## 4 Key Technologies for MEC-Assisted Vehicular Networks
### Software-Defined Networking  
SDN consists in employing software-based controllers or Application Programming Interfaces (APIs) to communicate with the core hardware architecture of the network. Instead of running management protocols within standard networking devices such as switches, SDN manages the routing of data packets in virtual networks or conventional hardware through a centralized server called the _SDN controller_. Network virtualization helps network operators to build various virtual networks within only one physical network or to connect devices on different physical networks to create a single virtual network. In the rest of this section, we present recent works on SDN-enabled VNs. In ref. [68], authors introduce a system that combines deep learning algorithms with 5G technologies (network slicing, MEC, SDN) to support applications in order to provide safety in IoV. They integrate the 5G slicing functionality using the SDN paradigm to enable flexible resource allocation to improve heterogeneous autonomous car applications' KPIs (e.g., low response time and low packet drop requirements). Then, they propose to employ AI techniques in an autonomous car to check driver conditions remotely and report serious incidents exclusively to the Remote-Control Center (RCC). The need for bandwidth is significantly reduced because of the integration of AI methods and the MEC paradigm. Additionally, the MEC approach is utilized in order to provide safety servers and to make it easier to meet the delay targets. The entire pipeline begins with the video stream that has been recorded by the smartphone and proceeds via machine learning processes to assess a driver's drowsiness. Lastly, they proposed SDN as a 5G slicing implementation approach to deliver vital signals to the control center with the necessary level of QoS. Their suggested method achieves a considerable improvement in average Round-trip Time (RTT) and throughput across many scenarios. In the current design of edge-based Vehicular Ad Hoc Networks (VANETs), each edge node operates independently, lacking efficient techniques for interaction among the involved smart objects, potentially leading to a load-balancing issue [5]. To address this challenge, researchers, in [69], have developed a three-level routing hierarchy based on the Dijkstra algorithm for an enhanced SDN and MEC-assisted VANET architecture. This model employs a control system to provide best-effort services for the VANET while considering the operational states of edge nodes. It incorporates a vehicular trajectory prediction algorithm to enhance network transfer efficiency. By utilizing edge devices for distributed computing and transmission, this approach can significantly reduce system processing load, improve system performance, and accelerate service response times. The routing hierarchy integrates three communication forms: V2V, Vehicle-to-Edge (V2E), and Edge-to-Controller (E2C). This integration offers protocol-independent routing, making effective forwarding decisions for various applications. The hierarchy is designed to manage dynamic and unpredictable VANET environments. The architecture is illustrated in Figure 6, and experimental results demonstrate that the proposed three-level routing hierarchy in the upgraded SDN-MEC-VANET design significantly enhances performance. Figure 6: SDN-MEC-VANET architecture [69]. There are challenges in an SDN-based MEC architecture: first, how to address the communication issues derived by single link failure and maintain the performance of the network by handling latency problems. Secondly, how to place the controller to minimize network cost. Work [70] addresses these two challenges and proposes a mobility-aware solution, focusing on an adaptive deployment technique, and a reliable controller placement approach based on deployment cost and link failure probability. Firstly, the deployment cost content is presented.