## 4 Results and Discussions  
### Coverage Analysis and Path Loss Limits of LoRaWAN(r) Wireless IoT Communication  
The purpose of this section is to identify the capability of LoRaWAN(r) technology to provide aerial wireless coverage, analyze the maximum achievable coverage when operating on-air via a drone, and identify the most suitable path loss (PL) model for the study area. Accordingly, a set of received signal strength indication (RSSI) measurements were taken at the National University of Malaysia (UKM), Bangi campus. The measurement setup consists of two devices; one GW mounted on a tripod at 2 m above ground level (AGL), near the Faculty of Engineering and Built Environment (FKAB), and an EN mounted on a drone. During the measurements, the drone took off vertically and maintained a height of 200 m AGL. The drone then moved horizontally through the measurement path shown in Figure 10 and transmitted the data packets every 200 m. The average RSSI value for the received packets at each of the 56 points was then recorded at the GW side. The GW and EN operated at the 915 MHz band and used SF12 at 125 kHz BW with a fixed transmission power of 20 dBm. Meanwhile, the antenna was an omnidirectional antenna of 2 dBi and 0 dBi gain for the GW and EN, respectively. As shown in Figure 10, the communication distance achieved was more than 10 km using the default setting. It was also noted that the signal strength was significantly below the LoRa receiver sensitivity of \(-137\) dBm. The worst recorded signal was \(-110\) dBm, which translates into coupling loss of 130 dB, calculated as \(\text{P}_{\text{Tx}}-\text{min}(\text{measured RSSI})\). The latter is below the theoretical maximum coupling loss (MCL) of 157 dB [69] of LoRa-based systems. As such, higher coverage is expected using the utilized configurations. Hence, it can be observed that the LoRaWAN(r)-based aerial wireless communication system represents a suitable and reliable communication option for the intended application. To further illustrate losses in the propagated signal due to factors such as shadowing, PL was calculated and compared with other reference empirical PL models. These models included a free space PL (FSPL) model as a baseline and two popular PL models based on Cloud-RF(r). The latter models include the Longley-Rice irregular terrain model (ITM) and  
Figure 10: Measurement route at the UKM campus area (Bangi, Malaysia) and the heatmap of LoRaWAN® coverage throughout the measurement route. ECC-33. PL was first calculated from the measured received power (\(P_{Rx}\)), equivalent to the measured RSSI in dBm, using Equation (8) [70]:  
\[PL(dB)=P_{Tx}-P_{Rx}+G_{Tx}+G_{Rx}-L, \tag{8}\]  
where, \(P_{Tx}\), \(G_{Tx}\), \(G_{Rx}\), and \(L\) are the transmitted power in dBm, transmitter antenna gain, receiver antenna gain, and other losses on both the transmitter and receiver's side, respectively. \(L\) is neglected in the calculation and assumed to be zero. FSPL was then calculated using Equation (9) [70]:  
\[PL_{FSPL}=20 log_{10}(f)+20 log_{10}(d)+32.44, \tag{9}\]  
where \(f\) is the frequency in MHz and \(d\) is the separation distance between the transmitter and the receiver in km. Finally, for Cloud-RF(r), simulations were performed using the same measurement parameters used during the measurements performed at the campus (see Figure 11). The tool works by assigning the area type-dependent parameters based on the area maps, considering the type of signal path area. It also considers the knife-edge diffraction impact and the irregular terrain impact on the transmitted signal (for the ITM) in the calculation process for more accurate PL predictions. Once simulations were complete, the PL data were extracted for each measurement point using the best server analysis feature of Cloud-RF(r). Figure 12a shows the actual measured PL compared against the extracted PL measurements for the two evaluated PL models based on the Cloud-RF(r) tool simulations in addition to the calculated FSPL as a baseline.