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JDR Vol.12 No.2 pp. 320-328
(2017)
doi: 10.20965/jdr.2017.p0320

Paper:

Early Fire Alert System During an Evacuation with Mobile Sensing Technology

Hideki Mori*, Masaki Ito*,†, and Kaoru Sezaki*,**

*Institute of Industrial Science, the University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

Corresponding author

**Center for Spatial Information Science, the University of Tokyo, Chiba, Japan

Received:
October 16, 2016
Accepted:
February 15, 2017
Online released:
March 16, 2017
Published:
March 20, 2017
Keywords:
participatory sensing, disaster prevention, mobile sensing.
Abstract

Natural disasters have inflicted major damage on humankind throughout history. Some of the most severe disasters in Japan and other countries have been earthquakes, which frequently trigger secondary disasters such as tsunamis, landslides, and fires. In fact, in Japan, most casualties in an earthquake are not due to the earthquake itself but to secondary disasters. Therefore, detecting the occurrence of secondary disasters and gathering the result in real-time are essential in order to minimize the number of casualties. Thus, we concentrated on creating an effective system for propagating information about fire accidents quickly using pedestrian mobile phones. (Note that we believed detecting fires was the easiest of these secondary disasters.) In addition, we establish a configuration-free system for propagating fire alerts by attaching an ad-hoc network device to fire alarms installed in homes. Thus, we can collect information on fire accidents without configuring any personal location information. We evaluate the effectiveness of our system in the event of a fire. Our results show that we can estimate the location of a fire with an error of <20 m. This is sufficiently accurate to locate a fire in order to obtain an overview of the situation.

Cite this article as:
H. Mori, M. Ito, and K. Sezaki, “Early Fire Alert System During an Evacuation with Mobile Sensing Technology,” J. Disaster Res., Vol.12, No.2, pp. 320-328, 2017.
Data files:
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Last updated on Dec. 11, 2018