A Fire Detection and Rescue Support Framework with Wireless Sensor Networks

Lim, Yong Beom; Lim, Sangsoon; Choi, Jae-Hyuk; Cho, Seongho; Kim, Chong-kwon; Hu, Yong-Woo Lee Hua; Zhang, Haobin; Hu, Haiyang; Xu, Bin; Li, Juefeng; Ma, Albert

doi:10.1109/iccit.2007.4420250

Cited by 7 publications

(8 citation statements)

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“…That is also the main reason why conventional fire detectors are placed on the ceilings of buildings [ 14 ]. For realisation of a wireless sensing IAQ system, the data of the low cost system is sampled at the sampling rate of 10 sample/min [ 15 ]. The data has been recorded for 15 min each time.…”

Section: Methodsmentioning

confidence: 99%

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building

Andrew¹,

Zakaria²,

Saad³

et al. 2016

Sensors

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In this study, an early fire detection algorithm has been proposed based on low cost array sensing system, utilising off- the shelf gas sensors, dust particles and ambient sensors such as temperature and humidity sensor. The odour or “smellprint” emanated from various fire sources and building construction materials at early stage are measured. For this purpose, odour profile data from five common fire sources and three common building construction materials were used to develop the classification model. Normalised feature extractions of the smell print data were performed before subjected to prediction classifier. These features represent the odour signals in the time domain. The obtained features undergo the proposed multi-stage feature selection technique and lastly, further reduced by Principal Component Analysis (PCA), a dimension reduction technique. The hybrid PCA-PNN based approach has been applied on different datasets from in-house developed system and the portable electronic nose unit. Experimental classification results show that the dimension reduction process performed by PCA has improved the classification accuracy and provided high reliability, regardless of ambient temperature and humidity variation, baseline sensor drift, the different gas concentration level and exposure towards different heating temperature range.

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Section: Methodsmentioning

confidence: 99%

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building

Andrew¹,

Zakaria²,

Saad³

et al. 2016

Sensors

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“…For large building or areas, a wireless system can have an issue with transmitting information to the main control panel. There are many works that investigated fire detection system utilizing different technologies and components [6]- [10]. In [6], ZigBee is used as the wireless technology in a fire detection system.…”

Section: Introductionmentioning

confidence: 99%

“…A fire monitoring system is designed in [10]. The hardware part includes the fire detection trigger module, the control module and the monitoring center module.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fire detection and alert system using labVIEW

Idris

Hashim

Kadmin

et al. 2019

IJECE

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Fire detection systems are designed to discover fires and allow the safe evacuation of occupants as well as protecting the safety of emergency response personnel. This paper describes the design and development of a fire detection and alert system. Temperature and flame sensors are used to indicate the occurrence of fire. This work consists of two parts, which are transmitter and receiver, both using ZigBee wireless technology. Arduino Uno is used as the microcontroller at the transmitter part to control the sensor nodes and give alert when over temperature and flame are detected. At the transmitter, the collected data from the sensors are transmitted by an XBee module operated as router node. At the receiver side, an XBee coordinator module which is attached to a computer using USB to serial communication captured the data for further processing. In addition, an interactive and user-friendly Graphical User Interface (GUI) is developed. LabVIEW software is used to design the GUI which displays and analyze the possibility of fire happening. The system can display the fire location and provides early warning to allow occupants to escape the building safely.

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“…For example, if fire is detected using both smoke and temperature sensors, an alarm will be generated when temperature exceeds 30 • C and smoke exceeds 100 mg/L. Lim et al [11] introduce a generic fire detection and rescue support system, which they say is applicable for any other disaster recoveries.…”

Section: Analysis Of Fire Detection Techniquesmentioning

confidence: 99%

Fire Data Analysis and Feature Reduction Using Computational Intelligence Methods

Bahrepour

Zwaag

Meratnia

et al. 2010

Advances in Intelligent Decision Technologies

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Fire is basically the fast oxidation of a substance that produces gases and chemical productions. These chemical productions can be read by sensors to yield an insight about type and place of the fire. However, as fires may occur in indoor or outdoor areas, the type of gases and therefore sensor readings become different. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. In this paper, the authors investigate spatial analysis of data for indoor and outdoor fires using data-mining approaches for WSN-based fire detection purposes. This paper also delves into correlated data features in fire data sets and investigates the most contributing features for fire detection applications.

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A Fire Detection and Rescue Support Framework with Wireless Sensor Networks

Cited by 7 publications

References 0 publications

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building

Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building

Intelligent fire detection and alert system using labVIEW

Fire Data Analysis and Feature Reduction Using Computational Intelligence Methods

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