## 6 Data Analysis Scenario  
In this section, we present a real-time pollution data analysis scenario to evaluate the data analysis capability of MoDisNet system. This evaluation is based on the air pollution data that was generated from our former project Discovery Net which constructed a sensor grid over a typical urban area as shown in the map of Figure 10 around the Tower Hamlets and Bromley areas in east London. There are some of the typical landmarks such as the main road extending from A6 to L10, the hospitals around C5 and K4, the schools at B7, C8, D6, F10, G2, H8, K8 and L3, the train stations at D7 and L5 and Gas Works between D2 and E1. 140 sensors are distributed in this area and collect data from 8:00 to 18:00 at 1-minute intervals to monitor the pollutants of NO, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\). The air pollution analysis uses the air pollution data to give an overall understanding of the air pollution characterization within this area by running the data mining algorithm. As the Discovery Net can only classify the pollution data into several pollution levels, such as high or low, but can't tell us \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline IENS & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline _APMM_ & 84.5\% & 90.29\% & 92.48\% & 93.06\% & 93.1\% & 93.32\% & 93.43\% & 93.66\% & 94.05\% & 94.4\% \\ \hline \end{tabular}
\end{table} 
Table 1: Centralized Clustering vs. Distributed Clustering (_APMM_ results). Figure 10: MoDisNet sensors evaluation case in an area of east London. the distribution of different pollutants in different locations and their contributions to the pollution levels. To improve the data analysis capability, in this data analysis scenario, we use the distributed clustering algorithm to cluster the pollutants into pollution clouds which can recognize different pollution patterns. From the experimental results of Discovery Net, we pickup all the high pollution level locations at 9:00, 15:30 and 17:00 respectively to check the contribution of different pollutants (NO, NO\({}_{2}\), SO\({}_{2}\) and O\({}_{3}\)) to the pollution levels. The results are shown in Figure 11.  
From the figures we can see that in the morning at 9:00, the high pollution locations are distributed around the main roads and the schools (highlighted by the circles). At 15:30, the high pollution locations are around the schools and the factory (which is a gas work and highlighted by the square). At 17:00, more pollution focuses on the main roads; the hospitals (highlighted by the ellipses) and the factory make the contribution as well. Checking the mining data set, we found the relationship between the pollution clouds, the different pollutants and pollutants' volumes, which is shown in Table 2.  
This table shows that, in each time snap, there are 3 different pollution clouds: Red, Blue and Yellow. Each cloud represents a kind of combination of the 4 pollutants. For example, in the morning \begin{table}
\begin{tabular}{|l|l|l|l|l|l|} \hline \multirow{2}{*}{Time} & \multirow{2}{*}{Clouds} & \multicolumn{4}{c|}{Analysis} \\ \cline{3-5}  & & NO & NO\({}_{2}\) & O\({}_{3}\) & SO\({}_{2}\) \\ \hline \multirow{4}{*}{9:00} & Red & High & Middle & Low & High \\ \cline{2-5}  & Blue & High & Low & Low & Low \\ \cline{2-5}  & Yellow & Low & Middle & Low & Middle \\ \hline \multirow{4}{*}{15:30} & Red & Low & Middle & Middle & Middle \\ \cline{2-5}  & Blue & Low & High & High & Middle \\ \cline{2-5}  & Yellow & Low & Low & Low & High \\ \hline \multirow{4}{*}{17:00} & Red & Middle & High & High & Middle \\ \cline{2-5}  & Blue & Low & Low & Middle & Middle \\ \cline{1-1} \cline{2-5}  & Yellow & Low & Middle & Middle & High \\ \hline \end{tabular}
\end{table} 
Table 2: Pollution pattern analysis. Figure 11: Pattern recognition for high air pollution level areas. at 9 am, the Red cloud that covers the main roads and the area around the main roads is characterized by high volumes of NO and SO\({}_{2}\). Most of these pollutants are emitted by the vehicles running in the morning rush hours.