# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 4 Discussion  
The results obtained in this study confirm the viability of using RSSI for indoor localization, achieving an accuracy of up to two meters under controlled conditions. These findings align with previous studies [31,32], which also demonstrated the viability of RSSI-based localization. However, as observed, the estimation of distances and unknown coordinates using RSSI in IoT networks is practical but subject to the influence of obstacles and interference, which can introduce variability into the results. Regarding the evaluated localization methods, significant differences in their performance were found. Trilateration, although efficient, was highly sensitive to interference, a limitation also highlighted in previous research [33]. In contrast, the Min-Max method showed greater tolerance to such interference but at the cost of a lower accuracy. This behavior highlights the need to strike a balance between robustness and precision in complex environments. The Maximum Likelihood method, on the other hand, proved to be the most accurate, consistent with previous studies that have emphasized its ability to estimate positions more precisely using RSSI [34]. Dynamic localization methods, such as those proposed in [35,36], suggest that these approaches could complement and improve the accuracy in more complex scenarios. Specifically, [35] proposed an RSSI-based localization system for sensor networks that could enhance the estimations in dynamic environments, while [36] explored dynamic localization in sensor networks, indicating that integrating these approaches could be an effective way to improve the accuracy and flexibility of localization systems in changing conditions. Although the results are promising, RSSI-based systems still face significant limitations due to the sensitivity of the measurements to obstacles and interference. This underscores the need to optimize both the propagation models and localization algorithms to improve the accuracy in uncontrolled environments. Combining different localization methods, such as trilateration, Min-Max, and Maximum Likelihood, could be an effective strategy to optimize the accuracy in more complex scenarios, as suggested by previous studies [37,38]. As for future research directions, it is essential to explore the combination of different localization methods to overcome the limitations inherent to each, which would allow for a more robust and accurate solution, especially in complex and dynamic environments. This approach could contribute to the development of more reliable IoT networks, with applications in areas such as logistics, environmental monitoring, and smart city management [39]. An example of this kind of advancement can be found in the article \"An Improved Wi-Fi RSSI-Based Indoor Localization Approach Using Deep Randomized Neural Network\" [40], which presents an enhanced methodology for indoor localization using Wi-Fi signals (RSSI). This approach combines a Deep Randomized Neural Network (RandNN) and an Improved and Adaptive Kalman Filter (IAUKF). Indoor localization is challenging due to signal fluctuations, reflections, and noise in enclosed environments. The proposed method uses the RandNN to process RSSI data and predict the location, while the IAUKF helps reduce the noise and improve the estimation accuracy by smoothing fluctuations. The results show that this approach outperforms the traditional methods, providing greater accuracy and robustness in dynamic environments. This advancement has practical applications in indoor navigation, asset tracking, and location-based services in places such as shopping malls, hospitals, or warehouses. The analysis shows that the Maximum Likelihood algorithm achieves a lower error distance when comparing the real coordinates of the unknown node to the calculated ones. This indicates greater accuracy in position estimation, making it a more suitable option for applications where precision is critical.