## 5 Discussion
Deep learning models are better suited for such large data predictions than other traditional supervised machine learning approaches. We trained the DNNs using large labeled data sets of 3,368,564, and 3,350,000 samples for wireless link lifetime prediction and one-hop channel delay prediction. Furthermore, it has been noted that deep learning models are well-suited for non-linear regression tasks because they can learn non-linear relationships between the input and output variables [93]. The wireless link lifetime prediction problem is an optimization task whose solution can be approximated by a polynomial combination of 8 th-order terms of differential position, velocity, acceleration, etc. The relationship between the input variables and the output for the one-hop channel delay prediction is unknown. Therefore, to learn complex, unknown relationships between inputs and outputs, deep learning is well suited. Finally, deep learning models used for regression tasks have shown superior generalization performance over traditional machine learning approaches [94]. This is really important, as we train the machine learning models using a data set collected from some network scenarios that may not represent all states of the data. Thus, the regression models should have the capability to generalize, in order to predict unseen data with a low prediction error. Therefore, deep learning is an excellent choice to train the wireless link lifetime and one-hop channel delay using data collected from a network scenario, allowing them to generalize for unseen data. Wireless link lifetime prediction and one-hop channel delay prediction yielded very low RMSEs of only 0.000331 and 0.00071, respectively, proving to be highly accurate for the test data set. Such a low error in predictions must have been because we trained deep learning models using large labeled data sets for DNNs to learn complex relationships and generalize from input data, in order to accurately predict wireless link lifetimes and one-hop channel delays. Due to its low prediction error, it was not necessary to experiment on other machine learning techniques, as the main purpose of this research was to propose a novel routing approach for SDVNs, where link lifetime and delay predictions can be considered as sub-tasks in achieving the main purpose of routing. As the results on routing, based on DNN predictions, were proven to be superior over other routing techniques compared in this research, and, as discussed above, the existing literature proves that deep learning is superior for complex non-linear regression tasks when trained with large labeled data sets, there was no requirement to experiment with other machine learning techniques. Therefore, the accuracy of DNNs was highly satisfactory for making routing decisions. The proposed routing technique is a link lifetime- and link delay-based routing framework that ensures that routing will only occur through stable links. For that purpose, as described in Section 3.4, the link lifetimes are inspected to check whether the link lifetime is greater than a specified threshold in order to determine the stability of the links. Metadata for computing routes is collected by the controller before scheduling routes. The links should be stable (exist) at least until the packet is delivered to the destination. Otherwise, if a very low link lifetime threshold such as 0.0 is set, links with a very low lifetime (say, 20 ms) will most probably not be available at the time of routing. On the other hand, if a very high link lifetime threshold is set, links that have a moderate link lifetime (say, 1 s) and really exist at the time of routing will not be added to the routes by the algorithm due to the high threshold. Thus, the optimum value should be selected experimentally. In Section 4.4.1, we experimentally found out that the optimum link lifetime threshold value was 200 ms, since link lifetime thresholds from 200 ms to 1000 ms gave the highest packet delivery ratio under the experimental settings.