## 5 Performance Evaluation
### Simulation Results  
In this section, we present the IoT LoRa performance evaluation using the metrics from Table 2. We compared the proposed SFPCR scheme to the FSRC scheme, also called the relay scheme, the mesh scheme, and the cluster-Based scheme. We focused on three factors: transmission probability, packets sent by a given number of LoRa nodes, and energy consumption. The number of nodes, payload size, and gateway distance were compared. This study validated the suggested technique for multihop LoRa networks by using relay and clustering approaches in corresponding spreading factor zones. Figures 5-7 illustrate how the packet success probability of LoRa devices varied with the number of nodes, payload size (B), and node distance from the GW (m). All the schemes exhibited an inverse proportionality between the success probability and the variables of number of nodes, payload size, and node distance from the GW. Figure 5 indicates that increasing the number of nodes decreased the chance of packets successfully reaching the GW for the FSRC, cluster-based, and mesh schemes. This was due to the capture effect as nodes competed for the channel and collisions arising from blind transmissions as these nodes transmitted data packets as soon as they were ready to transmit [55], which hindered the successful delivery of packets to the GW. In contrast, the proposed SFPCR scheme \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Coding Rate & 4/5 \\ Simulation time & 3600 s \\ Payload CRC & ON \\ Duty cycle regulation & 1 \% \\ Channel frequency & 868 (MHz) \\ Header (H) & 0 \\ Preamble symbol & 8 \\ Packet interval rate & 10–15 (min) \\ Low data rate optimization (DE) & 1 \\ Path loss exponent (\(\gamma\)) & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._employed collision-free transmission [45]. Due to this regulated access to the medium, each LoRa device transmitted its packets directly to the selected recipient. The SFPCR scheme deployed, among other things, density-based subspace clusters in the HSFZ and relay nodes in the LSFZ in order to boost and improve the performance of remote nodes so that data packets could be successfully sent to the GW with no transmission errors. Consequently, the SFPCR scheme outperformed the other schemes with the highest number of LDs by approximately 47.6% packet success probability, followed by the FSRC scheme at 24.7%, as shown in Table 3. However, as the number of LDs increased, the performance degraded because an increase in the number of nodes caused an automatic increase in the probability of packet error and interference [56]. Figure 6 depicts an inverse proportionality between success probability and payload size (B) for the SFPCR scheme and other comparative schemes. Because the LoRa network is utilized for long-distance transmission, an increase in packet size has a detrimental impact on the network's performance. Therefore, the smaller the size of a data packet, the greater the possibility of success. The success probability of the SFPCR scheme was roughly 65.7% with a default payload size of 20 bytes, as shown in Table 3. It provided a superior method for reducing the quantity of traffic delivered to the GW by using data aggregation for all nodes clustered in the HSFZ, in contrast to previous techniques. Figure 7 demonstrates a correlation between the distance of LoRa devices from the GW and their success probability. The success probability of all schemes diminished exponentially with rising average distances. However, at a distance between 3000 m and 3500 m, the SFPCR scheme and the FSRC scheme improved the success probability marginally due to the use of relays to extend packets from far LoRa devices to the GW. Moreover, the SFPCR scheme outperformed the other schemes in terms of success probability by more than 29.1% as the transmission distance from the GW increased.