# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 8 Conclusions  
By the seamless merging of MEC and vehicular networks, VEC extends the functionalities of the cloud toward the edge of networks, allocating resources in close proximity to vehicles. Several IoV applications are connected to the edge of their network to fulfill the increasing requirements in terms of computation and storage resources. Technically, the collected data from these vehicles have to be transmitted somewhere else to be processed, such as in the cloud. From the earlier designs, the cloud was considered to be too far from the vehicles. In addition, traffic services/applications require very fast and accurate real-time data processing near the vehicles. Edge computing is a solution for vehicles that supports high resource and data processing closer to the vehicle's device. However, during fast mobility, vehicles encounter slow connection while traveling from one edge server realm to another. This research proposed a framework that guarantees and enhances the connection between the vehicle and edge computing servers. The edge server performs the role of continuously supporting a stable connection for traveling vehicles. The proposed CCVEC framework is implemented and evaluated using OpenStack cloud technology besides three cluster servers. Results have been measured with the throughput, latency, and memory of the entire framework in two different scenarios. We conclude that the proposed framework is performed with acceptable results by spending 163 ms with 100 loads, and the latency increases while the load increases to spend 283 and 373 ms in 200 and 300 loads, respectively. These results are still valid and the values remain within acceptable latency percentages. Also, scenario 2 reflects the same perspective with 500 loads applied, where the throughput is only 60 kB/s from the ideal status. We conclude that the general finding of this research is to facilitate and smoothen the transmission of the vehicle from one realm to the next. Also, vehicle applications are supported with wise decisions in terms of the connection with edge selection. Future works plan to extend the idea by involving new collaborations between vehicles by interchanging vital information on the go which will boost the level of the latency profile and be beneficial from a security perspective while sharing sensitive traffic data.  
A.A.-A. and P.L.; methodology, A.A.-A., P.L. and A.M.; validation, A.A.-A. and P.L.; formal analysis, A.A.-A., P.L. and A.M.; investigation, A.A.-A. and P.L.; data curation, A.A.-A. and P.L.; writing--original draft preparation, A.A.-A. and P.L.; writing--review and editing, A.A.-A., P.L. and A.M.; supervision, P.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CC & Cloudlet Computing \\ CCVEC & Cluster Collaboration Vehicular Edge Computing \\ CPU & Central Process Unit \\ CVEC & Collaborative Vehicular Edge Computing \\ FC & Fog Computing \\ FCN & Fog Computing Nodes \\ ICMP & Internet Control Message Protocol \\ ID & Identification \\ IoT & Internet of Things \\ IoV & Internet of Vehicles \\ IoFV & Internet of Flying Vehicles \\ IP & Internet Protocol \\ \end{tabular}