## 3 Results
### System Performance Evaluation
#### 3.1.1 Fire Detection Responsiveness  
The system's fire detection capability was assessed by measuring the elapsed time from ignition to confirmed fire classification by the edge AI module. Across ten trials involving varied ignition sources--including paper, polyurethane foam, and electrical cable--under both flaming and smoldering conditions, the system demonstrated a consistent and rapid response. The mean detection time was measured at 5.8 s, with a standard deviation of 0.6 s. This performance significantly outperforms conventional ionization or photoelectric detectors, which typically activate after 20-30 s [55], especially under low-visibility smoldering scenarios. The hybrid sensor design contributed to this improvement by simultaneously evaluating thermal rise and particulate density, reducing reliance on any single indicator. Furthermore, the AI inference engine successfully distinguished fire events from non-threatening stimuli such as incense smoke, steam, and aerosol sprays, yielding a false positive rate of 0% in the experimental dataset. This confirms the reliability of the detection logic in realistic indoor environments. Table 3 presents the fire detection times recorded across ten experimental trials for four different ignition materials--paper, wood, polyurethane foam, and electrical cable. All measurements fall within one standard deviation of the system's mean detection time (5.8 \(\pm\) 0.3 s), demonstrating consistent and reliable early fire recognition across diverse combustion sources. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trial No.** & **Paper (s)** & **Wood (s)** & **Polyurethane Foam (s)** & **Electrical Cable (s)** \\ \hline
1 & 5.7 & 5.9 & 5.8 & 5.6 \\
2 & 5.9 & 5.6 & 5.7 & 5.9 \\
3 & 5.8 & 5.8 & 5.6 & 5.7 \\
4 & 5.6 & 5.9 & 5.9 & 5.8 \\
5 & 5.9 & 5.7 & 5.7 & 5.8 \\
6 & 5.7 & 5.6 & 5.8 & 5.9 \\
7 & 5.8 & 5.9 & 5.6 & 5.6 \\
8 & 5.6 & 5.8 & 5.9 & 5.7 \\
9 & 5.9 & 5.7 & 5.7 & 5.6 \\
10 & 5.7 & 5.6 & 5.8 & 5.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fire detection times for various ignition materials during controlled experiments. This table summarizes the detection latency of the AI-integrated fire response system across ten trials involving different ignition sources: paper, wood, polyurethane foam, and electrical cable insulation. Each value represents the time (in seconds) elapsed from ignition to confirmed fire classification by the edge AI module. All recorded times fall within one standard deviation of the system’s mean detection time (5.8 \(\pm\) 0.3 s), highlighting the consistency and responsiveness of the hybrid sensor across diverse fire types. Figure 6 provides a comparative visualization of fire detection times for four different ignition materials--paper, wood, polyurethane foam, and electrical cable--based on repeated trials in a controlled smart room testbed. The box plot format enables intuitive assessment of central tendency, dispersion, and outliers for each material. The consistent sub-6-s detection across all categories highlights the robustness and responsiveness of the proposed edge AI system, regardless of combustion type or material properties. To evaluate whether the proposed fire detection module demonstrates statistically consistent response times across different combustible materials, we conducted hypothesis testing using \(p\)-value analysis. Specifically, ten independent ignition-response trials were conducted for four common fire sources--paper, wood, polyurethane foam, and electrical cable--under identical experimental conditions. For each trial, the detection time was recorded from ignition to system activation. An independent samples \(t\)-test was applied to all pairwise material combinations to assess whether observed differences in detection time were statistically significant. As presented in Table 4, all resulting \(p\)-values were greater than 0.05, indicating no significant differences between material types at the 95% confidence level.