## 4 Network Implementation and Learning Algorithm
### Hybrid DELM Model
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **SF** & **TP** & **BW** & **CR** & **Power consumed (mWh)** & **Packet Delay (ms)** & **Packet Loss** \\ \hline Default LoRa & 12 & 14 & 125 & 4 & 43.18 & 85,448 & 73 \\ \hline Random Forest & 12 & 12 & 125 & 3 & 34.98 & 70,921 & 62 \\ \hline DELM & 11 & 10 & 250 & 3 & 29.97 & 63,231 & 56 \\ \hline Hybrid DELM & 9 & 8 & 250 & 2 & 26.37 & 57,250 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance analysis of LoRa node no. 3. Figure 5: Comparative analysis of LoRa performance: (**a**) power consumption representation; (**b**) packet delay representation; (**c**) packet loss representation. Finally, the hybrid DELM model was selected due to its effectiveness and efficiency in achieving the objective of the study; it achieved a LoRa node power reduction of 39%, with a model accuracy of 0.75. Looking ahead, further research and efforts should be focused on developing the hybrid DELM model to enable it to overcome some limitations, including the computational process, execution time, and intensive memory requirements, in order to achieve more advanced network optimization tasks. Conceptualization, A.K., H.T. and F.D. ; methodology, A.K. ; formal analysis, A.K., H.T. and F.D. ; investigation, A.K. ; writing--original draft preparation, A.K. ; supervision, H.T. and F.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data were derived from public-domain resources. The authors declare no conflicts of interest. ## References

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