## 3 Results and Discussions
### Growing Environment Measurement by the WSN
#### 3.1.1 Growing Environment Data Acquired by the WSN
In addition, this sensor is worthy of the IP68 code that means it is completely resistant to both water and dust. Thus, we will be able to use this sensor in the ground for a long period. As the weather station, a SE-WS700 system was selected. This weather station can acquire the data of air temperature, relative humidity, air pressure, wind speed, wind direction, 1 min_precipitation and solar irradiance at the same time, which enables a cheaper WSN system than in the previous work [3]. In addition, this weather station can measure each environmental data point every one minute. The important point is the accuracy of each measurement acquired by this weather station, especially the wind speed measured by the ultrasonic anemometer and precipitation measured by the Doppler radar rain gauge. Furthermore, its water sampler requires minimal maintenance [17]. Thus, this weather station has the potential to solve the problem raised in the previous work. In order to provide information services, especially when we integrate various data sources, interoperability of the data and system is important. Therefore, we used the Sensor Observation Service (SOS) to standardize the terminology and Units of Measure (UOM) (Table 3). SOS is a web service to query real-time sensor data and sensor data time series ([[http://www.opengeospatial.org/standards/sos](http://www.opengeospatial.org/standards/sos)]([http://www.opengeospatial.org/standards/sos](http://www.opengeospatial.org/standards/sos))). This service applies XML encoding for observation & measurement (O & M) data originating from the sensors. It defines a web service interface that allows querying observations, sensor metadata, as well as representations of observed features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Name** & **Model Number** & \multicolumn{2}{c}{**Note**} \\ \hline \multirow{3}{*}{Weather station} & & \(\bullet\) & Adoption of ultrasonic anemometer. \\  & WS700-UMB & \(\bullet\) & Adoption of doppler radar rain gauge. \\  & & \(\bullet\) & Free from maintenance. \\ \hline \multirow{3}{*}{Soil moisture sensor} & & \(\bullet\) & Being able to measure EC (0–1 V), VWC (0–1 V) \\  & WD-3-WET-SE & & and temperature (0–1.2 V) of soil. \\  & & \(\bullet\) & Worthy of IP68 code. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensor information used in this study. Figure 9: The structure of the WSN used in this study. Following the OGC standard, defines the means to register new sensors, to remove existing ones and to insert new sensor observations. The cloud sensor infrastructure called \"cloudSense\" has a sensor backend service based on the SOS, thus making it possible for us to add and modify sensors and automatically reflect the change in the sensor configuration without program modification [18; 19]. In order to send these environmental data from the data link server to the cloudSense, the data link server has HTTP-GET API defined by SOS. The terminology, physical quantity and UOM of each observed data point are standardized and stored in the database in cloudSense (Figure 10). Figure 10 shows the data flow of the IoT service in this study. The obtained data goes to the local server for WSN. The dataset then goes to the cloudSence for standardization. The standardized data are back to the Database of the local server. The primary information and the secondary information service were then delivered to the farmers at the field. The standardized data stored in cloudSense are also acquired using HTTP-GET API defined by SOS. In the HTTP-GET API, there are several kinds of requesting methods. First, \"getCapability\" is the method that can confirm the information about each observed data. Secondly, \"getObservation\" is the method that can acquire the monitored value of the selected observed property.