## 4 Results
### Configuration of the Simulation Environment
#### 4.2.1 Configuration for Routing  
Ad Hoc On-demand Distance Vector (AODV) is a reactive routing protocol that is used in distributed VANETs in which routes are obtained on demand [68]. [PERSON]'s algorithm, which is a shortest path-finding algorithm, has been used as a routing algorithm for SDVN [69]. Thus, we used [PERSON]'s algorithm for routing in SDVN and AODV for VANET, in order to test the effectiveness of the proposed routing framework for the SDVN paradigm. Configuration of Wireless Link Lifetime and Channel Delay Prediction Model  
For wireless link lifetime prediction using optimization, we used GuRoBi commercial optimizer version 10.0.0 as the optimization tool. A comparative analysis that compared GuRoBi to other commercial optimization solvers such as CPLEX and XPRESS revealed that GuRoBi provides fast solutions due to its support for different optimization problems, multi-threading capability, and automatic linearization of problems [70]. We implemented the optimization model using a Python script, where we used the GuRoBiPi Python library to find the link lifetimes. As the transmission power of a DSRC network device at a given node is variable (33 dBm for an urban scenario; 41 dBm for a rural scenario; 44 dBm for an autobahn scenario), the maximum transmission distance of a given wireless link (\(D_{ij}^{max}\)) is also variable in the network. We spawned 100 RSU nodes and 100 vehicle nodes in pairs and moved each vehicle node away from the RSU in the \(x\) direction, from where the vehicle node broadcasts its position to the RSU. When the RSU no longer receives broadcasts from the vehicle node, the difference between the \(x\) coordinate of the RSU node and the last received broadcast is computed as the maximum transmission distance. In this manner, for each of the mobility scenarios, the mean and variance of the maximum wireless transmission distances for the 100 pairs are calculated. Thus, the \(D_{ij}^{max}\) required for finding the link lifetime was modeled as a normal distribution with a mean and variance calculated as above. Note that there were three sets of normal distributions of \(D_{ij}^{max}\) for each of the mobility scenarios. Accordingly, it was experimentally found that the mean and standard deviation of the maximum transmission distance for the urban scenario are \(156.1\pm 0.3\) m, the rural scenario are \(270.4\pm 0.5\) m, and the autobahn scenario are \(332.3\pm 0.7\) m.  
For collecting data sets for wireless link lifetime and one-hop channel delay prediction neural network training and testing purposes, we created a network having 100 vehicle nodes and 50 RSUs for each of the mobility scenarios, with 60 km/h as the maximum speed of vehicles for urban mobility, 100 km/h as the maximum speed for rural mobility, and 250 km/h as the maximum speed for the autobahn mobility scenario. If the neural networks are trained using only the data collected from a single vehicular network, there can be a bias in the data set, as the data are not uniformly distributed in their range of values in a given vehicular network. Data set bias can cause inaccurate training and over-fitting. Thus, in order to have a uniform distribution of training data, we obtained data from three different mobility scenarios with different maximum velocities in the network. Collecting a data set for wireless link lifetime prediction--A similar approach to finding maximum transmission distance was used for obtaining wireless link lifetimes and status data from the vehicular network, which are used to train the wireless link lifetime prediction DNN.