# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### WSN Simulation
#### 4.2.3 Energy Dissipation Analysis  
The simulation results generated by the CupCarbon tool (Figure 12) for a 300 m area demonstrate distinct energy dissipation patterns across the three network topologies, dense, non-dense, and Mesh 2D, in a small-scale wildfire monitoring area.  
Particularly, the non-dense topology shows higher energy efficiency at all fire diffusion rates (20%, 40%, and 60%) compared to dense and Mesh 2D. This efficiency stems from its simplified single-tier architecture, which minimizes routing complexity, reduces hop counts, and avoids the overhead of dynamic inter-tier management needed in the dense topology. However, dense topologies excel in large-scale deployments through vertical rerouting and redundancy; their multi-layered design becomes counterproductive in confined areas, introducing unnecessary energy expenditure for maintaining tiered connections. In contrast, the Mesh 2D topology leads to the highest energy consumption due to linear hop scaling and lack of adaptive routing, judging it inappropriate for dynamic wildfire environments. The non-dense topology's lower energy demand also aligns with optimized duty cycling, where nodes prioritize sleep modes during low activity and employ efficient burst transmissions under higher fire rates. These findings emphasize the vital role of topology adaptability: non-dense balances simplicity and functionality for small areas, whereas dense's strengths emerge in expansive, obstruction-rich terrains. The results validate this paper's emphasis on context-aware network design, promoting, for topology, a choice based on deployment scale and environmental dynamics to improve energy sustainability in remote wildfire detection systems. A further investigation into advanced duty-cycling protocols and real-world validation could refine these models for broader applicability. These results align with theoretical predictions [64] as given by Equation (15). In fact, the non-dense topology established higher energy efficiency in small-scale deployments due to reduced hop counts and simplified routing, while the dense topology's multi-tier overhead underperformed in confined areas, as expected. Mesh 2D's rigid grid structure confirmed its inefficiency, validating theoretical critiques of its linear hop scaling and lack of adaptability in dynamic wildfire scenarios.  
\[P_{total}=N\Big{[}D(P_{tx}+P_{rx}+P_{idle})+(1-D)P_{sleep}\Big{]}+C(h-1)(P_{ rx}+P_{rx}) \tag{15}\]  
With  
* \(N\): Number of nodes in the network.
* \(D\): Duty cycle (fraction of time active), proportional to fire rate (e.g., D = 0.2 at 20% fire rate).
* \(P_{tx}\), \(P_{rx}\), \(P_{idle}\), \(P_{slap}\): Power consumed in transmit, receive, idle, and sleep states.  
Figure 12: Power dissipation analysis in a 300 m coverage area.  
Dynamic arbitration reduces the PLR by 3.6% through prioritized alert handling. Crucially, the AI model increases accuracy from 76.5% to 99.97% by replacing heuristic thresholds with data-driven risk prediction, confirming its indispensability for precise early warnings. Topology optimization provides foundational efficiency gains, while AI delivers diagnostic precision.