## 6 Conclusions  
This study focused on the development of a reliable, adaptive, and cooperative SDN-Enabled 5G VANET framework designed to dynamically adjust to network conditions to enhance dynamic QoS management for connected vehicles. In contrast to existing solutions, the proposed framework leveraged an SDN underlay network to achieve 100% packet delivery rate across all tests, demonstrating significant reliability in vehicular communications. As highlighted in the survey presented in [24], there remains a gap in its practical implementations regarding the need to adequately address the complexities of deploying SDN-Enabled 5G Infrastructures, including routing-based interference mitigation techniques. This paper contributes a functional framework and practical insight into the application of SDN principles with next-generation IoT and vehicular networks. Performance evaluations showed that integrating SDN Controller algorithms for real-time traffic and resource management supports the development of scalable and reliable solutions that support the stringent QoS demands of critical vehicular applications. One limitation observed from our study is the introduction of processing delays at the SDN gateway, which requires further optimisation. In addition, this paper focused on reporting the preliminary performance evaluation of the proposed framework and did not include  
Figure 14: Traffic classification comparison with existing AI-based methods in [18; 23].  
Figure 13: Packet delivery rate comparison with existing SDN-based approach in [14].  
comprehensive capacity-testing on a 5G network. Future work will explore these limitations, alongside other key challenges such as energy efficiency for resource-constrained edge nodes, ultra-low-latency demands, and the impact of high vehicular mobility in 5G VANET environments.  
Conceptualization, A.I.; software, A.I.; validation, A.I.; investigation, A.I.; resources, H.T. and R.A.-Z.; data curation, A.I. and R.A.-Z.; writing--original draft, A.I.; writing--review and editing, H.T. and R.A.-Z.; visualisation, A.I.; supervision, H.T. and R.A.-Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study are included within the article.  
The authors acknowledge the support of the University of Salford Computer Networking and Telecommunications Research Centre for providing institutional resources and facilities that contributed to the successful completion and publication of this research.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
3 GPP & Third-Generation Partnership Project 5G & Fifth-Generation Mobile Networks 5G xHAUL & 5G Transport Network AI & Artificial Intelligence AP & Application Plane APIs & Application Programming Interfaces CNN & Convolutional Neural Network C-RAN & Cloud Radio Access Network CP & Control Plane CU & Central Unit DL & Deep Learning DNN & Deep Neural Network DP & Data Plane DU & Distributed Unit eMBB & Enhanced Mobile Broadband gNB & Next-Generation Node B IoT & Internet of Things LiDAR & Light Detection and Ranging LSTM & Long Short-Term Memory ML & Machine Learning MLP & Multi-Layer Perception mMTC & Massive Machine-Type Communications NFV & Network Functions Virtualization NGN & Next-Generation Network ONF & Open Networking Foundation O-RAN & Open Radio Access Network OBU & On-Board Unit OS & Operating System OSI & Open Systems Internetworking QoS & Quality of Service RADAR & Radio Detection and Ranging RAN & Radio Access Network