## 4 Experiments and Discussion
### Realization and Experimental Results of LoRa Repeat Planning for Solving No Signal  
To transmit sensing data in the large areas without a 3G/4G telecommunication signal, as shown in Figure 8, we first assembled a LoRa-support solar micro-weather station with LoRa communication, an illuminance sensor, an atmospheric pressure sensor, an ultraviolet light sensor, a carbon dioxide sensor, etc. The antenna was 868 megaHz. For convenience, the RSSI values were measured by the tool WifiInfoView. Then, we used the RSSI to measure the signal strength and set up a gateway on the beam of the pavilion at the entrance of the Fushan Botanical Garden, which has both electricity and 3G/4G internet signals. The gateway shared the electricity of the pavilion lights and monitors. In order to  
Figure 6: NB-IoT-support temperature and humidity sensors. avoid an unstable power supply in mountainous areas, the gateway was supplemented by 7A batteries that can be charged at any time to achieve uninterrupted power. Next, we set up a LoRa-support solar micro-weather station near the top of the mountain where we needed to conduct a resource investigation. Thus, the LoRa-support solar micro-weather station could continuously send out sensing data by using LoRa communication technology. However, as shown in Figure 9, at a distance of between 775 m and 1645 m from the LoRa-support solar micro-weather station, that is, from 810 m above sea level to 680 m, the signal was lost for an RSSI of \(-120\) dB and was unstable from \(-110\) dB to 120 dB. Therefore, as shown in Figure 10, we initially set up a LoRa repeat station between the LoRa-support solar micro-weather station and the gateway to solve the lost-signal problem so as to successfully repeat the real-time sensing data, including data on the illuminance, atmospheric pressure, ultraviolet light, and carbon dioxide. As shown in Table 3, we  
Figure 8: LoRa-support solar micro-weather station. Figure 7: Real-time data on temperature and humidity. Moreover, the green light means the battery has enough power. evaluated the correspondence between the distance and the signal strength using three times the average of the RSSI from the LoRa-support solar micro-weather station on the mountain slope without rain. In order to reduce the location errors of the RSSI from \(-110\) dB to \(120\) dB caused by the interference, we excluded the RSSIs below \(-110\) dB, and Equation (3) became: \(I(d)=\left\{ \begin{array}{ll}1,&\text{if }d\geq 500\\ 0,&\text{otherwise}\end{array} \right.\) where \(\varepsilon=500\) m or \(\varepsilon=0.5\) km. Then, the sensing data were transmitted to the LoRa gateway, which was connected to the 3G/4G telecommunication network with an average of \(10\) s. Finally, these real-time sensing (the sensor senses immediately) data were transmitted to a web server within seconds. Figure 9: RSSI of LoRa signal between the LoRa-support solar micro-weather station and the gateway. (**a**) The RSSI variety from the LoRa-support solar micro-weather station to the gateway. (**b**) The RSSI from the LoRa-support solar micro-weather station to the gateway. Furthermore, as shown in Table 4, when it rains heavily, the signal strength was attenuated by about 20% so that Equation (3) became: \(I(d)=\left\{ \begin{array}{ll}1,&\text{if }d\geq 400\\ 0,&\text{otherwise}\end{array} \right.\) where \(\varepsilon=400\) m or \(\varepsilon=0.4\) km. Different levels of signal strength can reduce the location errors of the RSSI caused by the interference of heavy rain. Accordingly, we set up a signal repeat station every 0.4 km between the LoRa-support solar micro-weather station and the gateway on the mountain slope.