## 7 Conclusions  
In this work, we have developed a system that addresses the challenge of accurately estimating indoor environments by utilizing RSSI readings from four FNs. We used tracking system approaches based on RSSI to create precise internal positions. We discovered that employing signal averaging techniques and fingerprinting approaches helped accuracy to some degree. However, it was noted that errors still persisted when dealing with weak signals, especially in corners of the area, leading to poor performance across the entire region. The fingerprinting method entailed significant expenditures due to the considerable data collection needed to cover a large area. We suggested using B-spline fitting methods and artificial neural network (ANN) approaches to identify the final location in order to overcome this problem. The study showed that the proposed strategy may achieve location learning with only a small amount of data-by-data augmentation. The target region was gradually divided using this method, \begin{table}
\begin{tabular}{c c c c} \hline
**Target Coordinates** & **2 Step of Quadrant** & **Estimated Coordinates** & **Error Distance** \\ \hline (0, 3) & 0.0002, 0.0018 & (0.38, 2.63) & 0.53 m \\ (1, 3) & 0.0013, 0.9650 & (1.13, 2.63) & 0.40 m \\ (0, 2) & 1.0000, 0.0000 & (0.38, 1.88) & 0.40 m \\ (1, 2) & 0.9974, 0.9976 & (1.13, 1.88) & 0.18 m \\ \hline \end{tabular}
\end{table} 
Table 4: Experiment results after the second step of the PSQ method. Figure 8: Comparison of average error distances calculated based RSME obtained using both methods. \begin{table}
\begin{tabular}{c c c c} \hline
**Target Coordinates** & **1 Step of Quadrant** & **Estimated Coordinates** & **Error Distance** \\ \hline (0, 3) & 0.0006, 0.0024 & & 1.06 m \\ (1, 3) & 0.0087, 0.0170 & & 0.79 m \\ (0, 2) & 0.0331, 0.0046 & & 0.79 m \\ (1, 2) & 0.0998, 0.0020 & & 0.35 m \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental results after the first step of the PSQ method. which was based on the ANN method and used signal analysis and domain segmentation to precisely track the moving node's location. Instead of using lookup tables (LUTs), we approximated the B-spline surface. This approach produced more accurate coordinate values compared to fingerprinting methods. Our proposed model has shown an impressive level of prediction accuracy, as evidenced by the lowest reported position error of 0.18 m. Although there were instances where the sensor's limits were exceeded by the highest position inaccuracy, resulting in 1.06 m, overall, the results demonstrate how well our suggested model can predict target coordinates. The suggested method's adaptability allows for its application in various scenarios, including indoor activities to identify target locations in unrestricted open spaces. Moreover, the internal location tracking system we developed can also be adapted for uses such as portable robots, automated guided vehicles (AGVs), and mobile phones (LBS). However, it may be more difficult to find the optimal weights when the signals have shorter intervals, and accuracy may suffer when fewer location signals are collected than now. In our future research, we aim to enhance the accuracy of the location estimation system by investigating the optimal number of quadrant steps while taking into computational the computational time complexity. In addition, we can extend the application of our PSQ algorithm to outdoor localization by combining RSSI signals and time-series deep learning algorithms as in [36; 37; 38] for precise localization within the cell of an external base station. Conceptualization: K.R.K. and J.H.C. ; Data curation: K.R.K. and A.L. ; Formal analysis: K.R.K. ; Methodology: K.R.K. and J.H.C. ; Writing- original draft: [PERSON] and A.L. ; Project administration: [PERSON]; Writing- review and editing: [PERSON] and J.H.C. All authors have read and agreed to the published version of the manuscript. The present research was supported by the research fund of Dankook University: R20200113 in 2020. Not applicable.