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Improving Fire Safety Systems Based On Internet Of Things And Deep Learning

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thesis
posted on 21.12.2021, 16:26 authored by Mohamed Gamaleldin
Structure fires are one of the main concerns for fire safety systems. The actual fire safety of a building depends on not only how it is designed and constructed, but also on how it is operated. Computational fluid dynamics software is the current solution to reduce the casualties in the fire circumstances. However, it consumes hours to provide the results in some cases that makes it hard to run in real-time. It also does not accept any changes after starting the simulation, which makes it unsuitable for running in the dynamic nature of the fire. On the other hand, the current evacuation signs are fixed, which might guide occupants and firefighter to dangerous zones.

In this research, we present a smoke emulator that runs in real-time to reflect what is happening on the ground-truth. This system is achieved using a light-weight smoke emulator engine, deep learning, and internet of things. The IoT sensors are sending the measurements to correct the emulator from any deviation and reflect events such as fire starting, people movement, and the door’s status. This emulator helps the firefighter by providing them with a map that shows the smoke development in the building. They can take a snapshot from the current status of the building and try different virtual evacuation and firefighting plans to pick the best and safest for them to proceed. The system will also control the exit signs to have adaptive exit routes that guide occupants away from fire and smoke to minimize the exposure time to the toxic gases

History

Language

English

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis

Thesis Advisor

Lian Zhao Zaiyi Liao