## 1 Introduction  
Wireless sensor networks (WSNs) have been widely employed in area monitoring, military applications, and fire detection applications [1, 2, 3, 4, 5, 6, 7]. The sensor networks record the conditions of environments to monitor the applications and organize the collected data at a central location. Figure 1 shows a WSN example, comprised of numerous sensor nodes for recording the conditions and a single base station (BS) for collecting the data. When a sensor node detects a real event (e.g., the appearance of a tank), the node generates an event report. It forwards the report via multiple hops toward the base station. Although the sensor network automatically operates the collection and analysis of the data without human intervention, the network has profound impact on its lifetime, since it is affected by tiny configurations (e.g., the field size, the BS location etc.) after deployment of the sensors [8, 9, 10]. In a sensor network, the configuration of the network environment is very important because it is exceedingly difficult to change [2, 11]. In addition, it consumes a lot of resources to add or modify the network configuration (a sensor node addition, its routing path modification, etc.) one installed [8, 9, 10, 12]. The lifetime of the network is heavily determined by the selection with respect to energy consumption, node deployment, scalability, connectivity, coverage, and security [5]. Therefore, before deploying sensors in the field, we should utilize some techniques, such as modeling and simulation [13, 14, 15], to predict the behavior and performance of a network with the consideration of the network environment [8, 16, 17]. Researchers have proposed several simulators to predict the behavior and performance of WSNs [16; 17; 18; 19]. Even though these simulators accurately model sensors and simulate the models under various operational scenarios, they may place some restrictions on the model construction for the sensor networks. It is necessary to include the synthesis technology based on a process without any extra human effort (i.e., hardcoding). Thus, the WSN simulators should be able to freely manipulate the network configuration, to model and simulate the behavior of the sensors, and to evaluate the factors of the nodes. To this end, the system entity structure and model base (SES/MB) framework [13; 15; 20; 21; 22; 23; 24] synthesizes entity structures and behavioral characteristics to generate diverse simulation models based on a transformation process. The SES systematically expresses the structures of complex systems and the MB represents behavioral models of the systems using Discrete Event System Specification (DEVS) formalism [13; 14; 25; 26]. Although the SES/MB uses the structural expression and the atomic DEVS models to build the executable simulation models, it is difficult to synthesize the structure and the model including the configuration parameters with little time and cost. For example, to configure or modify a BS's location, an atomic model of the BS should be manually hardcoded with the location parameters (X and Y coordinates). For this reason, there are limits on synthesizing the entity structures and behavioral models, including the environment parameters. It is necessary to achieve a new SES/MB framework to effectively synthesize them for WSNs. In this paper, we propose the SES/MB for WSNs (hereafter called the WSN-SES/MB) framework to automatically synthesizes the structure and models through our modeling construction process. The WSN-SES/MB uses the complicated synthesizing process of the WSN modeling construction (i.e., transformation process) to achieve time and cost savings. In our proposed framework, the transformation processes the entity structures and DEVS behavioral models of a sensor network with the parameters of the network environment.