# Cooperative Content Caching Framework Using Cuckoo Search Optimization in Vehicular Edge Networks
## 3 Related Work
In [38], the authors determine the optimal path with the lowest routing cost using a hybrid whale dragonfly optimization approach with rider-integrated cuckoo search (RI-CS) to find the best route with minimal routing cost. The study shows the denial-of-service attack and allocates bandwidth to the devices in VANET, by evaluating the targeted energy consumption. It searches for the device with low or dropped bandwidth and then provides the required bandwidth by measuring the distance. But pre-caching and clustering are not used for end-node cooperation to minimize the delay ratio and to gain more throughputs. The authors in [39] proposed a giraffe-kicking optimization algorithm (GKO). It uses hybrid C-means to find the redundant sensor nodes to avoid energy consumption and to improve packet delivery consumption. As C-means is an iterative clustering to avoid dissimilarities among the data points. Table 1 summarizes the significant related works. \begin{table}
\begin{tabular}{c c c c c} \hline
**References** & **Year** & **Technique** & **Problem** & **Considered Parameters** & **Limitations** \\ \hline
[18] & 2020 & \begin{tabular}{c} PCZS, PCNS with \\ N-S3 simulator \\ \end{tabular} & \
\begin{tabular}{c} Pre-caching for none delay \\ tolerate applications in \\ vehicular networks \\ \end{tabular} &
\begin{tabular}{c} Dynamic adaptability, \\ reduce response time \\ and increase cache \\ hit ratio \\ \end{tabular} & Accurate location \\ \hline
[19] & 2018 & \begin{tabular}{c} Cross entropy with \\ Poisson distribution \\ \end{tabular} & \
\begin{tabular}{c} Caching selective data at \\ RSUs because of large \\ content size and short \\ storage capacity in fully \\ distributed networks \\ \end{tabular} &
\begin{tabular}{c} Improved performance \\ in delay, cache hit ratio \\ and overhead \\ \end{tabular} & \\ \hline
[22] & 2017 & SA & \
\begin{tabular}{c} Improved caching \\ placement policy to \\ maximise caching gain \\ \end{tabular} &
\begin{tabular}{c} Minimise latency and \\ improve user’s quality \\ of experience \\ \end{tabular} & Cost, find only global \\ \hline
[24] & 2018 & \begin{tabular}{c} Optimal1, \\ Zipf distribution \\ \end{tabular} & \
\begin{tabular}{c} Delivering multimedia \\ files within limited \\ storage capacity \\ \end{tabular} &
\begin{tabular}{c} Pre-caching files to \\ reduce download time, \\ efficient delivery \\ \end{tabular} & Storage capacity, \\ \hline
[25] & 2021 & \begin{tabular}{c} Whale optimisation \\ \end{tabular} & \
\begin{tabular}{c} Based on the clustering to \\ find the optimal path \\ \end{tabular} &
\begin{tabular}{c} Cluster head manage the \\ cluster for \\ better performance \\ \end{tabular} & Time, \\ \hline
[12] & 2020 & ACO and knapsack &
\begin{tabular}{c} Fetching location based \\ and popular contents from \\ multiple caching servers. \\ \end{tabular} & Delay, service cost, finds \\  & & local best solution \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the related work. The above-mentioned related work shows that CS outer performs optimal route finding and popularity prediction on the basis of user preferences to pre-cache the data, which is a more suitable option to entertain the user conveniently. It will also improve the cache hit ratio while caching more files nearest to the user in high mobility. Complexity analysis of the state-of-the-art and baseline techniques is shown in Table 2 and the abbreviations used in the study are defined in Table 3.