## 2 Materials and Methods
### Experimental Setup and Evaluation Metrics  
To validate the functional performance and risk reduction efficacy of the proposed AI-integrated fire control system, a series of controlled experiments were conducted in a full-scale smart room mock-up equipped with operational HVAC ducts, ceiling diffusers, and networked sensing-actuation devices. The test environment measured 6 m \(\times\) 5 m \(\times\) 3 m (90 m\({}^{3}\)), representative of a standard commercial office or residential unit. The space was instrumented with thermal cameras, particulate density sensors, airflow meters, and high-speed data loggers to capture real-time system behavior under simulated fire conditions. Fire scenarios were initiated using standardized combustible materials including paper stacks, polyurethane foam, and cable insulation, following ignition protocols in line with ISO 9705 [54] (Room Fire Tests). Both flaming and smoldering fire types were tested to assess system responsiveness under diverse conditions. For each trial, the fire source was located in different zones of the ceiling-mounted diffuser grid to evaluate spatial coverage and directional suppression performance. The following evaluation metrics were used to quantify system performance:  
* Detection Time (s): Time elapsed from ignition to verified fire classification by the edge AI sensor. * Ventilation Activation Time (s): Delay between fire detection and full deployment of motorized ceiling diffusers. * Smoke Clearance Time (s): Time required to reduce visible smoke concentration below 10% optical density using ERV extraction. * Suppression Duration (s): Time from nozzle discharge initiation to full flame extinguishment. * False Alarm Rate (%): Incidence of non-fire events triggering suppression or ventilation. * Power Consumption (W): Total energy usage by the sensing, actuation, and communication subsystems during active operation. * System Uptime (%): Operational reliability under network-disrupted conditions using edge-only fallback control. To conclude the evaluation of the proposed AI-integrated fire control system, the experimental results demonstrated consistent and reliable performance across all key metrics. The system achieved rapid detection and response times, effective smoke clearance, and swift suppression without incurring false alarms--highlighting the accuracy and robustness of the hybrid sensing logic and control framework. Furthermore, its ability to maintain full operational capacity during simulated network outages underscores the advantage of edge-level autonomy in mission-critical safety applications. Figure 4 presents a schematic diagram illustrating the key components of the proposed integrated fire suppression system and their interconnections. The system is designed to automate the entire process from fire detection to extinguishing agent discharge through an intelligent control structure. As shown in the diagram, the core modules include the sensor unit (101), control unit (300), rotating nozzle (201), actuators (203, 204), extinguishing agent storage tank (400), and the connecting ducts and pipelines (407, 408). Notably, the rotating nozzle (201) is engineered for high-precision directional control, enabling targeted discharge toward the fire source based on commands from the control unit (300). Through this networked integration of all components, the system enhances suppression efficiency and minimizes response time in real-world fire scenarios. The combination of fast time-to-intervention, precise spatial actuation, and minimal energy consumption positions the proposed system as a high-performance, resilient alternative to conventional fire suppression technologies. These experimental outcomes serve as a strong validation for the system's practical viability and inform the subsequent quantitative risk reduction analysis presented in the next section. Figure 5 presents the actual prototype of the smart fire response module deployed in the experimental testbed.