Development of a Real-time Safest Evacuation Route using Internet of Things and Reinforcement Learning in Case of Fire in a Building 


Vol. 37,  No. 2, pp. 97-105, Apr.  2022
10.14346/JKOSOS.2022.37.2.97


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  Abstract

Human casualties from fires are increasing worldwide. The majority of human deaths occur during the evacuation process, as occupants panic and are unaware of the location of the fire and evacuation routes. Using an Internet of Things (IoT) sensor and reinforcement learning, we propose a method to find the safest evacuation route by considering the fire location, flame speed, occupant position, and walking conditions. The first step is detecting the fire with IoT-based devices. The second step is identifying the occupant's position via a beacon connected to the occupant's mobile phone. In the third step, the collected information, flame speed, and walking conditions are input into the reinforcement learning model to derive the optimal evacuation route. This study makes it possible to provide the safest evacuation route for individual occupants in real time. This study is expected to reduce human casualties caused by fires.

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  Cite this article

[IEEE Style]

안유선 and 최하늘, "Development of a Real-time Safest Evacuation Route using Internet of Things and Reinforcement Learning in Case of Fire in a Building," Journal of the Korean Society of Safety, vol. 37, no. 2, pp. 97-105, 2022. DOI: 10.14346/JKOSOS.2022.37.2.97.

[ACM Style]

안유선 and 최하늘. 2022. Development of a Real-time Safest Evacuation Route using Internet of Things and Reinforcement Learning in Case of Fire in a Building. Journal of the Korean Society of Safety, 37, 2, (2022), 97-105. DOI: 10.14346/JKOSOS.2022.37.2.97.

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