## 6 Results
### Latency Results  
Table 8 shows, for the 10 different network topologies studied, the latency value obtained both in the MIP model and in the GRASP, as well as their percentage difference. Moreover, the table also shows the identifier of the nodes pinpointed as critical. As it can be seen, each case is summarised into two rows: the first one shows the results of one critical node removal, whilst the second one shows two critical nodes removal results. As previously stated, the critical nodes in the MIP model are iteratively extracted, and, for this reason, each table row only shows the current node removed. For the case of the GRASP, both tests are independent and, thus, complete results are shown for one critical node and two critical nodes' removal. Regarding the identification of the critical nodes, GRASP offers really good results when extracting the most influential node, delivering a 90% success rate. However, when testing the network for two critical nodes, this rate is reduced to around 65% due to the GRASP failing to identify one or both of the critical nodes. The justification of such low value lies in the fact that MIP and GRASP tests for this scenario slightly differ. Since GRASP nodes are being removed simultaneously instead of iteratively, there might be a combination of two nodes that deteriorates the network more than the iterative removal of the two most influential ones. Network #8 is further analyzed in order to understand why the first critical node is missed. The first important aspect to notice is that the missed and the correct critical nodes are inside the critical node test set selected by the features. Specifically, both are in range of the base station and their neighborhood is inside the top 10. Regarding the solution obtained by GRASP, the relay of both nodes slightly differ. However, it can be seen that both send data directly to the base station. The MIP results deliver the same scenario, but, in this case, the relay is very similar between them. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Latency Values**} & \multicolumn{3}{c}{**Critical Node Ids**} \\ \hline  & **MIP** & **GRASP** & **Gap** & **MIP** & **GRASP** & **Success Rate** \\ \hline \multirow{2}{*}{Network \#1} & 235 & 235 & 0\% & 26 & 26 & 100\% \\  & 236 & 236 & 0\% & 59 & 26, 59 & 100\% \\ \hline \multirow{2}{*}{Network \#2} & 240 & 240 & 0\% & 31 & 31 & 100\% \\  & 242 & 243 & 0.41\% & (60, 84) & 31, 60 & 100\% \\ \hline \multirow{2}{*}{Network \#3} & 231 & 233 & 0.86\% & 70 & 70 & 100\% \\  & 234 & 236 & 0.85\% & 85 & 16, 70 & 50\% \\ \hline \multirow{2}{*}{Network \#4} & 211 & 211 & 0\% & (20, 21) & 20 & 100\% \\  & 211 & 212 & 0.47\% & (32, 77, 86) & 20, (32, 77) & 100\% \\ \hline \multirow{2}{*}{Network \#5} & 214 & 216 & 0.93\% & 23 & 23 & 100\% \\  & 217 & 219 & 0.92\% & (43, 92) & 23, 55 & 50\% \\ \hline \multirow{2}{*}{Network \#6} & 241 & 242 & 0.41\% & (12, 81) & 81 & 100\% \\  & 244 & 245 & 0.44\% & (12, 25, 81) & 25, 39 & 50\% \\ \hline \multirow{2}{*}{Network \#7} & 231 & 232 & 0.43\% & 2 & 2 & 100\% \\  & 234 & 235 & 0.42\% & (53, 91) & 19, 27 & 0\% \\ \hline \multirow{2}{*}{Network \#8} & 248 & 248 & 0\% & 30 & 98 & 0\% \\  & 249 & 250 & 0.4\% & (24, 98) & 43, 98 & 50\% \\ \hline \multirow{2}{*}{Network \#9} & 228 & 229 & 0.44\% & 96 & 96 & 100\% \\  & 234 & 235 & 0.42\% & 77 & 64, 96 & 50\% \\ \hline \multirow{2}{*}{Network \#10} & 233 & 233 & 0\% & 17 & 17 & 100\% \\  & 242 & 244 & 0.82\% & 96 & 17, 96 & 100\% \\ \hline \multirow{2}{*}{Average} & & 0.307\% & & & 90\% \\  & & 0.511\% & & & 65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between MIP and GRASP latency models. Nodes between parenthesis are interchangeable since they offer the same result when being removed. After seeing that the characteristics of the missed and the critical nodes are almost identical, a conclusion can be extracted by looking at the latency results.