## 4 Results
### Performance of Wireless Link Lifetime Prediction and One-Hop Channel Delay Prediction Using DNNs  
The training curves of DNNs for wireless link lifetime and one-hop channel delay predictions as well as the computational complexity comparison of wireless link lifetime prediction models are shown in Figure 15.  
It is very clear in Figure 15a that all DNN models' losses (Mean Squared Error) decayed exponentially with the number of epochs, such that all models were trained successfully. At the end of training, the wireless link lifetime prediction model's loss was as low as 0.0000010, while that of one-hop channel delay prediction under low contention was as low as 0.00000049. Therefore, the wireless link lifetime prediction DNN was successfully trained to fit the actual wireless link lifetimes of vehicular networks, while the one-hop channel delay prediction model was successfully trained to fit the exact one-hop channel delay under low-contention scenarios. However, under high-contention scenarios, the loss in the one-hop channel delay prediction machine learning model at the end of training was as high as 0.000019, such that it was less suitable for one-hop delay prediction under high contention scenarios, as evident from Figure 15a, even though the training converged. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Transmission protocol & User Datagram Protocol (UDP) \\ \hline Communication channels & DSRC for (I2V,V2I,V2V), point to point between RSUs, CSMA from RSU to controller \\  & node, and LTE between vehicles and controller node \\ \hline Wifi-standard & IEEE 802.11p \\ \hline DSRC transmission power & 33 dBm (urban), 41 dBm (non-urban), 44 dBm (highway) \\ \hline DSRC OFDM data rate & 27 Mbps \\ \hline DSRC propagation loss model & Cost-Hata (urban), 3-log distance (non-urban,autobahn) \\ \hline DSRC propagation delay model & Constant speed propagation delay \\ \hline DSRC error rate model & Nist error rate model \\ \hline LTE pathloss model & Cost-Hata \\ \hline LTE maximum transmit power & 23 dBm \\ \hline LTE SRS periodicity & 2, 5, 10, 20, 40, 80, 160, 320 \\ \hline LTE fading model & Trace fading loss model \\ \hline LTE EPC data rate & 1000 Mbps \\ \hline RSU backbone data rate & 1000 Mbps \\ \hline RSU backbone delay & 10 μs \\ \hline Payload size for routing & 104 bytes \\ \hline Broadcasting status data payload size & 104 bytes for proposed routing, 56 bytes for Dijkstra, 0 bytes for AODV \\ \hline Unicasting uplink status \& metadata payload size & \(24\sum_{m\in\mathcal{S}_{i}}(\mathcal{N}_{m}Z_{m}+\frac{1}{3})+104(\mathcal{N} _{i}+1)+8\) bytes for proposed routing, \\  & \(24\sum_{m\in\mathcal{S}_{i}}(\mathcal{N}_{m}Z_{m})+56(\mathcal{N}_{i}+1)\) bytes for Dijkstra, 0 bytes for AODV \\ \hline packet\_in & 24 bytes for proposed routing, 0 bytes for Dijkstra and AODV \\ \hline FlowMod payload size & 76 bytes for proposed routing, 68 bytes for Dijkstra, 0 bytes for AODV \\ \hline Communication cost per byte & 1—wired, 2—DSRC, 40—Cellular \\ \hline Routing frequency & Variable (\(f^{\prime\prime}\)) in the range [0.02, 5] \\ \hline Number of nodes & Variable (\(\mathcal{N}\)) in the range [4, 256] \\ \hline Link lifetime threshold & Variable (\(T_{th}\)) in the range [0, 10] \\ \hline Network contention threshold & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._The test data set for wireless link lifetime prediction consisted of 673,712 samples using data collected entirely from a vehicular network. The DNN had a loss (mean squared error) of 0.00000011 and an RMSE of 0.000331 for the testing data set. As the wireless link lifetimes were normalized using a scale of 100 s, RMSE mapped to an error of only 33.1 ms between the predictions of the DNN and real wireless link lifetimes. Therefore, the DNN does a very good job of predicting wireless link lifetimes, as the 33.1 ms error was much smaller than the 100 s time frame.