# A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring
## 4 Experimental Validation and Measurement Results  
In the literature, a variety of WSN systems have been widely adopted in fire hazard detection and prediction [17; 18; 19; 20; 21]. Therefore, in this work, in order to validate the functionality of our proposed software framework, we applied our developed WSN system to detect indoor fire hazards in an office building.  
As shown in Figure 4, the experimental setup consists of two wireless sensor modules, an application point module, a host computer, a fire source (i.e., lighter), and three environmental sensors (i.e., a light sensor, a CO\({}_{2}\) sensor, and a humidity sensor). The CO\({}_{2}\) and humidity sensors are connected to wireless sensor module 1, while the light sensor is connected to wireless sensor module  
Figure 3: Illustration of the entire wireless sensor network (WSN) system operation.  
Figure 5 shows a sharp rise in the carbon dioxide level after the ignition of the lighter. We can see that the CO\({}_{2}\) level gradually reached a peak value of 1380 ppm. After extinguishing the lighter, the flame disappeared, so the CO\({}_{2}\) level began to drop slowly with time. In addition, as shown in Figure 6, the output voltage of this light sensor exhibited a valley value during the presence of the flame. An almost immediate voltage drop appeared at around T = 25 s, and a corresponding voltage rise appeared at around T = 55 s. Figure 7 plots the relative humidity (RH) values sensed by the humidity sensor in Figure 4. We can see that the RH level dropped sharply at around T = 26 s, and started to recover at around T = 55 s. The RH level is expected to be higher during the presence of a nearby flame because the byproducts of a combustion event are carbon dioxide and water. However, since the adopted RH sensor does take temperature into account when determining the RH value, the temperature increase caused by the flame adversely affected the sensor output. As a result, the measured RH value became even lower in this experiment.  
These experimental results show that our software framework can successfully fulfill data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. Figure 8 plots the comparison of collected data from the three sensors after normalization, when a nearby fire hazard event occurs. To avoid false positive detections, the integrated microcontroller will make decisions based on the captured three simultaneous pulses in Figure 8. In this study, we also verified that the detection behavior of this WSN system is normal when a new environmental sensor is inserted or an existing environmental sensor is removed from the two wireless sensor modules in Figure 4. The observed robust system operation demonstrates that our high-level software framework supports convenient heterogeneous WSN configurations while the system is running. From the user's  
Figure 8: Comparison of the normalized sensor data from three heterogeneous sensors.  
Figure 7: The recorded humidity level resultant from this fire hazard event.  
point of view, this is a very useful feature because ordinary users without a strong background or WSN knowledge can freely configure or adjust WSNs without worrying about system crashes.