# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### WSN Simulation
#### 4.2.2 Latency and Packet Loss Analysis  
Figure 10 presents tests conducted for forest areas with diameters ranging from 500 m to 3500 m. The total end-to-end latency L is calculated using Equation (4), assuming identical operating conditions across the three topologies. (\(t_{tx}=0.2\) s, \(t_{proc}=0.1\) s, and \(t_{queue}=0.3\) s, \(t_{reroute}=0.5\) s).  
The test results indicate that the proposed hybrid star/circle topologies demonstrate a significant latency advantage over the traditional 2D Mesh topology. The hybrid dense topology leverages vertical rerouting between tiers (with 100 m radius increments), reducing hop counts by 50-60% compared to non-dense and 2D Mesh topologies. This structural efficiency enables the dense hybrid architecture to achieve 41-47% lower latency than the  
Figure 10: Latency comparison between star/circle and 2D Mesh topologies.  
Figure 9: Comparison of star/circle and 2D Mesh topology deployments.  
non-dense hybrid and 73-81% lower latency than 2D Mesh. The non-dense hybrid, while constrained by intra-tier routing, still outperforms the mesh by 55-65% due to axis-based path optimization. While the non-dense hybrid lags slightly behind its dense counterpart, both significantly outperform the mesh in terms of energy efficiency and resilience to obstacles.  
The Packed Loss Rate (PLR) quantifies the probability of end-to-end packet loss over a multi-hop network path. For a route with h hops, where each hop has an independent per-hop loss rate of 2%, the PLR is calculated using Equation (14) as a one-shot computation based on final transmission statistics. Despite assuming a uniform per-hop loss, the proposed star/circle topology outperforms 2D Mesh in terms of packet delivery efficiency (\(P_{loss,i}=0.02\)) as shown in Figure (a)a.  
\[PLR=1-(1-0.02)^{h}\!\!=1-(0.98)^{h} \tag{14}\]  
The PLR varies significantly across the three network architectures due to differences in routing efficiency and hop count scaling. The dense hybrid star/circle performs best, with the PLR rising gradually from 3.96% (500 m) to 14.87% (3500 m). The non-dense hybrid shows faster PLR growth, reaching 31.09%, while 2D Mesh performs worst, with the PLR climbing to 75.81% due to inefficient scaling. At 3500 m, the dense hybrid reduces the PLR by 52.2% compared to the non-dense hybrid and by 80.4% compared to 2D Mesh. These results highlight the importance of efficient, adaptive inter-tier routing. Dense hybrid's design limits hop buildup, making it ideal for real-time, low-latency applications like wildfire detection.  
Figure (b)b presents the simulation results generated by the CupCarbon tool for a 300 m area, considering that ZigBee/IEEE 802.15.4 is limited in range and sensitive to interference and that sleeps are not scheduled. These results show that the dense hybrid star/circle topology achieves the lowest PLR for all fire diffusion percentages (20-80%), with a 17.3% improvement over 2D Mesh and 11.7% over non-dense at 60% diffusion. At 80%, dense maintains an 80.2% PLR, while non-dense and Mesh 2D exceed 91%. These high PLR values are explained by the fact that non-dense and Mesh 2D topologies are more affected by simulation considerations about ZigBee limitations and the absence of sleep scheduling. As fire spreads, node failures increase, degrading connectivity and amplifying packet loss. This explains that the dense topology's adaptive routing dynamically reroutes data around compromised nodes, whereas non-dense and Mesh 2D networks suffer from redundant hops and inflexible paths.  
Figure 11: PLR comparison across topologies: (a) varying diameters, (b) diameter = 300 m.