## 5 Conclusions  
This study presents an integrated, AI-based fire safety platform that combines edge computing, hybrid sensing, and smart suppression mechanisms to address limitations inherent in conventional systems. The experimental and simulation results provide strong evidence for the system's superior performance across three key dimensions: detection speed, suppression precision, and overall risk reduction. The first innovation lies in the system's edge AI-driven detection framework, which reduced average detection latency to 5.8 s--substantially outperforming traditional photoelectric or heat-based detectors. This rapid detection enabled timely actuation of ventilation and suppression modules, significantly improving occupant survivability during early-stage fire events. Moreover, the dual-mode sensing architecture exhibited exceptional selectivity, eliminating false alarms during non-fire scenarios such as steam, vaping, or incense. This level of robustness enhances the system's practical applicability in sensitive environments prone to nuisance triggers. Second, the use of a residue-free clean extinguishing agent--deployed via a 360\({}^{\circ}\) motorized nozzle--enabled spatially confined flame suppression within 13.2 s on average. The localized delivery pattern not only minimized damage to adjacent infrastructure but also ensured compatibility with electronics-rich environments such as data centers and control rooms. In conjunction with a fast-acting ERV-based ventilation mechanism, the system restored breathable air quality and visibility within 88-95 s, offering critical support for safe evacuation and responder access. Third, a probabilistic risk assessment conducted using ISO 31000 and NFPA 551 methodologies revealed substantial reductions across all risk domains: 75.6% in life safety, 58.0% in property damage, and 67.1% in business interruption, resulting in an aggregate composite risk reduction of approximately 73% (\(\Delta\)Ascore/\(\Sigma\)baseline). These improvements shift the residual risk into the ALARP (As Low As Reasonably Practicable) zone, aligning with best practices in performance-based fire protection engineering. Building on these core performance gains and risk reduction outcomes, future research is increasingly oriented toward the integration of advanced digital technologies to further enhance system intelligence, responsiveness, and operational efficiency. In particular, the incorporation of an Explainable AI (XAI)-driven risk decision framework, an AIoT-enabled modular response architecture, and seamless integration with Building Information Modeling (BIM) are emerging as pivotal directions for the development of next-generation intelligent fire safety systems. [PERSON]. [81] demonstrated that XAI-based fire detection models significantly improve transparency and operator confidence by visualizing how key sensor inputs influence classification decisions. In parallel, [PERSON]. [82] emphasized the value of AIoT-enabled architectures in enabling real-time data fusion, decentralized control, and adaptive response coordination across heterogeneous suppression units. This perspective is further expanded by [83], who introduced an AIoT-powered digital twin framework capable of predicting and visualizing fire spread dynamics, thereby advancing real-time situational awareness and decision support. On the architectural front, [PERSON]. [84] illustrated that BIM-integrated systems can generate structure-aware risk maps and deliver real-time evacuation guidance, enhancing both occupant safety and first responder effectiveness. Complementing this, [PERSON] and [85] highlighted the role of BIM-based smart systems in optimizing evacuation planning and delivering context-sensitive alerts tailored to spatial layouts and fire progression.