# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 2 Background
### Vehicular Edge Computing: An Overview  
Vehicular edge computing (VEC) represents a great breakthrough emerging in the field of MEC vehicular networks. VEC aims to migrate vital operations such as computing,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **MEC** & **Fog Computing** & **Cloudlet** \\ \hline Node types & Servers in base stations & Routers, access points, gateways & Small data center \\ Deployment location & Network edge & Network edge & Remote network \\ Context awareness & High & Medium & Low \\ Proximity & One or multiple hops & One hop & One hop \\ Scope & Medium & Small & Small \\ Cooperation & No & Partially & No \\ Supporting mobility & Yes & Yes & Yes \\ Business interest & 5G requirements & Internet of Things & Mobile applications \\ Scalability & High & High & High \\ Software architecture & Mobile orchestrator & Fog abstraction & Cloudlet agent \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different edge computing paradigms.  
Figure 1: Edge vs. cloud server.  
communication, and caching resources to be in the proximity of vehicular end devices. Thus, VEC plays an important role in addressing the ever-increasing demands of edge devices with low delay, a fast response time, and high bandwidth. In contrast to traditional MEC, the main contribution of VEC is to provide smooth mobility for vehicles accounting for dynamic topological changes and complicated communication factors due to the rapidly varying channel environment over time [18].  
In general, vehicles handle certain operations through local computation, communication, and storage resources. Edge servers are often placed close to vehicles at roadside units (RSUs) for gathering, processing, and updating data. Due to limited storage, vehicles offload heavy computation and latency-sensitive tasks to edge servers, which can effectively decrease the response time and efficiently alleviate the heavy burden on backhaul networks [19, 20]. The VEC framework can instantly provide the requested content from caching nodes without connecting to the core network, which enhances end-to-end latency and supports efficient network bandwidth [21, 22]. Due to constrained capacity, there is still a level of limitation to cache all the contents. Figure 2 shows the architecture of vehicular edge computing. The vehicular edge computing architecture is illustrated in the following points and in Figure 2.  
* Vehicular terminals: This term refers to any vehicle-connected device rather than ordinary mobile nodes.
* Edge servers: In VEC, RSUs often act as edge servers, which are geographically distributed along the sides of roads. In this research, this will be the edge server device that holds all the design and implementation of the claimed idea.
* Cloud servers: Cloud points usually reside at the very far edge of the network. Cloud servers periodically obtain the uploaded information from edge servers.  
Among mobile edge computing (MEC), fog computing (FC), and cloudlet computing (CC), fog computing is more involved in the computing layer which leverages devices such as M2M wireless routers. The main aim of fog computing nodes (FCNs) is to compute and store data from end local devices prior to forwarding these to the cloud. The other term, MEC, is presented as intermediate nodes with processing and storage functionalities located at the base stations of cellular networks or any edge server in the network. Cloudlets is implemented on dedicated devices with the capability of acting as a lower-scale data center located nearby to consumers. The latter technique allows end-devices to offload computing to cloudlet devices and gain in terms of resource provisioning, just like a data center would [5].