation that affects the content freshness and scalability. In [22], cloud-based VANET architecture data are cached at numerous layers, named vehicular cloud (VC), local internet cloud (LIC), and then at RSUs cooperatively, to improve the service response time [23]. Caching placement is done by convex optimization problem and simulated annealing (SA), to achieve the caching gain with average low latency. In highway scenarios, vehicles move in identical directions; therefore, a platoon-based mobility model is used. If the requested content is cached in advance by any vehicular cloud member, then it will be satisfied by V2V communication, otherwise fetching will be conducted in V2I communication. Finally, the request is satisfied via a remote server. To maximize the caching gain, all VC members collaboratively decide about caching. SA is a trajectory-based search algorithm starting with an initial guess and final solution at a high temperature then gradually cools down the system.