# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 7 Conclusions and Future Work  
In this study, we proposed a hybrid localization algorithm that replaces only the ranging part of the existing range-based localization method with a deep regression model that uses data-driven learning. For the ranging part, the accuracy of distance estimation in an indoor NLOS environment is improved by using the dual-band RSS, and this is designed to cover various cases by using regression with a neural network. The improvements in the ranging and positioning accuracy of the proposed method were verified through a ray-tracing-based simulation for general indoor spaces. Furthermore, the proposed localization method is compatible with various situations, even when the indoor structure is changed. The prevailing fingerprint-based methods are cannot be applied in the situation we assumed where the deployment of APs are different compared to the offline stage.  
Based on the results of ray-tracing based simulation, the use of dual-band RSS could reduce the median of positioning error up to 2.56 m, which was a 9.9% smaller error than the error obtained with the single-band RSS alone in the rule-based localization. By applying the deep regression method to the ranging part, the median error of 1.99 m was obtained, which was a 22.3% improved result compared to the rule-based method. Considering that the use of deep regression with single-band RSS only caused 10% or less performance gain, the combination of the dual-band RSS and the deep regression method was effective in improving the accuracy of range-based localization using RSS.  
The robustness of the proposed method encourages the use of RSS localization because it can reduce the intensity of the site survey process. In addition, the proposed method has the advantage that it can be implemented by using only the beacon signals from APs, without any special communication overhead. This will be useful for small IoT devices that are difficult to mount with multiple types of sensors or high cost network interface controller.  
However, some future research should be followed to apply the proposed method in various situations. In this study, we considered the wall structure as a major factor of the complex indoor channel. In the real environment, there are many small obstacles that cannot be reflected in the ray-tracing based simulation, and the RSS fluctuations caused by the multi-path effect. In addition, analysis of more diverse materials and interior structures is required. After all, for a deep regression ranging model that operates robustly in various environments, as much experiments as possible are required to train and to validate the model.  
Nevertheless, the proposed method is still promising in that a properly trained ranging model can be used even in an untrained environment. In practice, we expect to increase the learning efficiency by using a transfer learning technique that tunes the model trained through simulation with relatively small dataset from real experiments. Furthermore, extending the research to the triple-band-based ranging method is possible by utilizing 6 GHz band communication, which is expected to be supported in the Wi-Fi 6E standard.  
Conceptualization, B.-h.L. and S.-C.K.; methodology, B.-h.L. and K.-M.P.; software, B.-h.L. and K.-M.P.; validation, B.-h.L. and Y.-H.K.; formal analysis, B.-h.L.; investigation, B.-h.L. and K.-M.P.; resources, B.-h.L.; data curation, B.-h.L.; writing--original draft preparation, B.-h.L., K.-M.P. and S.-C.K.; writing--review and editing, B.-h.L., K.-M.P. and Y.-H.K.; visualization, B.-h.L.; supervision, Y.-H.K. and S.-C.K.; project administration, S.-C.K.; funding acquisition, S.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018 ID).  
Not applicable.  
The authors declare no conflict of interest.