# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 4 Experimental Results
### Performance Analysis
However, the number of handovers of the DoTHa algorithm was still as many as 214, which shows that its ping-pong effect was serious. Although the Mininet-WiFi algorithm had the minimum number of handovers of 10 in this scenario, its Act. TP was 100 Mbps, Avg. TP was 125 Mbps, and PLR was 10%, which was worse than the other three algorithms. The number of handovers for the OHA algorithm was 17. The performance of the OHA algorithm was 119 Mbps for Act. TP and 133 Mbps for Avg. TP, which were 19% and 6.4% better than the Mininet-WiFi algorithm. In order to ensure the QoS requirements of the OHAQR algorithm, the number of handovers was 27. The performance of the OHAQR algorithm was 102 Mbps for Act. TP, 135 Mbps for Avg. TP, and 4% for PLR, which were 2%, 8%, and 60% better than the Mininet-WiFi algorithm. In Scenario 3, the number of handovers of the four methods was less than or equal to two. The Mininet-WiFi algorithm had the smallest number of handovers, but it had the worst performance in Act.TP, Avg.TP, and PLR. The DoTHa algorithm had the best performance in avg. TP and PLR, showing that this is the most applicable scenario. The number of handovers of the OHA algorithm was two, its performance in Act. TP was 60 Mbps, and PLR was 12%, which were 33.33% and 7.69% better than the Mininet-WiFi algorithm, respectively. In order to ensure the QoS requirements, the OHAQR algorithm \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Handover Algorithm**} & \multicolumn{1}{c}{**Mininet-WiFi**} & \multicolumn{1}{c}{**DoTHa**} & \multicolumn{1}{c}{**OHA**} & \multicolumn{1}{c}{**OHAQR**} \\ \hline \multirow{4}{*}{Scenario 1} & Number of Handovers & 17 & 248 & 13 & 15 \\  & Act. TP (Mbps) & 81 & 106 & 106 & 123 \\  & Avg. TP (Mbps) & 127 & 134 & 131 & 135 \\  & Avg. PLR (\%) & 8 & 5 & 7 & 5 \\ \hline \multirow{4}{*}{Scenario 2} & Number of Handovers & 10 & 214 & 17 & 27 \\  & Act. TP (Mbps) & 100 & 101 & 119 & 102 \\  & Avg. TP (Mbps) & 125 & 129 & 133 & 135 \\  & Avg. PLR (\%) & 10 & 7 & 6 & 4 \\ \hline \multirow{4}{*}{Scenario 3} & Number of Handovers & 1 & 2 & 2 & 2 \\  & Act. TP (Mbps) & 45 & 59 & 60 & 68 \\  & Avg. TP (Mbps) & 119 & 132 & 119 & 126 \\  & Avg. PLR (\%) & 13 & 5 & 12 & 9 \\ \hline \hline \end{tabular} Note that Act. TP means actual throughput, Avg. TP means average throughput, and Avg. PLR means average packet loss rate.  
\end{table} 
Table 3: Summary of the experimental results of the four handover algorithms in three scenarios. had two handovers, but its performance was 68 Mbps for Act. TP, 26 Mbps for Avg. TP, and 9% for PLR, which were better than the Mininet-WiFi algorithm by 51.11%, 5.88%, and 30.77%, respectively. Based on the Mininet-WiFi algorithm, when the signal strength between the UE and the currently connected AP is lower than the minimum signal strength, the UE will automatically disconnect the connected AP. Then, the UE starts to search for nearby APs and performs a reconnection process to complete the handover process. This method will increase the number of handovers as the deployment density of APs in the scenario increases, resulting in poor performance in terms of throughput and packet loss rate. The DoTHa handover algorithm only considers a single threshold \(T\) added to an \(HM^{Good}\) or \(HM^{Bad}\) to prevent unnecessary handovers from being triggered. The design of DoTHa handover algorithm is simple and only suitable for sparse wireless network topology. As long as the signal strength of the AP near the UE is greater than or equal to the sum of the signal of the AP currently connected to the UE and the HM, the handover process will be triggered. Since the algorithm mainly considers the handover algorithm designed in the sparse wireless network environment, it had better performance in Scenario 3. However, since the UE could receive a large number of APs with high signal strength in Scenarios 1 and 2, there were frequent handovers.