## 3 Results
### System Performance Evaluation
#### 3.1.4 False Alarm and Environmental Robustness  
To ensure practical applicability in real-world environments, the proposed fire detection system was tested under a variety of non-fire stimuli and ambient conditions. The goal was to evaluate its resistance to false alarms and its stability across common environmental variables such as humidity, airflow, and particulate interference.  
Simulated non-fire scenarios included aerosol spray, steam discharge, incense smoke, vaping emissions, and warm air currents generated by HVAC systems. In all ten controlled test runs per scenario (\(n=50\) total), the system correctly classified these events as non-fire, yielding a false positive rate of 0%. This performance is attributed to the dual-mode sensing strategy--simultaneously analyzing smoke particle characteristics and thermal gradients--and the real-time classification by the edge AI module, which had been trained on an augmented dataset including these interference types.  
Environmental robustness was further verified through stress testing at varying ambient humidity levels (30-85%), airflow velocities (0.1-0.5 m/s), and temperature ranges (10-35 \({}^{\circ}\)C). Detection latency and classification accuracy remained consistent across all tested conditions, with no significant drift in baseline sensor outputs or AI inference scores. The edge processor's localized decision-making also ensured continued operation in simulated network outage conditions, with no dependency on cloud connectivity for alarm logic execution.  
These results confirm that the proposed system maintains high selectivity and environmental stability, minimizing the operational burden associated with false activations while preserving responsiveness under dynamic indoor conditions. This makes it particularly suitable for deployment in sensitive spaces such as residential areas, data centers, and underground transportation facilities where nuisance alarms can cause costly disruptions.  
Figure 7: Diagram illustrating the fire detection and suppression flow enabled by edge-AI processing. The system determines nozzle direction and activates suppression within a defined coverage area of 2.5 m radius, ensuring localized and targeted fire response.  
The alert issuance process is managed locally by the edge AI processor, which performs real-time classification and directly triggers suppression and ventilation actions, eliminating the need for external server dependencies. This architecture ensures a deterministic response, even during communication failures. Real-time sensor data is processed and temporarily stored at the edge for immediate inference and control, while key event logs are securely transmitted to a cloud server for long-term storage and analysis.  
To meet mission-critical reliability requirements, the system integrates AES-based encryption, dual-path redundant communication channels, and MODBUS-based data synchronization. This hybrid design not only ensures fast, localized alarm responses but also strengthens the system's resilience and data integrity. These features collectively enhance operational reliability, making the system suitable for deployment in safety-critical fire safety applications.  
In addition to detection accuracy, the system's architecture was validated for operational reliability. Alarm triggers were issued locally by the embedded edge AI unit, independent of external servers, ensuring immediate actuation even under network failure. Real-time data were buffered internally in binary (key-value) format and externally synchronized through MODBUS registers. The system also employed encryption protocols and dual-path communication to enhance data security and operational resilience under mission-critical conditions.