Fire Early Warning Using Fire Sensors, Microcontroller and SMS Gateway

Kharisma, Rizqi Sukma; Setiyansah, Ardi

doi:10.18196/jrc.2372

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…Fifth paper was written by Rizqi Sukma Kharisma and Ardi Setiyansah [22]. In this paper, they described the development of fire early warning system using fire sensors, a microcontroller, and also a SMS gateway.…”

Section: B Literature Reviewmentioning

confidence: 99%

The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring

Hery

Haryani²,

Mitra³

et al. 2022

International Journal of New Media Technology

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Fire is a type of disaster that can occur anytime and anywhere as a result of any accidental or intentional causes. Without exception, houses are also very vulnerable to fire. To anticipate the catastrophic effects of fire that can destroy houses, advanced technology, such as the Internet of Things (IoT) can be utilized to detect the smoke and fire. This study aims to design an early warning fire detection system for home monitoring using smoke detection sensors based on Arduino microcontroller together with NodeMCU ESP8266. This early warning fire detection system is expected to function by notifying homeowners when detecting the presence of smoke in their homes. With the aid of this detection system, the issue of potential damage, death, or material loss caused by fire can be significantly reduced. The results and testing of the designed system will be discussed in the paper.

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Section: B Literature Reviewmentioning

confidence: 99%

The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring

Hery

Haryani²,

Mitra³

et al. 2022

International Journal of New Media Technology

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“…Fire alarms play a role in giving signals or signs of a fire (Kharisma and Setiyansah, 2021). The use of fire alarms refers to SNI 03-3985-2000.…”

Section: Active Fire Protection Facilities and Infrastructurementioning

confidence: 99%

Fire Protection System Evaluation in the Oncology Center Building of Hospital in Surabaya

Dzahabiyyah

Haqi

Suhariono

2022

IJOSH

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Introduction: The Oncology Center Building is the part of Hospital X Surabaya that focuses on developing cancer services. As a type A hospital, Hospital X is required to have a preparedness program in the facilities and infrastructure for fire protection and prevention. The purpose of this study was to evaluate the fire protection facilities and infrastructure in the Oncology Center Building of Hospital X Surabaya. Methods: This study was an observational study using a cross-sectional approach. The data analysis method used was descriptive. The primary data collection was carried out through interviews with the Hospital Occupational Safety and Health committee of Hospital X and the secondary data was in the form of profiles on Hospital X, as well as its policies, programs, guidelines, and SOPs related to fire prevention in Hospital X. Results: Active fire protection facilities and infrastructure in the form of fire detectors and fire alarms are in accordance with SNI 03-3985-2000. Meanwhile, other active fire protection facilities and infrastructure, such as fire extinguishers, hydrants, and sprinklers, are still not in accordance with the applicable policies because there are several elements that have not been fulfilled. The passive fire protection facilities and infrastructure are still not in accordance with existing policies because there are no walls, windows, and fireproof glass in the Oncology Center Building. Conclusion: The active and passive fire protection facilities and infrastructure in the Oncology Building, Hospital X still do not comply with the applicable policy because there are several elements of each fire protection facility and infrastructure that do not meet the specified standards.

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“…Several researchers have developed early detection of hotspots, including fire detection using video [5] and sensors [6]. Limitations in using sensors, especially gas sensors, can occur when there is other smoke, for example, people smoking.…”

Section: Introductionmentioning

confidence: 99%

Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier

Adhinata¹,

Ramadhan²

2022

IJACSA

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The forest is an outdoor environment not touched by the surrounding community, so it is not immediately handled when a fire occurs. Therefore, surveillance using cameras is needed to see the presence of fire hotspots in the forest. This study aims to detect hotspots through video data. As is known, fire has a variety of colors, ranging from yellow to reddish. The segmentation process requires a method that can recognize various fire colors to get a candidate fire object area in the video frame. The methods used for the color segmentation process are Gaussian Mixture Model (GMM) and Expectation-maximization (EM). The segmentation results are candidates for fire areas, which in the experiment used the value of K=4. This fire object candidate needs to be ascertained whether the segmented object is a fire object or another object. In the feature extraction stage, this research uses the DenseNet-169 or DenseNet-201 models. In this study, various color tests were carried out, namely RGB, HSV, and YCbCr. The test results show that RGB color produces the most optimal training accuracy. This RGB color configuration is used to test using video data. The test results show that the true positive and false negative values are quite good, 98.69% and 1.305%. This video data processing produces fps with an average of 14.43. So, it can be said that this combination of methods can be used to process real time data in case studies of fire detection.

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Fire Early Warning Using Fire Sensors, Microcontroller and SMS Gateway

Cited by 14 publications

References 12 publications

The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring

The Design of Microcontroller Based Early Warning Fire Detection System for Home Monitoring

Fire Protection System Evaluation in the Oncology Center Building of Hospital in Surabaya

Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier

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