# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 7 Discussion
### Limitations of Experiments and Simulations  
This paper examines the parameters that influence LoRa PHY performance. We note that the hardware also affects the performance and is not discussed here. The received power \(P_{r}\) in wireless communication is expressed as follows:  
\[P_{r}=\frac{P_{t}G_{t}G_{r}M\lambda^{2}}{(4\pi)^{2}d^{2}}, \tag{7}\]  
where \(P_{t}\) represents transmitted power; \(G_{t}\) and \(G_{r}\) are the gains of the transmitting and receiving antennas, respectively; \(\lambda\) denotes wavelength; and \(d\) is the distance between two antennas. Because the maximum EIRP in LoRa is limited to 14 dBm in Korea, the transmission power should be suppressed to maintain the same EIRP if the antenna gain were to increase. As shown in Equation (7), however, high antenna gain can increase the received power at the receiver. \(M\) represents the hardware gain of the receiver influencing the strength of the received power. A hardware product that offers high gain must be selected as long as the cost allows. It is noted that the antenna gain used in the experiments is 3 dBi, for instance. If the testbed uses another antenna with a higher gain, the maximum communication range can increase. For example, using an antenna with a 10 dBi gain can extend the range from 1.34 to 2 km while satisfying the communication reliability requirement of 90%. However, a higher antenna gain comes with an increase in size, resulting in high costs and physical limitations.  
This paper adopts the simplified path loss model in Equation (2). This model has been used for approximating path loss in the outdoor environment and provides general analysis for various systems. Most of the models, including the aforementioned, however, have not been proved to accurately predict the path loss in an indoor environment. In fact, it has been a challenge to develop an accurate path loss model for an indoor environment because the path loss properties inside a building are uncertain. Recent research has studied an IDP model [27; 29] that considers every structural detail of a building to examine the properties. In the campus scenario, which includes a few buildings, it may not be possible to apply the model to all the buildings. Therefore, combining other methods with the model is expected to obtain more accurate results for the combined indoor and outdoor environment.  
The lack of accurate models for an indoor environment is another limitation in our approach, nevertheless. Suppose that several nodes are deployed inside a building in which many people move around. Signal properties become completely random, and signal strength thus fluctuates. Figure 11 shows the variance of received signal strength when conditions are crowded. In this context, we estimated the communication range in the most conservative manner. One interesting observation, then, is that signal performance somewhat depends on the distance between a node and a gateway, and this finding is represented by the simplified model. However, we do not argue that our observations are finalized. A dense deployment of nodes may cause an unexpected drop in PDR performance, and measurements in a large area (e.g., at the city level) have not been examined yet. One finding from the experimental results is that our campus (600 \(\times\) 600 m) needs to deploy two LoRa gateways primarily because of packet collision. This implies that the benefit of long-range communication in LoRa is somewhat limited and suggests that a new network strategy must consider the way by which multiple gateways can be managed.