## 7 System Performance and Evaluation
### Outlier Detection and Event Identification  
We used a K-means centroid algorithm (discussed in Section 5.2) to make clusters of the raw data obtained from the underground mine. Figure 8a shows five separately identifiable clusters of all collected data and how they depend on the number of monitoring parameters. These data provide answers to the research questions: Are there any abnormalities? If so, what are the reasons for the abnormalities? Figure 8b shows the clusters of raw temperature data. As shown, cluster 1 is well-defined as the silhouette values are higher, followed by clusters 3 and 2. Higher cluster values show a higher variability in the obtained data. Figure 8c illustrates the three clusters of humidity raw data that show significant variations, especially in cluster 2. Figure 8d depicts the clustering of CH\({}_{4}\) data measured at the mine face and the midway point of the mine with a high consistency for cluster 1. Similarly, Figure 8e is related to the raw data clustering of CO\({}_{2}\) with less variability than cluster 1. In addition, Figure 8f focuses on CO clustering, and both clusters showed a high consistency. The K-means clustering classifies data into a defined number of clusters based on the compactness and separation. The x-axis of these clusters is the cohesion, while the width of the cluster calculated from \(\zeta i\) is represented by the y-axis. Higher values on the x-axis are helpful for determination of the Euclidean distance. As long the as the silhouette value is near 1, the cluster is more compact, and a decreasing value of the Euclidean distance defines a new cluster. Such dissimilarities are helpful for defining new sub-clusters within the same cluster. The values along the negative x-axis represent the dissimilarities in cluster values, i-e, outliers. K-means clustering is also helpful for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{T_{0}}\)... & \(\mathbf{T_{1440}}\) & **Temp.** & **Humidity** & \(\mathbf{CH_{4}}\) & \(\mathbf{CO_{2}}\) & \(\mathbf{CO}\) \\ \hline Average & 36.35567 & 67.57835 & 1224.175 & 651.6824 & 2.536082 \\ Standard Error & 0.124595 & 0.24012 & 28.06941 & 12.36075 & 0.1185 \\ Median & 36.3 & 68.5 & 1185 & 656 & 3 \\ Mode & 35.9 & 69.7 & 985 & 460 & 3 \\ Standard Deviation & 1.227 & 2.364917 & 276.4516 & 121.73932 & 1.16709 \\ Skewness & 0.221967 & \(-\)0.50707 & 0.317597 & 0.07118 & \(-\)0.128 \\ Variance & 1.5058 & 5.6510 & 76176 & 351.6563 & 1.3621 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative results of parameters at the mine face shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{T_{0}}\)... & \(\mathbf{T_{1440}}\) & **Temp.** & **Humidity** & \(\mathbf{CH_{4}}\) & \(\mathbf{CO_{2}}\) & \(\mathbf{CO}\) \\ \hline Average & 32.06907 & 61.26804 & 465.3711 & 285.7732 & 2.5154 \\ Standard Error & 0.258292 & 0.5165 & 6.68 & 1.9040 & 0.119 \\ Median & 31.2 & 62 & 450 & 284 & 3 \\ Mode & 35.2 & 64 & 450 & 300 & 3 \\ Standard Deviation & 2.54388 & 5.087 & 65.8494 & 18.75 & 1.17736 \\ Skewness & 0.1108 & \(-\)0.37651 & \(-\)2.3288 & 0.366078 & \(-\)0.11685 \\ Variance & 6.4713 & 25.8857 & 4336.152 & 351.6563 & 0.5262 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Quantitative results of parameter monitoring at the midway point of the mine. predicting the air quality of the underground mine. To ensure accuracy, clusters obtained during experimentation were compared with four sets of clusters already gathered during training sessions. Time, percentage accuracy, and mean values are the basic elements of clustering. Under the different scenarios, the system was tested for outlier's detection and the efficiency was found to be more than 90% for all cases. Figure 8: K-means clustering analysis results of sensor nodes installed inside a mine during two months from December 2016 to February 2017.