# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 5 Conclusions  
This paper describes an energy-efficient and scalable WSN architecture fitted for early wildfire detection in remote locations. By using a hybrid star/circular topology combined with adaptive routing protocols and round-robin dynamic arbitration, the proposed architecture exhibits sizable advances relatively to conventional Mesh 2D topologies. It achieves a 53-55% reduction in node deployment, 41-81% lower latency, and 50-60% fewer routing hops. The simulation results for a 300 m radius confirm the advantage of the non-dense topology in energy consumption (0.0425-0.0832 W), while the dense configuration proves most effective in optimizing the PLR, achieving up to an 80.4% reduction compared to Mesh 2D topologies.  
Furthermore, the integration of artificial intelligence (multiclass logistic regression), allows high-precision fire risk prediction (99.97% in terms of accuracy) based on temperature and humidity sensor inputs. Combined with ZigBee and GSM technologies, the system guarantees robust communication in areas with limited or no internet coverage.  
These results highlight practical advantages, including cost-effective deployment, adaptability to diverse areas, and improved sensitivity to fire risks. This architecture  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Node Count** & \multirow{2}{*}{**Topology**} & **Covered Area (km\({}^{2}\))** & **Node Spacing (m)** & **PLR/Node** & **Avg. Power/Node (W)** & **Total Power (W)** \\ \hline Proposed & 24 & Star/Circular & 0.2826 & 100 & 2.08\% & 0.003 & 0.0718 \\ \hline [PERSON] et al. (2015) [6] & 300 (avg) & 2D Mesh & 2.5 (avg) & 50–100 & 7.2\% & 0.255 & 76.5 \\ \hline [PERSON] et al. (2023) [11] & 200 & \begin{tabular}{c} Hierarchical \\ Tree \\ \end{tabular} & 4–6 & 100–150 & \(\leq\)2\% & 0.210 & 42.0 \\ \hline [PERSON] et al. (2021) [15] & 30 & Star & 5 & 200–300 & 1.8\% & 0.120 & 3.6 \\ \hline [PERSON] et al. (2020) [25] & 50 & Star & 1 & 100 & 1.2\% & 0.180 & 9.0 \\ \hline [PERSON] et al. (2016) [43] & 500 (avg) & \begin{tabular}{c} Multi-hop \\ Mesh \\ \end{tabular} & 15 (avg) & 50–100 & 10\% & 0.300 & 150.0 \\ \hline [PERSON] et al. (2022) [52] & 300 & \
\begin{tabular}{c} Heterogeneous \\ Mesh \\ \end{tabular} & 8 & 100 & 2.5\% & 0.240 & 72.0 \\ \hline [PERSON] et al. (2009) [55] & 100 & Cluster Tree & 2 & 100–150 & 2\% & 0.210 & 21.0 \\ \hline [PERSON] et al. (2000) [64] & 100 &
\begin{tabular}{c} Dynamic \\ Cluster \\ \end{tabular} & 1–2 & 50 & \(\leq\)5\% & 0.180 & 18.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance analysis of WSN architectures for wildfire detection.  
provides a promising foundation for next-generation wildfire detection systems and has the potential to support open environmental monitoring initiatives globally. Future work will consider lightweight FWI-inspired methods using additional IoT sensors.  
Methodology, A.Z.; Software, S.A., H.A.S., S.A.Q. and S.G.; Validation, H.A.S. and S.A.Q.; Formal analysis, S.G.; Data curation, S.A.; Writing--original draft, A.Z.; Writing--review & editing, S.G.; Visualization, S.A.; Supervision, A.Z.; Project administration, A.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.