SMOKE AND FIRE DETECTION SYSTEM USING ARDUINO WITH GSM NOTIFICATION

The purpose of this study is to build an early fire detection system that is able to detect the conditions of the environment and the results are informed quickly on the incident area and fire department. The research method used was experimental using Arduino Uno, GPS Ublox Neo 6MV2, SIM900A and three sensors such as fire sensors, smoke sensors and temperature sensors. All sensors function as a fire indication detection system. The results of the system test obtained an average GPS error of 1.6% with an accuracy of reading shifts and the average distance obtained was ± 4 meters. The average data transmission speed between Providers is 1 second because the processing time and sending speed are in line with the network conditions and the capabilities of the GSM module used.

The fire detection system combines the simultaneous measurements of smoke, carbon monoxide, and carbon dioxide. The security of campus against intruders moving in laboratories, class rooms, staffrooms or washrooms. The fire alarm system consists of Fire detectors (with can be smoke detector, heat, or Infra-Red detectors), control unit and alarm system. A fire detection system is developed based on the simultaneous measurements of temperature and smoke. The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone. Previous fire detection algorithms used data from sensors for temperature, smoke, and combustion products. The smoke sensor alarms when the analog output signal exceeds or equal the threshold value. The node includes analog sensors to measure smoke, carbon monoxide (CO) and temperature. A fire alarm system should reliably and in a timely way notify building occupants about the presence of fire indicators, such as smoke or high temperatures.

Current indoor fire detection systems often have limitations, including insufficient intelligence and slow response times. Recent studies have started to explore AI vision-based methods for real-time fire and smoke detection to replace traditional fire sensors. However, indoor environments present many interference factors, and few methods can effectively demonstrate superior fire and smoke detection performance under multiple simulated interference conditions. To meet the requirements of real-time, high accuracy and anti-interference, a 3D convolutional neural network (3D-CNN)-based real-time fire and smoke detection model has been developed via red-green-blue (RGB) and near-infrared (NIR) feature fusion. RGB and NIR images with complementary information were used as dual-stream inputs to the model. The feature extraction module was enhanced by 3D attention mechanisms to simultaneously capture dynamic features between frames and static features within frames of fire and smoke. A non-linear feature gated fusion module has been developed to solve the challenges of missing multi-modal features and insufficient fusion. To validate the engineering effectiveness of the model, a large-scale multi-modal dataset was constructed in real indoor building scenarios, and a real-time indoor fire and smoke detection system was also developed and deployed at the experimental site.

Fires very often occur due to gas leaks, careless throwing of cigarette butts or electrical short circuits which result in the fire getting bigger, causing a lot of losses. Many fire cases are discovered when the fire has already occurred and few can be detected from the start. The research aims to develop an effective system for detecting fires by integrating fire and smoke sensors, as well as providing notifications via the Blynk platform. This research is research and development (R&D) using a prototype development model. The results of the functionality testing obtained a 100% presentation which shows that the prototype is suitable for use. Tests were carried out using three levels of fire levels and various distances, showing good response from the fire sensor with a consistent incoming notification speed of around 2-3 seconds. The results of fire measurements using the MQ-2 smoke sensor show accuracy in detecting smoke concentration levels, with timely notification of potential fires. This research has produced a detection tool that has the ability to detect early fire in a room which can provide warning notifications that are integrated with Internet of Things (IoT).

Early Forest Fire Detection and Verification using Optical Smoke, Gas and Microwave Sensors

A single-chip CMOS smoke and temperature sensor for use as an intelligent fire detector is proposed. The proposed smoke sensor measures smoke density based on the light-scattering method. The temperature sensor is integrated with the smoke sensor not only to sense heat from a fire but also to compensate for the temperature dependency of the smoke sensor. The prototype chip includes an on-chip photodiode (PD), pixel circuit, correlated double sampling (CDS) circuit, and analog-to-digital converter (ADC). The prototype chip was fabricated using a 0.35-mu m CMOS process and was placed inside the smoke detection chamber, while the thermistor for the temperature sensor is placed outside the chamber. The measurement results show plusmn 1% smoke detection accuracy over the range 4% ~ 25% and plusmn1degC temperature-sensing accuracy over the range 25degC ~ 95degC. The power consumption of the prototype chip is 220 nW, excluding the infrared light-emitting diode (IR LED).

This research describes the process of making and developing a fire detection system using IoTtechnology. The background for making this tool is because the existing tool at the research site, namely at the Captive Power Plant of PT Indorama, is still a conventional device with an ordinary smoke sensor system and notifications only via a buzzer. This system combines telegram notification mode. Using three sensors namely temperature sensor, smoke sensor and fire sensor is a very effective combination as an early warning system. The test results show that the system provides notification via telegram when there is a significant increase in temperature above 31oC. The smoke sensor is tested with three smoke conditions, namely thin, medium and thick with a value of 704-803 ppm. The sensor response is less than 2 seconds, notification instructions to telegrams are sent directly without any delay. In the fire sensor test, six test conditions were carried out with variations in distance ranging from 10-100 cm, the sensor response was under 2 seconds, immediately activated the buzzer and a telegram notification was sent. The results of this study contribute to the hope that with the three sensor combinations the security layer will be more optimal and in real terms it can be used in industrial buildings which are prone to fire.

