# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 6 Performance Evaluation
### Simulation Results  
#### 6.2.1 Algorithm Evaluation  
We first evaluate EE-LoRa for SF selection and power control with \(\mathcal{T}_{1}\) TP levels. We assume that all nodes are static. The number of nodes varies between 1000 and 4000. We compare the obtained results to legacy LoRaWAN and the appropriate algorithms listed below: \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of nodes \(N_{t}\) & [1000-4000] \\ Number of gateways \(r\) & 4 \\ Network Layout & Square, 7 km side \\ Path loss model & Okumura–Hata urban and dense urban \\ Spreading Factor SF & \(s\in\Omega=\{7,8,9,10,11,12\}\) \\ Transmission Power IP level & \(l\) [2 dBm, 14 dBm] \\ Carrier Frequency & 868 MHz \\ Bandwidth & 125 kHz \\ Coding Rate & 4/5 \\ ED/GW antenna & 3 dBi omnidirectional \\ Gateway height & 30 m \\ Node height & 1.5 m \\ Packet generation rate \(\lambda\) & 6 packets/h \\ Payload size \(k\) & 40 bytes \\ Preamble length \(n_{prcam}\) & 8 bits \\ \(T\) & 720 s \\ \(R\_D1\) & 1 s \\ \(R\_D2\) & 2 s \\ \(V\) & 3.3 V \\ \(I_{rx}\) & 10.5 mA \\ \(I_{lat}\) & 1.4 mA \\ \(I_{id}\) & 1.5 \(\mu\)A \\ Node battery capacity C & 1800 mA.h \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Simulation parameters. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \(\mathbf{s}\) & \(\mathbf{7}\) & \(\mathbf{8}\) & \(\mathbf{9}\) & \(\mathbf{10}\) & \(\mathbf{11}\) & \(\mathbf{12}\) \\ \hline \(D(s)\) & 3773 & 126 & 62 & 28 & 8 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Example of node distribution at different SFs when \(N_{t}=4000\) nodes. Legacy LoRaWAN: Each node is assigned the lowest SF that allows it to reach the nearest gateway using the highest TP (14 dBm), according to Table 4. To make a fair comparison, the TP for each node is then reduced to the lowest value, which permits it to reach a gateway with the selected SF. * OPT-MAX [16]: Minimizes the highest probability of collisions in any given SF. Then, the TP for each node is derived using the integer linear programming algorithm OPT-TP [16], which aims to minimize the network energy consumption. * OPT-DELTA [16]: Derives the SF for all nodes in a way to balance the probability of collisions in all SFs. After that, the optimal TPs for nodes are obtained using OPT-TP. Figure 8 illustrates the node distribution over different SFs obtained using EE-LoRa compared to legacy LoRaWAN when the number of nodes is 4000. The percentage of nodes at SF 7 exceeds 94% when using legacy LoRaWAN. Meanwhile, the distribution of nodes over other SFs is below 6%. The highest percentage of nodes at SF 7 can be explained by the best coverage and highest received SNR in multi-gateway deployment. However, such a distribution dramatically increases the number of collisions and reduces the network throughput since LoRaWAN is based on the Aloha protocol. On the other hand, it can be seen that our proposed algorithm redistributes the nodes over different SFs, which reduces the collision probability and improves the network throughput. Figure 9 represents the obtained total throughput of the LoRaWAN network. EE-LoRa always outperforms the total throughput of the legacy LoRaWAN, especially when the number of nodes increases. The obtained network throughput using OPT-MAX and OPT-DELTA are very close to that acquired using EE-LoRa when the number of nodes is lower than 3000. When the number of nodes is 4000, the total throughput improvement of EE-LoRa achieves 53%, 6.2%, and 6.28% compared to legacy LoRaWAN, OPT-MAX, and OPT-DELTA, respectively. Figure 10 depicts the LoRaWAN network's energy consumption during period \(T\), which represents the time between two consecutive transmissions. We can show that EE-LoRa consumes more energy than legacy LoRaWAN, in which each node chooses the lowest SF without regard for network performance. However, the energy consumption using OPT-MAX and OPT-DELTA are very high compared to EE-LoRa.