# NetSel-RF: A Model for Network Selection Based on Multi-Criteria and Supervised Learning
## 4 Network Selection
### Results and Analysis  
Figure 5 depicts how NetSel-RF, SSF, and AHP-TOPSIS behave regarding the number of handovers, revealing NetSel-RF has a higher number of unnecessary handovers than SSF and AHP-TOPSIS because it suffers from the ping-pong effect in the movements 100, 215, and 415. This ping-pong is caused by \(AP3\) and \(AP4\) offering identical conditions (i.e., the same RSSI level and percentage of users connected to AP) to \(dev1\).  
Figure 5: Number of handovers: NetSel-RF without movements module.  
To address the ping-pong effect, we enhanced NetSel-RF with a movements module. This module uses a row vector to store the variable \(AP\_target\) as well as the last seven locations (latitude and longitude) of end-devices. A movement is given by a location change. Thus, the handover is performed only when \(AP\_target\) has the same value for all seven movements; otherwise, the end-devices remain connected to their current AP. It is important to highlight that we performed tests by considering 3, 5, 7, and 9 movements to choose the proper number to eliminate the ping-pong effect.  
Table 6 exemplifies two vectors containing \(AP\_target\). NetSel-RF does not perform any handover for the \(AP\_target\) presented in the first row since this row jumps between \(AP2\) and \(AP3\). Conversely, since the \(AP\_target\) is the same for the seven location changes (movements) in the second row, NetSel-RF carries out the handover. In practice, we filled this vector for all devices in movement by using Mininet features: \(dev.params[\"associatedTo\"]\) and \(dev.params[\"position\"]\).  
Figure 6 depicts the evaluation results regardubg the number of handovers carried out by the enhanced NetSel-RF, AHP-TOPSIS, and SSF. These results reveal that NetSet-RF and AHP-TOPSIS outperform SSF regarding this evaluation metric. In particular, the number of handovers for RSSI, AHP-TOPSIS, and our model are 9, 3, and 3, respectively.  
Figure 7 shows the evaluation results regarding the throughput drops of the enhanced NetSel-RF, AHP-TOPSIS, and SSF. These results reveal our model and AHP-TOPSIS outperform SSF regarding this evaluation metric. In particular, NetSel-RF and AHP-TOPSIS suffer 3 throughput drops (\(0 Mbps\)). In turn, SSF has 7 drops. In summary, according to Figures 6 and 7, the multi-criteria based approaches behave better than SSF regarding the number of handovers and throughput drops.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Variable**Movement** & **Mov1** & **Mov2** & **Mov3** & **Mov4** & **Mov5** & **Mov6** & **Mov7** & \\ \hline AP\_target & AP2,loc1 & AP2,loc2 & AP2,loc3 & AP3,loc4 & AP2,loc5 & AP3,loc6 & AP2,loc7 & X \\ AP\_target & AP2,loc1 & AP2,loc2 & AP2,loc3 & AP2,loc4 & AP2,loc5 & AP2,loc6 & AP2,loc7 & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: AP_target example.  
Figure 6: Number of handovers: NetSel-RF with movements module.  
Figures 6 and 7 reveal that NetSel-RF behaves similarly to AHP-TOPSIS regarding the number of handovers and throughput drops. However, there is an essential difference between our model and AHP-TOPSIS. NetSel-RF performs transfers earlier than AHP-TOPSIS (as shown in Figure 8). This is due to the fact that our model is proactive and AHP-TOPSIS is reactive. Thus, NetSel-RF selects a new AP before suffering a disconnection. The proactive approaches are more effective than reactive ones regarding QoS and QoE since the end-devices perform the handover before the network link quality degrades, meaning that end-devices are always connected [38].