## 4 Evaluation and Experiments
### Experiments
#### 4.2.3 Process and Analysis of Comparative Experiments
In the next experiment, two monitoring nodes were adopted. Each node had the same hardware as that mentioned in Section 4.2.1. Each monitoring node was connected with four sensors (water temperature, pH, DO, and conductivity) as mentioned in Section 4.2.2. All nodes adopted the same approach proposed in this paper and the same as that of Node 3 in Table 3. However, the monitored water samples are different. To verify the energy saving ability with the changing of water quality, five groups of water sample were used, which corresponded to the five grades of water quality as shown in Table 2. Each group was placed in a separate container. The sensor probes of Node 1 were fixed in the container, which contained the worse quality of water group 5. The sensor probes of Node 2 switching location from one container to the others lasted for two hours each. The switching sequence is as follows: Water group 5--Water group 4--Water group 2--Water group 1--Water group 3. Table 2 shows that except the water temperature, all the parameters are different in the five water groups, especially the DO and conductivity. Therefore, in our simulation, Node 1 was working in a steady water environment, meanwhile Node 2 was working in a water environment with changing \begin{table}
\begin{tabular}{c c c c} \hline
**Node Name** & **Control Rules** & **Description** & **Hardware** \\ \hline Node 1 & Works continually without any control. & Has a constant sampling interval of 30 s and communicating interval of 5 min. \\ \hline Node 2 & Makes the communication device sleep & Has a constant sampling interval and communicating interval same as those of Node 1. However, the communicating device enters a dormancy state for 0.5 h after working continually for 2 h. & All nodes have hardware similar to those mentioned. \\ \hline Node 3 & Uses the approach proposed in this paper. & & \\  & & & \\  & & & \\ \end{tabular}
\end{table} 
Table 3: Comparison of the three nodes. water parameters. However, the water parameters changed in our simulation. Every battery was fully recharged again, and the monitoring nodes began performing data collection and communication. The dump energy of the three battery groups was tested. Figure 20 shows the test result of the average of three experiments. Node 1 had a longer duration than Node 2 for the same energy consumption. This result means that the ontology middleware reconfigured the WSN at different grades of water sample and effectively reduced the energy consumption. The ontology middleware played a role in saving energy, which decided to reconfigure the monitoring node according to the data rule. Finally, two monitoring nodes that had the same hardware as mentioned in Section 4.2.1 were adopted. Each monitoring node was still connected with the four sensors (water temperature, pH, DO, and conductivity) as mentioned in Section 4.2.2. All the sensor probes of the two monitoring nodes were fixed in the container of Water group 5. The same approach proposed in this paper as for Node 3 in Table 3 was adopted for two monitoring nodes. We set the observed objects of water temperature  
Figure 19: Test results of durations of three nodes for the same battery consumption with different control method. Figure 20: Test results of durations of two nodes for the same battery consumption with different monitored water samples. and pH to unobserved in the management interface with Node 2. Node 1 still monitored four items (water temperature, pH, DO, and conductivity). Every battery was fully recharged again, and the monitoring nodes began performing data collection and communication. The dump energy of the three battery groups was tested. Figure 21 shows the test result of the average of the three experiments. Node 2 had a longer duration than Node 1 for the same energy consumption. This result means that the ontology middleware that makes the water temperature sensor sleep and the pH sensor of Node 2 have an effect on energy consumption. The observation demand state rules play a role in decision making.