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm

Currently, the forest fires are worldwide problem which requires a complete solution. The consequences of it are related, not only, to the environmental and biodiversity destruction but also to human and economic losses. In this article, forest fire and smoke detection systems, methods and technics literature review are carried out. To accomplish this aim, a generic mobile and non-mobile monitoring and surveillance systems applied in this field analysis took place. From the review carried out, it has been found that for fire detection two types of methods have been used, one based on machine learning and another through color features extraction of images or video frames, on the other hand, the combination of both methods is used for the detection of smoke and fire or only smoke, this guarantees accurate rates of more than 90%. Finally, a detailed analysis of the use of these fire and smoke detection algorithms in the UAS/UAV’s systems was carried out paying special attention on them due to the flexibility, versatility, and maneuverability abilities among others displayed by these systems that help prevent and mitigate forest fires.KeywordsFire forestSmoke forestMachine learningColor features extraction

Fire is one of the few disasters that frequently occurred in Indonesia. Jakarta has archived 1139 fire incident with more than 20 casualties in 2016. By the rapid development of information technology and communication in Indonesia, it turns out that the number of incident and casualty still cannot be suppressed. This research is focused on early fire detection and home monitoring based on Wireless Sensor Network using 4 sensors such as temperature, air humidity, Carbon Monoxide, and smoke sensors. If the fire probability calculation categorized as high, system will perform an action of unlocking home door lock, turning on alarm, and notifying the user. Based on the trials and testing result, system is able to send notification to user precisely. However, error still can be encountered with up to 0.3% ratio on fire probability calculation.

Fire is a disaster that can cause great losses, both in terms of material and non-material. Therefore, early detection of fire is very important to prevent the spread of fire and reduce the impact caused. This study aims to design a fire and smoke detection system based on the Internet of Things (IoT). This system will use sensors that are sensitive to changes in temperature, the presence of fire, and smoke, so that it can provide information quickly and efficiently. This system operates by detecting the presence of fire through a flame sensor, temperature changes using a temperature sensor (DHT11), and smoke gas concentration with an MQ-2 sensor. Data collected by the sensor will be sent to the microcontroller (ESP8266) for processing and then forwarded to the IoT platform, so that information can be accessed in real-time via mobile devices or computers. The test results show that this system is able to detect fires with good accuracy and provide warnings quickly. By implementing IoT technology, this system is expected to increase efficiency and speed in responding to fires, so that the impacts caused can be minimized.

This research aims to develop a real-time monitoring system for peatland fire potential based on the Internet of Things (IoT) with a focus on early detection of potential peatland fires. The main problem to be solved is the lack of an effective system in the early detection of potential peatland fires, which can cause serious environmental impacts. The method used involves the use of air temperature, air humidity, soil moisture, and fire detection sensors integrated with alarm-based alerts. Data collection is done in real-time to provide a deeper understanding of peatland conditions and potential fire risks. The research results show that the developed system is capable of providing accurate and fast information related to peatland conditions, thus helping to prevent and reduce the impact of peatland fires. With this system, it is expected to increase efficiency in early fire detection and minimize the losses caused by peatland fires.

ANR are featured by mobility and communication capabilities as they involve mobile robots and ad-hoc sensor networks enabling ubiquitous applications. This work proposes a low-cost ANR surveillance system for intruder and fire events within a large environment that is divided into operational zones with sensor nodes. Each sensor node comprises of low-cost odor, smoke, infrared fire, and motion detector sensors, radio frequency (RF) transceivers for networking and communication. Events detected within the zones are routed to the robots while suppression systems are activated based on the event criticality. On receiving the event signals from different zones, the robots navigate to these zones to assess the scenario as required. This assessment is then routed to a base station for further processing. Each robot is associated with one operational zone, but is not limited to it, since it performs other environment related functions. The network enables a distributed system with sensor nodes as the primary task distributor, while the robots negotiate and redistribute these tasks. Hence, path planning, and navigation would be executed locally and communicated among the robot team for effective collaboration. This work aims to advance research in the field of ANR through applicational approaches.

This paper introduces a vehicle door monitoring system based on a real-time operating system (RTOS) to improve vehicle safety and security. The system employs RTOS principles to manage concurrent tasks, ensuring deterministic responses and efficient handling of critical operations, including door status, controls locking mechanisms, manages ignition, and handles emergencies. In this, two critical emergency situations have been handled: a crash with the preceding vehicle and fire detection within the vehicle. Real-time sensor inputs are received from infrared sensors, a collision switch sensor, a fire sensor, and control buttons, with feedback provided to users via liquid-crystal display (LCD), a Global System for Mobile Communications (GSM) module, and Universal Asynchronous Receiver Transmitter (UART) interfaces. In emergency scenarios, the system performs prioritized tasks, sending Short Message Service (SMS) alerts to designated contacts, ensuring rapid and reliable communication. The RTOS framework enables task scheduling, interprocess communication, and resource management to ensure timely execution of safety-critical functions and minimize latencies. The implementation demonstrates the potential of RTOS in developing reliable and responsive safety systems, emphasizing its suitability for real-time vehicular applications.

Multi-criteria fire detection systems using a probabilistic neural network