## 5 Simulation Results  
We performed a simulation experiment to evaluate the WSN-SES/MB and compare it to the DEVS-C++ simulator. This existing simulator should additionally work the implementation of coupled DEVS models and the configuration of the environment parameters to manually synthesize the models and the parameters. The sensor field was proportionally determined according to the number of sensors in consideration of the density of nodes as shown in Table 2. The sensors forwarded reports  
Figure 5: Executable Simulation Model of a WSN. to the BS via multiple hops [9; 52]. The transmission range of the sensor nodes is 100 m [9]. The location of the BS is freely determined according to parameters. We set the behavioral time of each state for atomic DEVS models based on [9]. In the simulation experiment, we randomly generated 100 events. There was no packet loss in the experiment. The observation time of the simulation is 200, where an event is generated at every time of 2. Figure 6 illustrates the execution time of our WSN-SES/MB framework and the DEVS-C++ according to the number of sensor models. The WSN-SES/MB synthesizes the entity structure and the atomic DEVS models; the DEVS-C++ synthesizes atomic and coupled DEVS models. Although our framework consumes an almost constant execution time synthesizing between 50 and 200 models, it improves the time by up to 19% when synthesizing between 500 and 1000 models. On the other hand, the DEVS-C++ increases the time proportionally between 50 and 200 models, and it increases the time rapidly between 500 and 1000 models. In addition, apart from the execution time of the DEVS-C++, additional hardcoding and the code build times are also consumed. In modeling and simulation, to obtain the optimization model, these technologies should alter model parameter values and restart simulations more than dozens of times [53]. Therefore, the proposed WSN-SES/MB framework uses our transformation process to save time in running a synthesis of DEVS models. Figure 7 shows the average ratio of CPU utilization measured according to the number of sensor models while synthesizing the DEVS models. The WSN-SES/MB utilized an average of 7.6% of the CPU, and the DEVS-C++ worked an average of 11.6% of the CPU resources. Overall, our proposed framework saved about 4% of the CPU resources as compared to the DEVS-C++. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Sensors** & **50** & **100** & **150** & **200** & **500** & **1000** \\ \hline Sensor of Field (m\({}^{2}\)) & 200 \(\times\) 200 & 300 \(\times\) 300 & 400 \(\times\) 400 & 500 \(\times\) 500 & 1000 \(\times\) 1000 & 2000 \(\times\) 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment of the field according to the number of sensors. Figure 6: Synthesis execution time versus the number of sensor models. Figure 8 illustrates the average memory utilization measured according to the number of the sensors while synthesizing the models. The WSN-SES/MB used an average of 36.04 megabytes of the random-access memory (RAM), the DEVS-C++ operated average of 37.32 megabytes of the RAM. Overall, our framework minutely saved the memory use when compared to the DEVS-C++. Thus, the proposed framework saved both execution time and CPU resource use while maintaining the memory use as compared to the DEVS-C++ when the models were synthesized. Figure 9 shows the average turnaround measurement time according to the number of sensors and the BS locations (i.e., the top center, the middle, the bottom center). Overall, it was confirmed that the simulation models operated normally in various environments. As shown in this figure, the simulation models with the BS in the middle spend a shorter average turnaround time than other models. This was because the intermediate SENSOR models forwarded reports to the BS within a short number of hops. Thus, our framework worked normally, despite the different number of sensor nodes, field sizes, and BS locations based on the proposed transformation process.