# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 4 Experimental Results
### Performance Analysis  
In the above three scenarios, we used the built-in handover algorithm of Mininet-WiFi, the dual HM handover algorithm used by DoTHa [14], and the proposed OHA and OHAQR methods to compare the performance of the system. The experimental results included number of handovers, handover processing time, network transmission performance, and average throughput. Furthermore, to further demonstrate our methods, time-stamp-based statistics were collected from the experiments on the aforementioned switching algorithms, and we employed results performed in high-density topologies to clearly indicate the statistical distribution during the experiments. In Scenario 1, the UE moved from the position of API to the position of AP2 in a straight line at a walking speed, and then moved from the position of AP2 to the position of AP3 in a straight line at a walking speed. In Scenarios 2 and 3, the UE started to move within the coverage of the seven APs and moved in the direction randomly calculated by the \"random direction\" mobility model. In the mobility model, the connection mechanism we applied was strongest-signal-first (SSF), because it more actively switched the UE's connection to the AP that provides its best connection to prevent the UE from continuously connecting to the least-loaded APs with poor signal quality [23]. The iperf network traffic generation tool [24] was used as a test tool for network transmission performance, and the type of data transmitted was user datagram protocol (UDP) [25] packets. The test period of a single transmission was 1 s, and the maximum throughput transmitted during every period was 150 Mbps. In Scenario 1, four handover algorithms, Mininet-WiFi, DoTHa, OHA, and OHAQR, were used to observe the RSSI variation in UE during the experiment, as shown in Figure 5. Using the built-in handover algorithm of Mininet-WiFi in Scenario 1, it can be found in Figure 5a that the first connection was established at 8 s after the start of the experiment. During this period, the RSSI of the connection between the UE and the AP remained very stable because it was caused by connecting to the AP with the strongest RSSI. However, in Figure 5b, the handover algorithm of DoTHa was used. During the entire experiment period, a large number of cases of disconnection and reconnection occurred after the AP was disconnected. This is because in a high-density network environment, the signals affect each other, and the connected AP is frequently changed (causing the RSSI of the UE to be intermittent), resulting in the occurrence of the ping-pong effect. As shown in Figure 5c,d, the OHA and OHAQR algorithms proposed in this study made the RSSI of the UE remain stable most of the time. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Scenario 1** & **Scenario 2** & **Scenario 3** \\ \hline Number of APs & 7 & 7 & 3 \\ AP Coverage & 125 m & 125 m & 25 m \\ AP Operating Mode & 802.11 ac & 802.11 ac & 802.11 ac \\ Channel Testbed & 5G Hz & 5G Hz & 5G Hz \\ Testruns & 1000 & 1000 & 1000 \\ UE Mobility Model & Random Direction & Random Direction & Random Direction \\ UE Moving Velocity & 0.9\(\sim\)1.5 m/s & 0.9\(\sim\)1.5 m/s & 0.9\(\sim\)1.5 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The settings of the simulation environment parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Value** & **Symbol** & **Value** \\ \hline \(T\) & \(-\)70 dBm & \(HM2^{ndoad}\) & 40 dBm \\ \(HM^{Good}\) & 50 dBm & \(HM2^{ndndnd}\) & 20 dBm \\ \(HM^{Bad}\) & 30 dBm & \(T^{QoS}\) & 0.78 \\ \(T^{5-HO}\) & \(-\)70 dBm & \(HM^{QoS}\) & 10 \\ \(HM^{5-HO}\) & 5 dBm & \(RSSI^{Max}\) & \(-\)1 dBm \\ \(T^{U}\_HO\) & \(-\)88 dBm & \(RSSI^{Min}\) & \(-\)90 dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The parameter settings of the proposed algorithm.