## 5 Conclusions  
To conclude, it should be noted that this work solved one of the DAS software optimization problems. This was done by studying the operation of noise reduction algorithms that do not impose the significant requirements for the computing devices performance in various scenarios of impact on the sensor. The algorithms or their combinations demonstrating the highest efficiency were identified for each type of event, modifications were proposed to increase their performance. A summary of the comparison results is given in Table 1.  
Figure 9: 3 kHz event spectrum. (**a**) Initial; (**b**) processed with initial FDDA algorithm; (**c**) Processed by modified FDDA algorithm. The first column in Table 1 corresponds to the frequency at which the fiber sensor was affected. This information is provided only to associate the results presented in the table with the figures above. As shown by numerous experiments, the efficiency of the filtering algorithms does not depend on frequency within the linear part of the sensor frequency response. Our studies have also shown that it is expedient to use the FDDA method after using the other considered algorithms. From Figure 4, for example, it can be seen that the first peak in the spectrum after processing turned out to have a significant spreading at its base. Thus, the limitation of this method is the lack of capacity to detect several neighboring frequencies. Another flaw, already mentioned above, is the disappearance of low-intensity oscillations from the useful signal under some conditions. In other cases, the FDDA method gives a noticeable increase in the signal-to-noise ratio, both when used alone or in combination with other algorithms. We believe that the search for the most efficient data processing algorithms, along with the use of cost-consuming narrow-band laser solutions reported recently, can meet the demand for the use of DAS in an ever-expanding range of fields of science and technology. The results obtained in this work can be applied to a wide range of problems. First, the presence of an inexpensive laser in the monitoring system will make it available not only for geological exploration, monitoring of pipelines, and mines, but also for new applications such as urban traffic monitoring and security systems [70; 71; 72; 73]. New areas will open up where DAS technology can be applied. In addition to studies already carried out using a distributed acoustic sensor on the behavior of the red weevil in palm trees [74], acoustic observations of cicadas and bees seem to be the most obvious future work [75; 76]. Perhaps this will provide key data to address issues related to declining bee populations around the world. Secondly, the use of the proposed simple filters in combination or separately allows obtaining data with increased SNR when studying new materials and mechanical designs. A huge number of works are devoted to non-destructive testing, which indicates the importance of this area [77; 78; 79; 80; 81]. In a number of engineering tasks, it is required to obtain data on all spectral components: on the fundamental tone and additional harmonics [82]. In such cases, the MA and MD filters will give the desired SNR gain and allow the full spectrum to be saved for analysis. In tasks where it is required to obtain information about the fundamental tone of the acoustic vibration and to suppress the remaining harmonics along with noise, it is advisable to apply the FDDA filter after using the MA and MD algorithms. Such challenges may arise during the operation of vortex flowmeters or during the use of acoustic sensors for diagnosing new devices and products [83; 84; 85]. Another important and dynamically developing area, where the technology of distributed fiber-optic sensors can be applied in the very near future, is a smart home, smart city, smart production, and the Internet of Things [86; 87; 88]. With the spread of such systems, the issues of energy saving and increasing the lifetime of individual elements become more and more relevant.