## 4 Conclusions  
This paper proposed a WiFi-based fingerprint localization scheme for indoor environments. The proposed scheme includes an AP filtering method (PONE selection) to remove noise and enhance positioning precision, as well as an Adaboost-based localization method to accurately detect the position of any device. Extensive experiments in campus-building \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Total Error** & **3D Error** & **2D Error** & **Building Error** & **Stair Error** & **Reference** \\ \hline AdaBoost & 4.45\% & 0.00\% & 28.51\% & 0.33\% & 71.16\% & - \\ SVM & 6.86\% & 0.00\% & 41.05\% & 0.27\% & 58.68\% & [24] \\ Decision Tree & 6.05\% & 1.66\% & 32.43\% & 0.53\% & 65.38\% & - \\ KNN & 6.49\% & 6\% & 29.72\% & 11.25\% & 52.71\% & [23] \\ BP & 16.68\% & 24\% & 60.53\% & 14.99\% & 32.21\% & [25] \\ ANN & 7.86\% & 2\% & 42.76\% & 1.41\% & 53.73\% & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of localization errors for different localization algorithms: the proposed Adaboost-based localization vs. other algorithms. Figure 7: Comparison of 2D localization error distances for the proposed AdaBoost-based localization and other machine learning-based algorithms for various RSS thresholds. environments were conducted to evaluate the scheme in real-world settings, and its performance was compared to other well-known machine learning-based algorithms in terms of positioning accuracy. The results demonstrate that the proposed PONE selection method achieves higher localization accuracy than the two commonly used AP filtering methods. The proposed positioning system exhibits high accuracy, with minimal impact from device heterogeneity, suggesting that training a single Adaboost model for all devices is feasible rather than training individual models for each device. In summary, the proposed solution is scalable, easy to implement, and capable of achieving high accuracy. We will also address some limitations of this study, which are being left for future research. While we have measured WiFi signal strengths using five different smartphones, and these signals are inherently time-varying, the 70:15:15 random splitting of datasets for training, validation, and testing in this study could pose a _data leakage_ issue. This random splitting of datasets can result in optimistic performance estimates. Moreover, the performance comparison in this study was performed only with widely accepted classification methods since new advanced methods have improved the accuracy by focusing on different aspects of the localization process. For example, they address a robotic platform for eliminating the need for predefined maps and reducing human intervention [18], provide a semi-supervised learning framework leveraging unlabeled WiFi fingerprints [33], and utilize a hierarchical neural network architecture for WiFi fingerprints [34]. Investigating how to effectively combine these new features to enhance localization accuracy in WiFi environments is also an important area of future study. In addition, the performance comparison was conducted only with our own measured datasets, resulting in a limited conclusion, for which comparisons with other datasets are left for future study. Finally, the devices used to measure WiFi signal strengths in this study were from two commercial vendors, consisting of three different models. While investigating the effect of mobile devices on the localization accuracy is another important issue, we left it for future study since it should be carefully tested under environments agreed upon by participating vendors. Original draft preparation, analysis, S.J. ; and organization, review and editing, supervision, D.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the research fund of Hanyang University (HY-2020). The data presented in this study are available on request from the corresponding author, due to privacy restrictions. The authors declare no conflicts of interest.