## 6 Conclusions and Future Research  
This paper presents a routing framework for software-defined vehicular networks. The proposed framework generates link lifetime and link delay matrices with the aid of machine learning and selects its operation mode using the computed average normalized network contention parameter. Wireless link lifetime estimation using a DNN resulted in a much lower computational time compared to the optimization-based approach for large networks, thus enabling the proposed routing technique to estimate wireless link lifetimes while satisfying latency requirements in vehicular networks. Both DNNs predicting wireless link lifetime and one-hop channel delay proved to be accurate, having RMSE values of only 0.000331 and 0.00071, respectively. The results related to routing \begin{table}
\begin{tabular}{l l} \hline
**Network Instance** & **P\({}_{H3}\)** \\ \hline Link lifetime threshold of {0.0, 4.0, 6.0, 8.0, 10.0} s & {0.19, 0.07, 0.30, 0.78, 1.00} \\ \hline Routing frequency of {0.02, 0.06, 0.10} Hz & {0.42, 0.28, 0.09} \\ \hline Network size of {4, 8, 16, 32, 64} nodes & {0.50, 0.32, 0.29, 0.20, 0.08} \\ \hline Urban mobility scenario at speed {0, 10, 40} km/h & {0.32, 0.19, 0.07} \\ \hline Rural mobility scenario at speed {0, 20, 40, 60, 80, 100} km/h & {0.16, 0.33, 0.29, 0.30, 0.25, 0.21} \\ \hline Autobahn mobility scenario at speed {0, 30, 50, 90, 130, 170, 210, 250} km/h & {0.30, 0.34, 0.39, 0.37, 0.35, 0.33, 0.32, 0.31} \\ \hline \end{tabular}
\end{table} 
Table 4: Table of \(p\)-values when the hypothesis H3 is statistically significant. performance evaluation showed that, once the optimum link lifetime threshold (200 ms) is selected, the proposed routing technique for SDVN yields the highest packet delivery ratio, the lowest latency, moderate channel utilization, and the least cost, on average, compared to routing using AODV and Dijkstra. Results further proved that the performance of the proposed technique improved with increments in routing frequency and network size, and at moderate speeds (0-50 km/h). Thus, it can be concluded that the proposed routing technique improves routing in SDVNs. Furthermore, the results of this study prove that machine learning is more suitable for wireless link lifetime prediction than a computationally less efficient optimization-based approach. As this is the first study to scrutinize machine learning for exact one-hop channel delay prediction, which resulted in only a RMSE of 0.00071 under low contention scenarios, deep learning can be recommended for one-hop channel delay prediction given that the network normalized contention value is lower than 0.5. Based on the results, in order to deploy the proposed routing framework in a SDVN, an optimum link lifetime threshold set in the range of 200 ms to 1000 ms is recommended to obtain the best performance from the proposed routing framework, as setting a link lifetime threshold too high or too low causes relatively poor packet delivery ratios. As the average latency values resulted from the proposed routing lie much below the maximum latency allowed for vehicular communication, as discussed in Section 5, the proposed routing framework can be recommended to be used in an SDVN without any hesitation. Statistical analysis further proved the significance and superiority of the proposed routing framework compared to AODV and Dijkstra, where the proposed routing technique always resulted in statistically significant results for link lifetime thresholds in the range of 100 ms to 2000 ms, network size greater than 64, and routing frequency greater than 0.10 under any mobility scenario except the PDR. Even though it was not statistically significant, the average value of PDR for the proposed method was always higher than other routing techniques under any mobility scenario. This research fills a gap in the existing literature by estimating the link lifetimes and delay of the heterogeneous links, using machine learning along with a novel hybrid routing algorithm and an adaptive flow table update algorithm.