## 3 Experiments and Analysis
### Performance Evaluation  
Figure 4 quantitatively analyzes the variation in positioning accuracy under different RSS thresholds used in the proposed filtering method, PONE selection. The RSS thresholds are explained in Equations (3) and (4), and the number of decision tree-based weak classifiers is set to 60. The experimental results in Figure 4 show that the accuracy of the Adaboost algorithm with multiple decision trees is significantly improved compared to the single decision tree algorithm. Additionally, the localization accuracy of Adaboost is the highest among all tested localization algorithms along with the tested PONE thresholds. The proposed PONE method achieves the best positioning accuracy (95.55%) when the RSS filtering threshold is set to \(-90\) dBm. Figure 3: Examples of the RSS data from grids #4425 and #4426 on the fourth floor of Engineering Building 4. Figure 2: The left figure shows a map of two campus buildings (Engineering Buildings 3 and 4 at Hanyang University, ERICA, in Korea) connected by a bridging corridor on each floor. The right one demonstrates the grid structures (in yellow) on the fourth floor of Engineering Building 4. We conducted an analysis of how RSS thresholds affect positioning accuracy by examining the number of positioned APs at different RSS thresholds. The results of the cumulative distribution function (CDF) are shown in Figure 5. When we combine the findings from Figures 4 and 5, we see that as the threshold value increases, the number of positioned APs decreases, leading to a decrease in positioning accuracy. However, we observed that when the thresholds are set to \(-100\) dBm and \(-95\) dBm, the dataset contains a significant amount of noisy data, resulting in lower localization accuracy compared to the threshold set at \(-90\) dBm. As mentioned earlier, the method used to filter access points (APs) has a significant impact on the accuracy of a localization algorithm. To assess the effectiveness of the proposed AP filtering method, we compared it with other filtering methods (MaxMean [20] and Stable AP [21]) in terms of the accuracy of various matching algorithms. MaxMean is a commonly used AP filtering method that selects APs with higher average received signal strength (RSS) values. The Stable AP method ranks APs based on the product of the frequency of each detected AP and the variance of its received RSS value. In contrast, the proposed AP filtering method, PONE, sets the RSS threshold of the device to \(-90\) dBm. The experimental results in Table 1 demonstrate that the localization accuracy of PONE is superior to the other two methods. MaxMean and Stable AP do not account for fluctuations in RSS values received from the same AP at different times, resulting in larger positioning errors. By using a threshold RSS value to filter APs, the proposed PONE method mitigates the unreliability of RSS values in the fingerprint data, thereby enhancing positioning accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **PONE** & **MaxMean [20]** & **Stable AP [21]** \\ \hline AdaBoost & 95.55\% & 88.50\% & 92.56\% \\ SVM & 93.14\% & 91.28\% & 91.80\% \\ Decision Tree & 93.95\% & 82.14\% & 91.85\% \\ KNN & 93.51\% & 81.77\% & 91.93\% \\ BP & 83.32\% & 54.52\% & 72.89\% \\ ANN & 92.14\% & 75.55\% & 90.41\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of localization accuracy for different AP filtering methods. Figure 4: Variation in positioning accuracy under different RSS thresholds used in the proposed PONE AP selection. Different WiFi modules at the receiver from various vendors have different received signal gains. This means that devices may receive different RSS values from the same access point, even when they are in the same location. To test the proposed algorithm in such environments, we used it to estimate the positions of five different devices, and the results are displayed in Figure 6. We set the RSS filter threshold to \(-90\) dBm for all devices.