## 5 Discussion
However, it was evident that, when the speed increases beyond 50 km/h (e.g., 50-250 km/h in the autobahn scenario), the cost, latency, and packet delivery ratio decrease for the proposed routing, as the average link lifetime of the network tends to decrease with the increment in vehicular speeds. Even though the average link lifetime decreased in the range 0-50 km/h, the effect of the average network link entropy increment must have been more effective than the average link lifetime drop in that low speed range (0-50 km/h), which caused all communication cost, latency, and PDR to increase in that range for the proposed routing method. Here, we statistically analyze the significance of the routing results obtained for the proposed framework compared to other approaches. Let us consider the three null hypotheses given below. Note that we compare the significance of a given parameter (cost, latency, and PDR) of the proposed method against the same parameter of another routing technique, which has overlapping or intersecting 95% CIs with the proposed method at some data points. * H1 --The communication cost of the proposed routing is higher than AODV;
* H2--The end-to-end latency of the proposed routing is higher than Dijkstra;
* H3--The PDR of the proposed routing is lower than AODV. If the probability \(p\) (how likely it is that the data could have arisen by chance under the assumption that the null hypothesis is true [95]) is less than a significance level (a significance level of 0.05 is considered for this research), the alternative hypothesis can be accepted by rejecting the null hypothesis [96]. Let \(P_{H1}\), \(P_{H2}\), and \(P_{H3}\) represent the \(p\)-value for Hypothesis H1, the \(p\)-value for Hypothesis H2, and the \(p\)-value for Hypothesis H3, respectively. As per the results, \(P_{H1}<0.05\) except for the network size of 4 nodes, which had a \(P_{H1}\) value of 1.00. Therefore, the alternative hypothesis of H1 (the cost of the proposedrouting is lower than AODV) is statistically significant under any link lifetime threshold, any routing frequency, any mobility scenario, and for networks larger than four nodes. Furthermore, according to the results, \(P_{H2}<0.05\) except for network sizes of 4 nodes, 8 nodes, 16 nodes, and 32 nodes, and for maximum speed values of 0 km/h, 30 km/h, and 50 km/h in the autobahn mobility scenario, with \(P_{H2}\) values of 0.50, 0.37, 0.28, 0.11, 0.15, 0.22, and 0.17, respectively. Thus, the null hypothesis H2 can be rejected against the alternative hypothesis of H2 (the end-to-end latency of the proposed method is lower than Dijkstra) for all instances except for smaller networks (less than or equal to 32) and at low speeds (0-50 km/h) in the autobahn mobility scenario. Finally, we analyze the statistical significance of the results on PDR. According to the results on routing, the value of \(P_{H3}\) is less than 0.05, except in the instances given in Table 4.  
Therefore, the null hypothesis (H3) can be rejected, and the alternative hypothesis (the PDR of the proposed method is higher than AODV) is statistically significant for link lifetime thresholds in the range [0.1, 2.0], routing frequencies greater than 0.10 Hz, network size greater than 64 nodes, and the urban mobility scenario at speeds 20 km/h, 50 km/h, and 60 km/h. However, even though hypothesis H3 cannot be rejected at the network instances given in Table 4, due to the associated values of statistical significance, note that the average value of the PDR of the proposed method is always higher than AODV, except at link lifetime thresholds greater than 6.0 and at a network size of 4 nodes. The significance of this research is that the proposed routing framework results in higher reliability and lower latency on average for routing in SDVNs by selecting routes that have both high link lifetimes and a lower delay. Furthermore, the proposed routing framework effectively reduces the communication cost by using an adaptive flow rule computation, updating, and installation approach.