## 2 Materials and Methods  
### System Architecture Overview  
The proposed AI-integrated fire response system is architected as a multi-layered, edge-optimized platform that unifies early detection, automated smoke extraction, localized fire suppression, and smart communication. The system comprises four core functional subsystems: (i) a hybrid sensing unit, (ii) a ventilation and suppression module, (iii) a real-time control and communication hub, and (iv) a building integration interface. These components operate cohesively to minimize fire-induced risks and ensure continuity of building operations under emergency conditions. The hybrid sensing unit combines dual-channel smoke and heat sensors with an embedded edge AI processor to achieve fast and reliable fire detection. By employing convolutional neural networks (CNNs) trained on fire-specific datasets, the system conducts real-time, on-device analysis of particulate density and thermal gradients. This simultaneous evaluation enhances detection accuracy and significantly reduces false alarms by confirming fire events only when both smoke and heat signatures are present [23,24]. Unlike cloud-based systems, all inference is performed locally, eliminating network latency and enabling response initiation within six seconds of ignition. Upon fire detection, the system activates a motorized ceiling diffuser array integrated with an energy recovery ventilation (ERV) mechanism. The diffusers automatically rotate and open to establish a controlled negative-pressure airflow, enabling efficient smoke and heat extraction. This process enhances visibility and air quality for occupant evacuation while concurrently limiting fire propagation by reducing oxygen availability. In parallel, a precision-actuated nozzle with full rotational capability delivers a non-conductive, residue-free clean extinguishing agent, ensuring localized suppression with minimal risk of equipment damage or environmental contamination. All sensing and actuation devices are linked to the SmartThings Edge hub, which functions as the system's control core. The hub is configured with a TLS-secured MQTT protocol to enable bidirectional communication with user devices, control centers, and cloud services. Through this interface, real-time alerts, control signals, and status updates are transmitted with sub-second latency. The system is also interoperable with building automation systems (BAS) and building management systems (BMS), enabling centralized control, logging, and predictive maintenance analytics. The overall architecture is intentionally modular and scalable. It supports flexible zoning, redundancy, and selective activation of components based on risk profiling, occupancy, and fire growth patterns. Figure 1 illustrates the operational workflow of the proposed AI-enhanced fire response platform, detailing the sequential logic from system initialization to termination. The system begins with the acquisition of video data through edge-connected cameras, followed by intelligent fire signal detection using trained convolutional neural networks. Upon detection, the system identifies spatial fire characteristics to determine the location, scale, and appropriate suppression strategy. The flow bifurcates into two concurrent operations: the issuance of early warning signals and the configuration of suppression parameters such as nozzle aiming angle and spray type. Once configured, the fire suppressant--typically a clean agent or water mist--is discharged automatically. A real-time feedback loop evaluates whether suppression has been successfully completed. If not, the system continues suppression in a closed-loop manner. After confirmation of successful fire extinguishment, suppression activities cease, and the platform transitions to post-event monitoring and log recording. The final step involves system termination, ensuring that all operational data are archived for future diagnostics, maintenance, or audit.