## 5 Discussion  
For the SDVN architecture, we proposed a novel routing technique. We used the prospect of machine learning to estimate the wireless link lifetimes and channel delay at each hop using the metadata collected at the controller, where the mode of routing was decided by inspecting the knowledge generated as normalized network contention. The hybrid routing algorithm is a novel algorithm which has two modes: the highest stable least delay mode and the highest stable least distance mode, in which the mode was decided by using computed normalized network contention. Furthermore, if multiple links exist between two given nodes, both wireless and wired, the proposed method will effectively choose the best link considering the link lifetime and link delay parameters. Furthermore, we proposed an adaptive approach for flow rule computation, update, and installation, to effectively reduce the communication cost. Thus, the proposed routing approach ensures that routing will only occur on stable links selected using the link lifetime matrix and delay matrix generated with the aid of machine learning, resulting in a higher PDR (reliability), a lower latency, and a lower cost than the other routing techniques compared in this research. We compared the proposed routing framework's performance against Dijkstra, which is a shortest path algorithm for routing in SDVNs, and against AODV, which is used in VANET. The amount of time between a packet being created at a source and it being delivered to its destination is known as latency. In other words, it is the longest air interface delay that may be tolerated in direct mode. When sending V2X messages from a UE supporting one V2X application to an RSU via another UE supporting a V2X application, vehicular communication may handle a maximum delay of 500 ms. Furthermore, a minimal 100 ms latency for human-assisted driving is permitted for the Basic Safety Message (BSM) broadcast and equivalent applications of the Collaborative Awareness Message for V2V safety [76]. However, BSM does involve only one-hop broadcasts, and the previous 500 ms maximum is defined for a two-hop scenario. Thus, end-to-end latency per hop should be compared with the standard to check whether latency violations occur. As per the results, the average latency of the proposed method in a network of 256 nodes was only 106.24 ms, which was for a multihop scenario. Thus, the multihop average latency of the proposed routing method lies well below the maximum 500 ms latency defined for a two-hop scenario for vehicular communication. Therefore, the proposed routing method is well suited to be employed in vehicular communication applications without violating latency constraints. The proposed routing technique reduces latency by selecting the highest stable least delay mode under low contention scenarios, which effectively selects stable and low delay paths for routing. Although the proposed adaptive flow rule computation, updating, and installation approach may introduce some latency compared to the proactive approach, it has been successfully nullified by the highest stable least delay mode in the proposed routing technique, which has caused it to yield the least latency among the routing techniques. The maximum allowable packet loss rate at the application layer is referred to as reliability [88]. Based on transmission length and use, the trustworthiness (reliability) of vehicular communication is specified. Reliability for human-aided driving is typically as low as 20% when using the cooperative awareness message for V2V safety and the Basic Safety Message (BSM) broadcast [76]. In multihop scenarios, the PDR can be poor in vehicular networks due to the presence of isolated nodes in the network. It was proven in the results section that the average PDR of the proposed routing framework can be as high as 43.6% in large vehicular networks, which is much higher than that of Dijkstra (25.2%)and considerably higher than routing in VANET, which uses AODV (32%).