A Fuzzy Decision in Smart Fire and Home Security System

IOP Conference Series: Materials Science and Engineering

The purpose of this study is to create an early fire detection system based on fuzzy logic. This system provides early warning of fire hazards, reduces the risk of casualties, and is able to be implemented to a wider scale or scope. This system consists of a multisensor to detect fire, smoke and temperature in the room. KY-026 sensor as fire detection, MQ-9 sensor as smoke detection and DS18b20 sensor as temperature detection. The results of the sensor readings are processed by a microcontroller implanted with a Sugeno fuzzy logic method. Sensor input is processed through several stages, namely fuzzification, rule evaluation and deffuzification. The output from this system is a condition value between 1 and 5. The average error of testing between Arduino modules with Matlab is 0.99%.

IAEME, 2019

This study aims to develop an early detection system of abnormal conditions, causes of fires that occur in electrical installations in building in real time, so that it can immediately stop it. By applying the vague logic of a diversified pattern recognition method, this study succeeded in applying new modeling to the abnormal conditions of the system, so that it could be used to detect fire hazards early in buildings. The results of the prototype laboratory test show that this development system can achieve a success rate of 96.4% detection, while the old system using Miniature Circuit Breaker only reaches 42.8%.

International Journal of Computer Applications, 2016

This paper proposes a hardware model that provides new fire detection and control mechanism with the interface of artificial neural network and fuzzy logic. This work is based on the integration of hardware module and implementation of artificial neural fuzzy inference system (ANFIS). The hardware consists of temperature sensor, smoke sensor, flame detector and a microcontroller unit. The sensors sense the environment and send data to microcontroller for further processing. Here the microcontroller will work as a control unit. The hardware model of the system also consists of the GSM module for sending the warning message if severe fire exists, and a GPS module in order to indicate the fire location. This technique expresses the idea of implementing Fuzzy logic on the real time data which is collected by the sensors. The system aims to predict fire danger by sensing various parameters i.e. smoke, temperature etc. at the early stage. Artificial neural fuzzy inference system (ANFIS) has been utilized in order to enhance the reliability and certainty of real time fire detection mechanism and to reduce the false alarm rates. The system will focus on collection of data from sensors, data fusion through fuzzy logic and quantification of fire warning level. This neural network based fire alarm system

MEKATRONIKA

Fire alarm system consist of several type of sensors that works togeher in order to detect a fire breakouts. But the systems itself consist of errors especially detecting smoke which sometimes could not lead to a potential fire breakouts instead causing a false alarm. Moreover, vision cameras are expensive to be installed and it takes a lot amount of money for maintenance alone. This causes many household does not equip with such devices making fire breakouts to be inevitable. In this research, the aim of this is to create a system that is cheap and could detect a potential fire before it could even happen by applying Fuzzy logic technique. By adding an Artificial Intelligence in a fire monitoring and warning systems, This could reduce error and predict the right event that could lead to a fire breakouts. With this, many household especially in Malaysia could have the safety to overcome this

Safe From Fire (SFF) is an intelligent self controlled smart fire extinguisher system assembled with multiple sensors, actuators and operated by micro-controller unit (MCU). It takes input signals from various sensors placed in different position of the monitored area, and combines integrated fuzzy logic to identify fire breakout locations and severity. Data fusion algorithm facilitates the system to discard deceptive fire situations such as: cigarette smoke, welding etc. During the fire hazard SFF notifies the fire service and others by text messages and telephone calls. Along with ringing fire alarm it announces the fire affected locations and severity. To prevent fire from spreading it breaks electric circuits of the affected area, releases the extinguishing gas pointing to the exact fire locations. This paper presents how this system is built, components, and connection diagram and implementation logic. Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability. It is observed that SFF system demonstrated its efficiency most of the cases perfectly.

Energies, 2021

Fire monitoring systems have usually been based on a single sensor such as smoke or flame. These single sensor systems have been unable to distinguish between true and false presence of fire, such as a smoke from a cigarette which might cause the fire alarm to go off. Consuming energy all day long and being dependent on one sensor that might end with false alert is not efficient and environmentally friendly. We need a system that is efficient not only in sensing fire accurately, but we also need a solution which is smart. In order to improve upon the results of existing single sensor systems, our system uses a combination of three sensors to increase the efficiency. The result from the sensor is then analyzed by a specified rule-set using an AI-based fuzzy logic algorithm; defined in the purposed research, our system detects the presence of fire. Our system is designed to make smart decisions based on the situation; it provides feature updated alerts and hardware controls such as en...

