## 6 Experiment
### Design and Training of the Neural Networks in DLR+  
The general architecture of the neural networks used in DLR+ was presented in detail in Section 4.2. As noted, all of the parameters were left as variables, meaning that they can be further adjusted in future versions as desired. The neural networks considered in this work are deep feed forward neural networks with two hidden layers, which provide the capability to capture complex non-linearities in the system. This way, the networks consisted in an input layer, two hidden layers, and an output layer. As explained in Section 4.2, the number of neurons in the input layers is the number \(n\) of features to process from each sample in the classification process. For this version of DLR+, for the host's fitness, eight different features \(x_{i}\) were considered, plus an additional eight features \(x_{j}=x_{i}^{2},\ 1\leq i\leq 8\), to help capture nonlinearities, for a total of \(n=8\) input features, listed in Table 2.  
For the second neural network (the one that takes care of the messages fitness), we used a total of \(m=3\) different features, described in Table 3. We also included the squared features during the training process, but did not notice any gains in accuracy, so we decided to take them out. As for the number of neurons in the hidden layers, following the suggestions shown in Section 4.2 and seeking a short computational time, we opted for \(n_{h1}=\ 14\) and \(n_{h2}=\ 10\). In a similar way, we decided to use \(m_{h1}=5\) and \(m_{h2}=3\) for the messages' neural network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Nodes** & **ID** & **Buffer Size (MB)** & **Speed Range (m/S)** & **Interface** & **Description** \\ \hline
1 & 10 & p1 & 5 & 0.5-1.5 & Bluetooth & A group of pedestrians \\
2 & 10 & p2 & 5 & 0.5-1.5 & WAVE 802.11p@Mps & Another group of pedestrians \\
3 & 5 & b1 & 10 & 2.7-16.7 & WAVE 802.11p@Mps & A group of buses \\
4 & 10 & b2 & 10 & 2.7-16.7 & WAVE 802.11p@12 Mps & Another group of buses \\
5 & 15 & c1 & 15 & 5.5-22.22 & WAVE 802.11p@12 Mps & A group of low-speed cars \\
6 & 15 & c2 & 15 & 5.5-22.22 & WAVE 802.11p@24 Mps & Another group of low-speed cars \\
7 & 10 & c3 & 20 & 8.3-30.56 & WAVE 802.11p@12 Mps & A group of high-speed cars \\
8 & 10 & c4 & 20 & 8.3-30.56 & WAVE 802.11p@24 Mps & Another group of high-speed cars \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Group of nodes in the simulation. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Name** & **Description** \\ \hline \(x_{1}\) & Host speed & Speed (m/S) at which the vehicle is moving \\ \(x_{2}\) & Transmission speed & Transmission speed of the communications link (Mbps) \\ \(x_{3}\) & Transmission range & Maximum radial distance (m) at which the host can \\  & & & connect to other nodes \\ \(x_{4}\) & Avg number of connections & The number of connections, on average, that a host handle \\ \(x_{5}\) & Buffer size & Buffer size (MB) \\ \(x_{6}\) & Buffer occupancy & Percentage of buffer occupancy \\ \(x_{7}\) & Dropping rate & Rate at which a host drops packets \\ \(x_{8}\) & Abort rate & Rate at which a host aborts packet transmissions \\  & \(x_{i}=x_{i-8}^{2},\text{ for }9\leq i\leq 16\) & Composite features to help capture non-linearities \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Features considered in the first neural network (for host fitness) in DLR+. \begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Name** & **Description** \\ \hline \(y_{1}\) & Residual TTL & Also known as time-out ratio, is the ratio of the \\  & & remaining TTL to the initial TTL \\ \(y_{2}\) & Message size & Size of the message (Bytes) \\ \(y_{3}\) & Hop count & The number of nodes that the message has \\  & & traversed so far \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Features considered in the second neural network (for message fitness) in DLR+.