Prototype of early fire detection system for home monitoring based on Wireless Sensor Network

Saputra, Ferry Astika; Rasyid, M. Udin Harun Al; Abiantoro, Bey Aryo

doi:10.1109/elecsym.2017.8240373

Cited by 29 publications

(18 citation statements)

References 6 publications

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“…While this is a prototype setup, it has the potential to develop into an IoT-like system capable of providing an early fire alarm system connected remotely to home owners through mobile phone and/or to the concerned authorities through any device connected to the internet. Several research works have been reported proposing an early fire detection system based on Wireless Sensor Networks (WSNs) [21,22]. However, these reported designs depend on low range ZigBee protocol-based devices that do not provide the long-range advantages of LPWAN.…”

Section: Introductionmentioning

confidence: 99%

Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis

Nazir

Mosleh

Takruri

et al. 2022

Fire

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Fire alarm systems are typically equipped with various sensors such as heat, smoke, and gas detectors. These provide fire alerts and notifications of emergency exits when a fire has been detected. However, such systems do not give early warning in order to allow appropriate action to be taken when an alarm is first triggered, as the fire may have already caused severe damage. This paper analyzes a new dataset gathered from controlled realistic fire experiments conducted in an indoor laboratory environment. The experiments were conducted in a controlled manner by triggering the source of fire using electrical devices and charcoal on paperboard, cardboard or clothing. Important data such as humidity, temperature, MQ139, Total Volatile Organic Compounds (TVOC) and eCO2 were collected using sensor devices. These datasets will be extremely valuable to researchers in the machine learning and data science communities interested in pursuing novel advanced statistical and machine learning techniques and methods for developing early fire detection systems. The analysis of the collected data demonstrates the possibility of using eCO2 and TVOC reading levels for early detection of smoldering fires. The experimental setup was based on Low-Power Wireless Area Networks (LPWAN), which can be used to reliably deliver fire-related data over long ranges without depending on the status of a cellular or WiFi Network.

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Section: Introductionmentioning

confidence: 99%

Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis

Nazir

Mosleh

Takruri

et al. 2022

Fire

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“…In order to verify the speed of the fire decision algorithms used in MAI-FDS, three algorithms were compared: the temperature threshold algorithm, the CNN-based algorithm, and the early fire detection system (EFDS) [28]. The fire decision algorithm uses temperature, humidity, gas, and image data as decision data.…”

Section: Discussionmentioning

confidence: 99%

“…If he used it with other sensors, he would be able to correct those errors. Saputra proposed a fire detection system that directly implemented a fuzzy algorithm and detected fire using multiple sensors [28]. The fuzzy algorithm integrates multiple sensor data and deduces the results.…”

Section: Related Workmentioning

confidence: 99%

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Park

Lee

Yun

et al. 2019

Sensors

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A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

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“…Ferry [21] proposed a WSN based model for fire detection and home monitoring. It was built using Carbon Monoxide, temperature, humidity, air and smoke sensors.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient and Lossless Routing Scheme for Fire Detection in Wireless Sensor Networks

Padmavathi¹,

Kapileswar²,

Teja³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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A fire detection system is one of the primary needs in forests, commercial and residential buildings. Fire detection is done using either wire-based systems or Wireless Sensor Networks (WSNs). In general, the Wireless Sensor Networks are preferred as they are faster and more accurate than the wire based fire detection systems. In Wireless Sensor Networks the fire detection can be done using various routing systems like the Wireless Fidelity (Wi-Fi), Low Power Wide Area Network (LPWAN), Wireless Metal Insulator Transition (MIT) network etc. We have adopted the Node centric lossless routing scheme which increases the load balance and minimizes the delay of relay data over the sensors to the base station using an Energy-Efficient Multilevel and Distance aware Clustering (EEMDC) for Wireless Sensor Networks. Generally, it is done using Hybrid Energy Efficient Distributive (HEED) protocol and Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols. It has drawbacks with the energy levels and relaying of data. The proposed technique involves lossless and energy efficient schemes for data transmission. It uses General Self-Organized Tree-based Energy Balance (GSTEB) routing protocol, which can alter the root and renew the routing tree with a bit of delay and with consumption of less energy. Distance-Energy Cluster Structure Algorithm (DECSA) has the ability to reduce the communication complexities. Therefore, a stable and balanced load is achieved than LEACH and HEED protocols.

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Prototype of early fire detection system for home monitoring based on Wireless Sensor Network

Cited by 29 publications

References 6 publications

Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis

Early Fire Detection: A New Indoor Laboratory Dataset and Data Distribution Analysis

Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Energy-Efficient and Lossless Routing Scheme for Fire Detection in Wireless Sensor Networks

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