## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Software-Defined Networking
To solve the issues in both communication and computing while meeting the QoS needs of diverse applications, researchers, in [75], present an Autonomous Vehicular Network (AVNET) framework that considers the SDN and NFV ideas in MEC. MEC facilitates effective collaboration among various wireless networks. Moving computing tasks to the network edge, such as in cooperative driving, enhances response times and data transmission speed. MEC servers can store and process crucial information such as HD maps. By separating the control and data layers and integrating SDN with MEC hosts, a cohesive control interface is established without additional installations. This approach enables global network control for optimizing resource usage and efficient traffic management. The strategy proposed in [76] uses SDN to create a unified control layer for efficient connection and task management, addressing reliability and improving the mobile user experience. Researchers developed an Extended Forwarding Module (EFM) allowing access points to communicate with the SDN controller. This controller handles service classification, mobility control, and network resource development. To manage mobility in MEC-enabled 5G and VNs, four components were created, including a performance monitor module. The 5G-MEC design incorporates SDN enhancements and Soft-varied Multiple Access Management Services (S-MAMss), optimizing resource utilization and allowing mobile users to connect to multiple services simultaneously using different radio access protocols. In ref. [77], scientists investigated Xavier-Convolutional Neural Network (XCNN) and Distribution-Satin Bowerbird Optimization (CD-SBO) algorithms with caching approaches performed with the help of a vehicles' cache memory for MEC-assisted IoV and using SDN for energy-efficient resource allocation. For data processing, each RSU is outfitted with a MEC server. To improve communication efficiency, vehicles are grouped based on RSU coverage using a clustering algorithm. The model includes using Kullback-Leibler Divergence-K-Means Algorithm (KLD-KMA) and CD-SBO algorithm for load balancing and data parsing, achieving lower latency compared to other schemes. To give precedence to services related to road safety in emergency scenarios, researchers in [78] presented a fog (MEC)-level adaptive slicing framework with the support of SDN/NFV-based IoV. The idea is to periodically offload resources to handle emergency traffic load, and then restore the original resource allocation when the situation is back to normal. The results obtained demonstrate that the latency, which is the paramount QoS parameter for emergency services, is decreased by temporarily transferring certain resources from the entertainment slice to improve the resources in the road safety plane. In ref. [79], authors investigate the cooperative optimization problem for congestion control and compute offloading in multiserver vehicular edge computing networks. They suggest an approach considering balancing network load for computation offloading relying on Asynchronous Advantage Actor-Critic (A3C) in the automotive edge domain supported by SDN. Additionally, they create a resource provisioning and offloading scheme. The MEC host is installed in the RSU, where a Cellular-Vehicle-to-Everything (C-V2E) or Dedicated Short-range Communication (DSRC) mechanism enables V2I. RSUs may communicate with several cars using MIMO technologies. SDN is used to assist the collaboration of the heterogeneous Vehicular Edge Computing Network (VECN) design in order to provide traffic balancing among several servers with offloading workloads simultaneously. SDN controllers are often located in BS or remote system units, which can centrally monitor and regulate the requests and information from the data layer.