## 5 Discussion
The proposed routing framework yields a higher PDR based on two facts. The first one is that only stable links exist in the computed routes, ensuring that the routes really exist at the time of routing. The second one is that, when there are multiple links between two given nodes in different communication channels, it examines the link lifetime of the two candidate links and selects the link with the highest lifetime (in the highest stable least distance mode) or examines the combined effect of link lifetime and link delay (in the highest stable least delay mode) and chooses the communication channel that will ultimately lead to higher reliability compared to other routing techniques. Although there is no widely accepted standard value for the maximum tolerable communication cost for routing in SDVN, it is always better to use a routing approach that results in the least communication cost. The communication cost metric that we calculated depicts the average communication cost per node per routing cycle, which includes both control plane communication and data plane communication costs that occurred while forwarding packets from given sources to destinations. In all research experiments, the proposed routing technique yielded the lowest average communication cost under any link lifetime threshold, routing frequency, network size, or mobility scenario under any speed, compared to Dijkstra and AODV, which is a remarkable achievement. This result verifies the strength of the proposed adaptive flow rule computation, updating, and installation approach, which updates the switches with the latest flow rules only when the flow rules installed in the switches expire, which results in the least cost compared to proactive flow rule installation in Dijkstra and the distributed routing approach in AODV. Optimization can be used to achieve different objectives in computer networks. In [89], ant colony optimization has been utilized for cooperative multiple task reallocation under target precedence constraints for unmanned aerial vehicular networks. Furthermore, swarm intelligence algorithms such as particle swarm optimization, artificial fish farm, etc. have been proposed to optimize tasks in vehicular networks [90]. Similar to these studies, we modeled wireless link lifetime prediction as a non-linear optimization problem. Wireless link lifetime prediction using deep learning was experimentally proven to be much more computationally efficient than the optimization-based approach, which has a constraint with fourth-order terms. Deep learning utilizes the power of batch predictions and large data set training to accurately predict link lifetimes with much lower computational complexity compared to optimization. Therefore, deep learning stands out as a scalable solution for wireless link lifetime prediction, as the latency of the optimization-based approach extends to the order of seconds, as proved by the results. Both wireless link lifetime prediction and one-hop channel prediction tasks require high accuracy in order for the proposed routing framework to be effective. Deep learning has been proven to provide highly accurate regression when trained with large labeled data sets [91]. Another reason for the choice of deep learning for prediction tasks is that deep learning is scalable and can handle large volumes of data at once, using its capability for batch predictions [92]. The input vector sizes of deep learning models for wireless link lifetime prediction and one-hop channel delay prediction are 104 and 30, respectively. However, predictions are made for the whole network by using batch predictions rather than predicting for individual links or individual nodes one after another. Thus, the total maximum input data size per network becomes \(52N^{2}\) and \(60N\) for wireless link lifetime prediction and one-hop channel delay prediction, respectively, which can result in a large volume of input data when the network size grows.