## 5 Discussion and Conclusions  
### Discussion  
Through the experimental results in Section 4.3, it is clear that MSVR can effectively improve the localization accuracy compared with SVR regression. Meanwhile, the improved IPSOGWO algorithm reduces the average localization error by 26.1% and 30.9% in the two scenarios compared to the standard PSOGWO algorithm. This indicates that the improved algorithm can find the optimal grey wolf individuals in iterations, providing globally optimal parameters for MSVR. The experimental results in Section 4.4 show that the performance of the IPSOGWO-MSVR algorithm is still the best among the four methods, but we notice that the error of the IPSOGWO-MSVR algorithm rises more after switching from Scenario 1 to Scenario 2. We analyze that a possible reason for the increased error is the greater distance between the transmitter and receiver devices in Scenario 2. Additionally, the signal carries more ambient noise after reflecting from the multipath, affecting the model's localization performance, indicating that our algorithm's ability to resist the multipath is still relatively weak. In our next study, we will focus on the modeling of the localization performance of the model and work on improving its stability, incorporating more advanced noise reduction and feature extraction techniques.  
Furthermore, the practical implications and applicability of the IPSOGWO-MSVR algorithm can be extended to various real-world scenarios. In healthcare settings, for instance, the algorithm's ability to accurately track the movement of patients or devices within a hospital, while ensuring privacy, is paramount. The incorporation of CSI information allows for precise localization without necessitating patients to carry any device, ensuring comfort and privacy. In industrial settings, the IPSOGWO-MSVR algorithm stands out for its noise resistance, making it ideal for tracking assets and managing the workforce  
Figure 13: CDF of localization accuracy in different scenarios for four algorithms. (**a**) Scenario 1; (**b**) Scenario 2.  
with high accuracy and reliability. Its enhanced precision ensures real-time tracking with minimal errors, boosting safety and efficiency.  
For other wireless technologies, such as LoRa, BLE, and Zigbee, we have not conducted practical tests and may need to make adjustments considering the potential applications of the algorithm. For example, LoRa and Zigbee have low data transmission rates and need to consider the problem of signal delay. For BLE, our algorithms may be directly applicable, but signal interference and multipath issues need to be considered. In addition, this paper does not use the deep learning-based approach. This is because the approach requires a large amount of data training to achieve better results. We will further consider these aspects in our future research and conduct practical tests to verify the performance of our algorithm.  
The long-term stability of our model is a critical aspect that we have thoroughly considered. The performance of fingerprint localization is inherently influenced by the dynamic nature of indoor environments. Factors like room layout, temperature, and humidity can significantly impact the signal propagation and, subsequently, the localization accuracy. For instance, when testing the model under varying temperature and humidity conditions, we observed a fluctuation in RMSE of 15%. This underscores the need for adaptive mechanisms to maintain optimal performance over time. One promising approach to mitigate these challenges is the integration of crowdsourcing techniques. By continuously gathering and integrating data from a diverse set of sources, the model can be dynamically updated to adapt to the evolving conditions of the indoor environment. Our future work is dedicated to further enhancing the model's long-term stability, making it a reliable solution for a broader range of applications and scenarios.