Sensors

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the ra...

2020

Fire in a building is one of the disasters that is very damaging and very serious for human safety, it is known that DKI Jakarta has the highest fire cases with an average of two incidents per day with the biggest cause coming from electricity. The use of multiple sensors, known as multi-sensors, has demonstrated its superiority in carrying out early detection of fire so that the development of technology is currently focused on the use of better decision making algorithms. This study uses many sensors consisting of flame sensors, carbon monoxide sensors, temperature sensors and smoke sensors as an early detection of fire with fuzzy logic decision making algorithms in modeling low voltage electrical panels, presented in the form of a comparison between conventional type fuzzy logic with fuzzy logic modular type. Sensor data processing from the simulation of smoke to fire through Arduino Mega, then simulations using Simulink on MATLAB, the simulation results showed a reduction in the...

IEEE, 2021

One of the biggest issues for architects, planners, and landowners is house combustion. Singular sensors have been used in the case of a fire for a long time, but they cannot quantify the volume of fire to warn emergency service units. To resolve this problem, this research aims to develop an intelligent smart fire warning system that detects fires utilizing connected sensors and alerts property owners, emergency services. The current model is divided into three modules: Smoke Detection Module (SDM), which is responsible for detecting smoke to prevent unwanted incidents; Notification Send Module (NSM), which is responsible for creating an alert service to alert the closest support center and user; and Emergency Alarm Module (EAM), which is responsible for handling the emergency alarm schedule when a fire arises. The results prove that the device worked well, and it should be remembered that our proposal can be integrated into any kind of setting, such as a house, workplace, ship, or industry.

Recently, many applications of fuzzy logic are emerging. More and more popular articles are published, reviewing the field of applications or reviewing the theory. The purpose of this report is to give a common sense introduction to the possibilities of fuzzy logic. One of the most important areas where fuzzy logic is used is that of systems where the input as well as output are not fuzzy but just numbers. For such systems it is shown that the fuzzy approach actually is a function approximation method, often allowing more insight than different methods. Even in application areas that may not seem fuzzy at first sight, possibilities appear to exist. This is the reason for this surveying literature study on fuzzy logic applications in general and fire control applications in particular.

2018

One of smart home function is fire alert detection. The symptom detection of fire in the house is important action to prevent the mass fire and save many things. This research applies the new system of fire detection using gas leak concentration to predict the explosion and fire earlier called fire predictor and the fire appearance detector. The fire predictor just show the gas leak concentration and make an alarm rang. The fire detector use fuzzy system to make the fire detector classification. The output simulation system can send the data to MFC, but the MFC reader cannot parse it in real time.

Architectural Science Review, 1994

The paper starts with the introduction of the basic requirements of classical intrusion detection and alarm systems, and fire detection systems. The drawbacks of such conventional systems are highlighted. Techniques of computer vision are employed to remove the drawbacks and at the same time, increase the reliability and response rate of the systems. For security and low-level fite detection, a fuzzy-logic-based image-comparison algorithm is deemed adequate. In order to confirm the existence of fite or smoke, techniques related to optical flow are employed as high-level fire or smoke detection which generate a velocity field for the image so that the decision can be judged using fuzzy logic. Details of implementation and some experimental results have been included in the paper for illustration.

Journal of Engineering

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the ar...

The design has been developed since the social and economic cost of natural disasters which has increased in recent years due to population growth, change in land use patterns, migration and unplanned urbanization, environmental degradation and global climate change. Catastrophic disasters include fires, earthquakes, volcanic eruptions, tropical cyclones, floods, and droughts. Fire considered being natural or man-made, thus the management shall provide safety of the building occupant. The design consists of five major circuits to compensate the system operation. It includes Detection and Initiating Devices (DEADS), Notification Devices (NODES), Central Station Monitor (CSM), Annunciation Devices (ANODES) and the Suppression Circuitry. The DEADS is composed of a smoke detector and smoke ionization sensors which transmit initiated signal to CSM. The NODES are active devices like smoke alarms, and speakers attached to every room designed to give alarms to the room occupants. The Central Station Monitor designed with Arduino Uno as the Microcontroller served as the brain of the system interfaced with PHP & MySQL. The ANODES works once fire cannot be suppressed by the system itself, thereby when the fire department and other incident team needs to be contacted. The suppressor composed of robotic-arm connected to the water supply, fire hydrants, and sprinkler heads.

International Journal of Electrical and Computer Engineering (IJECE), 2024

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user's email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.