Push the Limit of Acoustic Indoor Fire Monitoring

Zheng Wang, Xiaoqi Sun, Yuanqing Zheng, Yanwen Wang

Abstract: In indoor fire rescue, swift and precise fire source localization and fire severity assessment are pivotal for firefighting strategic planning and casualty evacuation. However, existing solutions primarily focus on detecting fire presence, which do not offer insights into fire's localization and severity. In this paper, we propose UltraFlame, an accurate, user-friendly, and timely system for pinpointing fire sources and assessing fire severity based on acoustic sensing, which bridges significant gaps in fire safety and response. UltraFlame consists of a collocated commodity speaker and microphone pair, sensing fire by emitting inaudible sound waves. We conduct an in-depth investigation of sound propagation impacted by fire combustion, providing physically interpretable data for deep learning framework and enabling fire source localization even without any sound reflection by fire. We dedicatedly establish a correlation between fire severity and sound propagation delays, which serves as an effective indicator for estimating the heated region. Finally, an appropriate deep learning framework is employed to effectively extract temporal and spatial features from channel measurement. Extensive experiments demonstrate that 94% of the localization results have an error of less than 0.8m. Additionally, UltraFlame achieves an accuracy of 96.9% in fire severity assessment across diverse setups, providing real-time and reliable monitoring.

External IDs:dblp:conf/infocom/WangSZ025