IOT Based Fire Detection System

Fire Detection Systems are now widely used in various safety and security applications. The major amount of fire starts due to the electric short circuit. It leads to damage to property and also loss of life. To avoid that or to minimize the damage caused by fire outbreaks due to electric short circuits an IoT technology is used to control such a kind of risk. Traditional fire detection systems are not that effective and quick to alert the owner about fire, in case no one is present on the location. To overcome this problem in this paper we present the design and development of IoT based Fire Detection System. A system that combines qualities for fire, temperature and smoke detection, sending alert Text Message about the fire to the user along with onsite alarm(buzzer), updating temperature, humidity and smoke on ThingSpeak cloud every 15 seconds, and it also moves manually with the help of Android Application. The Fire Detection System consists of four main parts: Multiple sensors, communication system (Bluetooth, GSM, NodeMCU), motion planning (Manual patrolling), and Android application for manual patrolling of the system. This Fire Detection system can be used in college, school, office, and industry for safety purposes.

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Early Fire Detection System in Tanzania Markets

Engineering Proceedings ◽

10.3390/ecsa-7-08215 ◽

2020 ◽

Vol 2 (1) ◽

pp. 50

Author(s):

Rambo Hilary ◽

Philemon Rotich ◽

Anna Geofrey ◽

Anael Sam

Keyword(s):

Data Processing ◽

Mobile Application ◽

Detection System ◽

Early Stage ◽

Fire Detection ◽

Wireless Sensor ◽

Multiple Sensors ◽

Fire Detection System ◽

Processing Subsystem ◽

Fire Outbreak

Application of wireless sensor networks (WSN) and Internet of Things (IoT) used to provide real-time monitoring of fire outbreak in markets. The system integrates three subsystems namely; sensing subsystem which uses multiple sensors for detecting fire outbreaks. Data processing subsystem which collects data from the sensing subsystem through Xbee, analyses, and uploads data to the cloud. If values exceed the sensor threshold, an alarm is triggered and notification is sent to stakeholders via mobile application subsystem. The integration between sensing, data processing, and mobile application subsystems pave a new way for the mitigation of fire outbreaks at its early stage.

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Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽

10.3390/s19092025 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2025 ◽

Cited By ~ 6

Author(s):

Jun Hong Park ◽

Seunggi Lee ◽

Seongjin Yun ◽

Hanjin Kim ◽

Won-Tae Kim

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Transfer ◽

Detection System ◽

Smart Cities ◽

Fire Detection ◽

Machine Learning Algorithms ◽

Detection Accuracy ◽

Detection Systems ◽

Fire Detection System

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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Reducing False Alarms in Vision-Based Fire Detection

Applied Video Processing in Surveillance and Monitoring Systems - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-1022-2.ch012 ◽

2017 ◽

pp. 263-290

Author(s):

Neethidevan Veerapathiran ◽

Anand S.

Keyword(s):

Video Processing ◽

Detection System ◽

Chemical Properties ◽

Fire Detection ◽

False Alarms ◽

Video Signals ◽

Detection Systems ◽

Physical Sensors ◽

Single Feature ◽

Fire Detection System

Computer vision techniques are mainly used now a days to detect the fire. There are also many challenges in trying whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature and many other features (i.e.) color have to be taken into consideration. Early warning and instantaneous responses are the preventing ideas to avoid losses affecting environment as well as human causalities. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms. In order to reduce false alarms of conventional fire detection systems, system make use of vision based fire detection system. This chapter discuss about the fundamentals of videos, various issues in processing video signals, various algorithms for video processing using vision techniques.

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Realization of People Density and Smoke Flow in Buildings during Fire Accidents Using Raspberry and OpenCV

Sustainability ◽

10.3390/su131911082 ◽

2021 ◽

Vol 13 (19) ◽

pp. 11082

Author(s):

Gajanand S. Birajdar ◽

Mohammed Baz ◽

Rajesh Singh ◽

Mamoon Rashid ◽

Anita Gehlot ◽

...

Keyword(s):

Real Time ◽

Web Application ◽

Detection System ◽

Human Life ◽

Fire Detection ◽

Smoke Detection ◽

Urban Buildings ◽

System A ◽

Fire Detection System ◽

Fire Accidents

Fire accidents in residential, commercial, and industrial environments are a major concern since they cause considerable infrastructure and human life damage. On other hand, the risk of fires is growing in conjunction with the growth of urban buildings. The existing techniques for detecting fire through smoke sensors are difficult in large regions. Furthermore, during fire accidents, the visibility of the evacuation path is occupied with smoke and, thus, causes challenges for people evacuating individuals from the building. To overcome this challenge, we have recommended a vision-based fire detection system. A vision-based fire detection system is implemented to identify fire events as well as to count the number people inside the building. In this study, deep neural network (DNN) models, i.e., MobileNet SSD and ResNet101, are embedded in the vision node along with the Kinect sensor in order to detect fire accidents and further count the number of people inside the building. A web application is developed and integrated with the vision node through a local server for visualizing the real-time events in the building related to the fire and people counting. Finally, a real-time experiment is performed to check the accuracy of the proposed system for smoke detection and people density.

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The influence of external factors on false alarms in an infrared fire detection system

International Journal of Wildland Fire ◽

10.1071/wf13200 ◽

2015 ◽

Vol 24 (2) ◽

pp. 261 ◽

Cited By ~ 6

Author(s):

Pedro Canales Mengod ◽

José Andrés Torrent Bravo ◽

Leticia López Sardá

Keyword(s):

Detection System ◽

Fire Detection ◽

Time Of Day ◽

External Factors ◽

False Alarms ◽

Detection Algorithms ◽

Detection Systems ◽

Camera Orientation ◽

Fire Detection System

There have been many studies on the use of different automatic wildfire detection systems, yet few long-term analyses of any of these techniques have been reported. In this paper we present the results obtained from the study of an infrared fire detection system that has been working in the field for more than 10 years, over which period it produced 10 519 false alarms. This article gives a brief description of the system and discusses the false alarms, showing that factors that are often not taken into account in the development of fire detection algorithms, such as camera orientation, the type of surface being monitored, or the time of day, can lead to false alarms being triggered.

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