## 6 Conclusions and Future Work  
In large-scale WSNs, it is important to predict situations through various simulations before sensor nodes are distributed to the field, because it uses a lot of resources to change their configuration once installed. Before the deployment of a WSN, some techniques, such as modeling and simulation, should be used to predict the behavior and performance of the network. In addition, it is necessary to reduce unneeded time and costs when evaluating the various performances of simulation models.  
In this paper, we propose the WSN-SES/MB framework to effectively save cost and time during automatic model synthesis through our transformation process while modeling and simulating. Our proposed scheme was able to achieve:  
* Application of the SES/MB to synthesize the structure and models and automate the complicated synthesizing process of WSN modeling construction. Our WSN-SEN/MB framework automatically synthesizes the structure and simulation models through the transformation process;
* Reduced cost and time when synthesizing various sensor networks by proposing a novel transformation process. In the propsoed framework, our transformation process effectively reduces the cost and time of automatic synthesis through the proposed pruning algorithm;
* Efficiency in the WSN-SES/MB framework with our pruning algorithm. The proposed pruning algorithm configures environment parameters of the sensor network, such as the number of sensors, the field size, and the BS location. These parameters are used during the synthesis of the DEVS model;
* Increased diversity of synthesized simulation models. The WSN-SES/MB framework generates simulation models using the entity structures within the parameters and the atomic DEVS models;
* The modeling and simulation of large-scale WSNs. The proposed framework enables the construction of large-scale WSNs through various environment parameters.  
Therefore, the WSN-SES/MB framework achieves cost and time savings using our transformation process as compared to DEVS-C++ technology. In addition, our proposed framework increases the diversity of synthesized simulation models and facilitates the  
Figure 9: Average of Turnaround Time versus number of sensor nodes according to the BS locations.  
modeling and simulation of large-scale WSNs. In future work, we will study synthesis techniques by applying routing and security protocols to WSNs.  
Conceptualization, S.M.N.; supervision, H.J.K.; software, S.M.N.; investigation, formal analysis, H.J.K. and S.M.N.; writing--original draft preparation, S.M.N.; writing--review and editing, H.J.K. and S.M.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2018-0-01799) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03034644).  
Not applicable.  
Now applicable.  
Data Availability Statement: Data of this research is available upon request via corresponding author.  
The authors declare no conflict of interest.