# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 4 Results and Discussion
### Results Comparison  
To statistically evaluate the performance of the uGA and SS methods, each of the two methods was tried 50 times. Figure 4 shows box plots of the time cycles spent in the 100 trials. The results in Figure 4 can be summarized as follows:  
1. The average spent time cycles of uGA and SS are 1054 and 2041, respectively. 2. The spent time cycles of uGA are generally smaller than those of SS. 3. The distribution of the uGA time cycles is close to the normal distribution, whereas those of SS fall into two separate sectors that are far from each other. The quantile-quantile (Q-Q) plots in Figure 5 show the distance between the distribution of spent time cycles and the normal distribution. Almost all the points in Figure 5a  
Figure 4: Box plots of the time cycles spent by the uGA and SS methods. Each box plot is based on the results of 50 trials. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Experimental Factors**} & \multicolumn{3}{c}{**Spent Cycles 1a**} & \multicolumn{3}{c}{**Localization Errors 1b (m)**} \\ \cline{2-9}  & \(m\) & \(th_{a}\) & \(cTy\) & \(p_{m}\) & **Average** & **std.** & **Average** & **std.** \\ \hline
1 & 10 & 20 & 1 & 0.3 & 1166 & 153.96 & 0.948 & 0.0457 \\
2 & 10 & 40 & 2 & 0.5 & 1322 & 221.89 & 0.920 & 0.0606 \\
3 & 10 & 60 & 3 & 0.8 & 1341 & 385.91 & 0.959 & 0.0455 \\
4 & 20 & 20 & 2 & 0.8 & 1027 & 115.76 & 0.932 & 0.0529 \\
5 & 20 & 40 & 3 & 0.3 & 1355 & 303.79 & 0.935 & 0.0437 \\
6 & 20 & 60 & 1 & 0.5 & 1532 & 127.90 & 0.954 & 0.0587 \\
7 & 30 & 20 & 3 & 0.5 & 1119 & 322.25 & 0.941 & 0.0476 \\
8 & 30 & 40 & 1 & 0.8 & 1418 & 327.57 & 0.949 & 0.0281 \\
9 & 30 & 60 & 2 & 0.3 & 1166 & 153.96 & 0.948 & 0.0457 \\ \hline \hline \end{tabular}  
* Time cycles for the CPSL process from the beginning to termination. 1b The distance between the localization position-result and the real source location.  
\end{table} 
Table 3: Orthogonal experimental results of different \(m\), \(th_{a}\), \(cTy\), and \(p_{m}\) in uGA. lie within the area between the upper and lower percentile lines with a confidence level of 95%, which are close to the centerline \(y=x\). In Figure 5b, there are only a few points between the two percentile lines. To some extent, the quasi-normal distribution of the time cycles spent by the uGA verifies that the localization efficiency of the uGA is stable in the dynamic flow field of the FPSP. Apart from the localization efficiency, the localization error is another factor that should be considered in CPSL applications. Figure 6 shows box plots of the localization errors obtained using uGA and SS. In Figure 6, all the localization errors are smaller than 1.0 m, which means that it is easy to find the real chemical source within the neighborhood of all convergence locations. To summarize, the localization results verify that the termination criteria in uGA can successfully declare the chemical source in the FPSP. The moving paths of the SNs obtained when SS and uGA were employed are also compared. Figure 7 shows the paths generated in two typical simulation trials. In both trials, the MSN obtained its first contact with the plume at the top-right corner. When the SNs lost contact with the chemical plume, the plume re-findin  
Figure 5: Quantile–quantile (Q–Q) plots of the time cycles spent by uGA and SS. The thin gray lines are the upper and lower percentile lines with a confidence level of 95%. (**a**) Normal Q–Q plot of the time cycles spent by uGA; (**b**) normal Q–Q plot of the time cycles spent by SS. Figure 6: Box plots of the localization errors obtained by the uGA and SS methods. Each box plot is based on the results of 50 trials. movement were activated in uGA and SS, respectively. The paths in Figure 7 can be summarized as follows:  
1. The uGA outputs a set of straight-line paths, whereas the SS outputs revolving-shaped paths. 2. The interactions within the MSN in uGA were more effective than those in SS. 3. The paths in Figure 7a are more divergent than those in Figure 7b.