## 5 Conclusions  
For the requirement to notify users of their locations in indoor sensor networks or cognitive IoT networks, we decided to add an adaptive indoor localization system to deal with such a phenomenon. Accordingly, this paper proposed a novel NLOS acoustic signal identification method and an adaptive dual-receiving acoustic signal processing method for indoor localization systems. With these, we were able to obtain better indoor localization results in complex and flexible NLOS situations. Our key works are as follows: (1) The acoustic anchor of the dual-receiving structure was added in complex and flexible NLOS scenarios. First of all, an RSS selection mechanism and an improved TDOA algorithm were adopted to accomplish the entire indoor localization without the clock synchronization part. This structure greatly improved the acoustic signal transmission effect in complex and flexible NLOS scenarios. (2) As for the conventional NLOS acoustic signal identification part of DT, an advanced method, namely CTGSA-AFSA, was introduced to upgrade the classifier. It contains an adaptive chaos transformation strategy, improved GSA optimization method, and adaptive physical attenuation model for the initialization process, which enhances the classifier's ability to identify NLOS acoustic signals in complex and flexible scenarios in total. In our experiments, our novel AFSA optimization method could obtain a better effect and took less time compared  
Figure 10: Experiment situations. (**a**) The reference anchor; (**b**) single receiving experiment situation; (c) Dual-receiving experiment situation. with the original version. There is no doubt that our method can balance the relationship between accuracy and running time in flexible NLOS situations. Then, different classifiers or methods were used for comparison in man-made flexible NLOS scenarios. Finally, the artful use of dual-receiving structural acoustic anchors enabled improvements in NLOS identification accuracy. For the 12 fixed coordinates, the results show that after optimizing with the novel method proposed in this paper, the minimum positioning error was 5.31 cm, and the maximum positioning error was only 18.82 cm in this system. The data set that contained four patterns of NLOS situations benefited the localization process substantially. The proposed NLOS acoustic signal identification method also has some limitations. In addition, if users want to more comprehensively and skillfully integrate functions into a system, it is considered better to deal with cross-platform issues in case the system may not be able to meet performance expectations according to sensor coefficients. If necessary, some hardware and peripherals [2; 3; 4; 44] should be introduced. In fact, these mobile devices will also have a negligible impact on the system. Meanwhile, cognitive computing theory and intelligent sensor networks will also improve the efficiency and accelerate the automation or intelligence of these indoor sensor systems. In future works, for more complex and flexible scenarios, more and more machine learning researchers will try to use ensemble learning methods to optimize or create new algorithms or networks, including strong classifiers constructed by the bagging method [44; 45] or new functions or models constructed by the boosting method [46; 47]. Their superior performance will not only be reflected in a classification module, but also in regression analysis issues. This will also become a key motivation for acoustic signal identification research in the next stage. Conceptualization, R.K. and M.W. ; software, R.K. and X.L. ([PERSON]); experiment analysis, R.K. and X.L. ([PERSON]); writing--review and editing, R.K., M.W. and H.Q. ; funding acquisition, M.W. and H.Q. All authors have read and agreed to the published version of the manuscript. This research work is supported by the National Natural Science Foundation of China under Grants 62071135 and 61961010. It is also supported by Innovation Project of GUET Graduate Education No. 2023 YCXB05. Not applicable.