## 5 Statistical Analysis of Quality of IoT Service during the Year  
In this section a statistical analysis of the quality of the IoT OTH maritime surveillance service over a good weather (dry season which has its peak from December to February) and bad weather period (from May to July, peak of rain season) will be presented. Note that the major impact on the IoT OTH maritime surveillance service is the satellite communication that represents the communication backbone of the overall maritime surveillance sensor network depicted in Figure 4. In Section 3 we have explained in detail how detection files are generated and distribution of detection file sizes is presented (Figure 6). In Figures 15 and 16, we have shown the cumulative distribution function (CDF) of the time frame that is measured from the data collection start at the HFSWR location to the end of the detection file transfer to central location (referred as total detection file transmission time in the remainder of this section). Given the distribution of the detection file sizes shown in Figure 6, we have shown the cumulative distribution of total detection file transmission time for the two most common file size ranges. Figure 14: Transfer rate during bad weather conditions. Figure 15 shows the cumulative distribution for the 15-25 kB range, while Figure 16 shows the cumulative distribution for the 25-35 kB range. Figures 15 and 16 show the three rainiest months (May-July) and the three driest months (December-February), with the idea to observe whether there are some differences in the total detection file transmission times. The CDF of the rainiest months reaches the value 1 later than the CDF of the driest months. This means that, on average, it takes more time to transfer files to the central location when the weather conditions have more negative impact on the satellite link. However, given that the overall system is robust and can tolerate differences up to several seconds, the given differences in total detection file transmission times between the worst and the best weather condition months are acceptable. Figures 17 and 18 show the comparison of total detection file transmission time CDF for the two most common file size ranges--15-25 kB and 25-35 kB for the driest and rainiest  
Figure 16: CDF for detection file size range 25–35 kB. Figure 15: CDF for detection file size range 15–25 kB. months, respectively. As expected, the file size does have an impact on the total detection file transmission time as the CDF for range 25-35 kB reaches value 1 later than the CDF for range 15-25 kB. However, the time differences are not significant and do not affect the overall IoT OTH maritime surveillance service performance. In order to properly estimate the service performance parameters, it is needed to analyze service behavior on a monthly basis and to derive a mathematical model of that behavior. It is important to emphasize that neither of the derived distributions describes empirical data perfectly. However, similarity of the derived distributions and empirical data is very high so it might be said that the derived distributions are properly describing the empirical data. Since those analyses may prove to be too lengthy, only the best and the  
Figure 17: CDF of 15–25 kB and 25–35 kB for Dec–Feb. Figure 18: CDF of 15–25 kB and 25–35 kB for May–July. worst matching will be presented and discussed further. The conclusions are valid for the whole observed time frame. Firstly, the best matching case (June 2018) is presented (matching with 20 different probability density functions (PDF) is tested). After analysis in Matlab software package [33] is done, it has been found that the best mathematical description for the data collected during June 2018 can be achieved with PDF called \"generalized extreme value\" [34].