## 3 Results and Discussions
The Preprocessing of Null Records and Identification of Abnormal Observations Reported in the WSN System  
The original record solely consisted of 419 entries for each node despite of the fact that it should include 423 entries with a 10-min delta, therefore, it was compulsory to augment another four null entries. Meanwhile, there were also a small portion of null entries that were formed by removing the corresponding abnormal records. In this study, a combination of methods that are capable of handling null entries was adopted to determine and update the numeric values of those null entries. A second order polynomial interpolation was initially applied to cover 1/3 of the total filling of null entries. Then a forward filling approach was employed to address the remaining 66% null entries, because it is a common practice to deal with the gap-filling problem by using the previous observations in the time series. Finally, means in the relevant columns had been applied to fill the remaining null entries that were less than 1% (42 elements). Up to this point, all the records were complete and ready for further preprocessing steps. After the completeness of gap-filling procedure, it is both necessary and meaningful to examine and evaluate the quality of data reported by four correlated sensors that were installed in six columns. Figure 9 depicted the distribution overview of each slave node in every column by its corresponding boxplot of ammonia concentration. It is obvious that the vast majority of boxplots were comparatively short which suggested that ammonia concentration of relevant nodes had a high level of agreement with each other apart from those nodes mounted on Column B and Column C. Besides, the overall number of outliers (in diamond) for nodes mounted on Column C was also the least among all the six columns, which indicated that most of nodes exhibited a stable performance except for node 32. Outlier detection is among the most vital aspects to judge the performances of multi-sensor modules integrated in the slave nodes. The Turkey's fences [30] which use the lower and upper quartiles to define the corresponding limits were applied to indicate outliers in each node during the entire time span. In this study, four types of environmental sensors which had been integrated into the multisensor module of slave nodes were considered, namely: sensors of temperature, humidity, carbon dioxide, and ammonia. Utilizing this  
Figure 8: The individual transmission performance of data packets from slave nodes mounted on six columns in the WSN system. robust measure of scale, the outlier statistics that were distributed across six columns were compared in terms of four sensors on Figure 10.  
From Figure 10 it is clear that the temperature subpart of the multisensor module for all the indoor slave nodes exhibited extremely stable performance with no outlier detected. Additionally, the humidity measurement submodule achieved relatively high performance with its outlier rate fluctuating around 2%. The rationale behind that is the integrated AM2301 module consists of both a capacitive humidity sensor and a temperature sensor which are connected to a high-performance 8-bit microcontroller to ensure both excellent reliability and strong anti-interference ability. On the contrary, the readings of carbon dioxide sensors showed significant drifts and its outlier rate occupied an average 6.5% of all observations for each column. Considering the fact that pig respiration is one main source of CO2 emission, it is reasonable for nodes mounted on Column A, Column C, and Column E that represent the respiration zones of 60 cm height above ground (close to pig heads) to preserve considerable records of high concentration of CO2.