# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 6 Cooperative Content Caching Framework
### Optimisation Using Cuckoo Search
The results reveal that COOP (CS) performs better than COOP (ACO). Because the convergence time of CS is more than ACO, CS takes random steps and each step is independently chosen, while ACO takes continuous and small steps, and each new step depends on the previous steps. Due to dependencies and continuous search of ACO, it takes more time to find the optimal path, so it causes an increase in delay. Cooperatively caching the popular content will also reduce the caching and delivering cost; moreover, searching data from clusters also reduces the response time while increasing the hit ratio. Overall, the proposed technique outperforms in terms of delay, hit ratio, caching, and delivery cost. ## 8 Conclusions

In this study, we explored content caching in vehicular edge networks to improve performance through cache utilization, increase the cache hit ratio, and reduce response time. This study indicates that caching can minimize network traffic and enhance user productivity. It has been observed that pre-caching content depending on its popularity,

Figure 12: Caching cost with increasing number of files. Figure 13: Delay with the increasing number of users. increases the cache-hit ratio. The approach caches data based on the content's popularity as determined by user choices and the cooperative content caching technique. RSU anticipates the popularity of data and distributes this information with neighboring RSUs to prevent data duplication on servers nearby. RSU also encounters the node's mobility by using the Markov model to predict it based on previous observations, distance, and time. Thus, when a user requests data not present in its local cache, it is retrieved from neighboring servers instead of downloaded over the back-haul link. In addition, the user's longitude and latitude have changed due to mobility. Therefore, RSU delivers the content to the user's true location after discovering it. We then use a meta-heuristic optimization technique to discover the fastest path to the desired material. After analyzing several optimization algorithms inspired by nature, it was concluded that CS is superior at discovering the optimal path. COOP (CS) can substantially improve cache performance, cache hit ratio, and response time. The simulated trials are conducted with actual Movielens data, and the experimental results reveal that COOP outperforms the cache hit ratio and response time in vehicle edge networks. ## 9 Limitations and Future Research Direction

The present technique considers the content's popularity, which boosts the cache hit ratio, but the response time is still somewhat slow. There is a lack of collaboration between caching servers before data caching. For future work, we should emphasize different types of dynamic optimization problems with randomly changed global optima and caching different types of content simultaneously with textual data. In addition, popular content is pre-cached in this study and sent to mobile consumers using optimal routing. If the content is unpopular, it is retrieved from the back-haul server, which increases the response time and service costs. Our future emphasis will be placed on caching various types of content. Those contents include movies and videos. We also intend to exploit the neural networks-based recommendation technique to predict popular content for caching purposes and compare it with our results. In addition, recurrent neural networks can also be utilized to enhance the accuracy of user location prediction. Conceptualization, S.K.u.Z., S.M. and H.A. ; methodology, T.M., A.D.A. and H.E. ; software, S.K.u.Z. and M.A. ; validation, F.R., H.A., T.M. and M.A.K. ; formal analysis, S.K.u.Z., M.A. and F.R; investigation, S.K.u.Z. ; resources, H.E. and A.D.A. ; data curation, M.A.K. and S.M. ; writing--original draft preparation, S.K.u.Z., H.A. and M.A. ; writing--review and editing, S.K.u.Z., H.A. and M.A.K. ; visualization, S.M. and F.R. supervision, S.K.u.Z. and T.M. ; project administration, S.K.u.Z., H.E., A.D.A. and M.A.K. ; funding acquisition, A.D.A. and H.E.