Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

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.

Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6’s object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system’s capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

Fire detection is a critical task in ensuring the safety of human lives and property. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNNs), have shown promising results in fire detection due to their ability to automatically learn relevant features from images. In this project, we propose a real-time fire detection system using CNNs to detect fires in images or video streams. The proposed system consists of a CNN-based architecture that includes multiple convolutional layers for feature extraction, activation functions for introducing non-linearity, pooling layers for spatial dimension reduction, and fully connected layers for global pattern learning. The architecture is trained on a labelled dataset of fire and non-fire images or videos using optimization algorithms to minimize the loss function. To achieve real-time fire detection, the trained CNN model is integrated into a fire detection system that can process images or video streams in real-time. The system captures images or video frames from a live feed, pre-processes the data, and passes it through the CNN model for fire detection. The system can generate alerts or notifications in real-time when fire is detected, allowing for prompt response and mitigation measures. The performance of the proposed fire detection system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The system is tested on different datasets and under various conditions to assess its accuracy, robustness, and real-time processing capabilities. The results of this project demonstrate the effectiveness of CNNs for real-time fire detection and highlight the potential of the proposed system for enhancing fire safety in various applications, including surveillance systems, smart buildings, and industrial settings. The developed system can be used as an early warning tool for fire detection, enabling timely response and mitigation, and helping to prevent or minimize the damage caused by fires. Keywords: Fire

Standard laser-based fire detection systems are often based on measuring the variation of optical signal amplitude. However, mechanical noise interference and loss from dust and steam can obscure the detection signal, resulting in faulty results or the inability to detect a potential fire. The presented fire detection technology will allow the detection of fire in harsh and dusty areas, which are prone to fires, where current systems show limited performance or are unable to operate. It is not the amount of light or its wavelength that is used for detecting fire, but how the refractive index randomly fluctuates due to heat convection from the fire. In practical terms, this means that light obstruction from ambient dust particles will not be a problem as long as a small fraction of the light is detected and that fires without visible flames can still be detected. The standalone laser system consists of a Linux-based Red Pitaya system, a cheap 650nm laser diode, and a positive-intrinsic-negative photo-detector. Laser light propagates through the monitored area and reflects off a retroreflector generating a speckle pattern. Every 3s, time traces and frequency noise spectra are measured, and eight descriptors are deduced to identify a potential fire. Both laboratory and factory acceptance tests have been performed with success.

In recent years, fire detection technologies have helped safeguard lives and property from hazards. Early fire warning methods, such as smoke or gas sensors, are ineffectual. Many fires have caused deaths and property damage. IoT is a fast-growing technology. It contains equipment, buildings, electrical systems, vehicles, and everyday things with computing and sensing capabilities. These objects can be managed and monitored remotely as they are connected to the Internet. In the Internet of Things concept, low-power devices like sensors and controllers are linked together using the concept of Low Power Wide Area Network (LPWAN). Long Range Wide Area Network (LoRaWAN) is an LPWAN product used on the Internet of Things (IoT). It is well suited for networks of things connected to the Internet, where terminals send a minute amount of sensor data over large distances, providing the end terminals with battery lifetimes of years. In this article, we design and implement a LoRaWAN-based system for smart building fire detection and prevention, not reliant upon Wireless Fidelity (Wi-Fi) connection. A LoRa node with a combination of sensors can detect smoke, gas, Liquefied Petroleum Gas (LPG), propane, methane, hydrogen, alcohol, temperature, and humidity. We developed the system in a real-world environment utilizing Wi-Fi Lora 32 boards. The performance is evaluated considering the response time and overall network delay. The tests are carried out in different lengths (0–600 m) and heights above the ground (0–2 m) in an open environment and indoor (1st Floor–3rd floor) environment. We observed that the proposed system outperformed in sensing and data transfer from sensing nodes to the controller boards.

A fire disaster that does not save can certainly cause losses, both in the form of objects and casualties. This occurs for several reasons: late information obtained from the fire department or the owner's ignorance at the time of a fire. In this study, a fire early detection system was built using smoke, heat, and gas sensors based on an SMS gateway and an alarm. This system is used to provide information about fire detection as early as possible to protect against fire disasters. With this system, the potential and risk of fire can be reduced. This system is used to identify potential fires that occur in housing. Several experiments were carried out with fire simulations to get the reaction from the sensors used. Covers smoke testing, temperature testing, gas testing, and SMS message responses from various providers. This research produces a fire early warning system that provides SMS and alarm alerts.

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.

Multisensor fire detection algorithm (MSFDA) is one of the current important research issues for intelligent building. First, we design a fire fighting robot with extinguish for the intelligent building. The fire fighting robot is constructed using aluminium frame. The contour of the robot is cylinder. The diameter is 40 cm, and height is about 80 cm. There are six systems in the fire fighting robot, including structure, avoidance obstacle and driver system, software development system, fire detection, remote supervise system and others. We design the fire detection system using three flame sensors in the fire fighting robot. The adaptive fusion method is proposed for tire detection of fire fighting robot. We use computer simulation to improve the method to be adequate for fire detection. We design the fire detection system in the fire fighting robot, and program the tire detection and fighting procedure using sensor based method. Finally, we implement the fire detection system using fire fighting robot. If fire accident is true, the robot can find out fire source using the proposed method by fire detection system, and move to fire source to fight the fire using extinguish.

A modified YOLOv5 architecture for efficient fire detection in smart cities

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).

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.

Unwanted fire causes many disasters. Thus, fire detection has been an important phenomenon to save human life. In this paper, we proposed a hybrid model that incorporates computer vision and smoke sensor-based fire detection into a single system to devise a more accurate and smart fire detection system. Our vision-based fire detection model utilizes color and motion attributes of fire combustion. Rule-based color segmentation is performed for classifying fire color regions and a modified image difference technique is used for detecting the motion of fire regions in consecutive frames. The proposed dynamic threshold technique is used for mitigating the false positive rate of the vision-based model. After performing a vision-based analysis, the proposed algorithm uses the MQ-2 smoke sensor to detect environment smoke and gas caused by fire combustion. Our implemented system raises a fire alarm in the form of remote notification after analyzing vision and smoke sensor-based model data. Real life experimental data shows that our system can detect fire with 86.67% of accuracy. In addition, the proposed system can be incorporated in existing surveillance systems at an adequately low extra expense.

The security of home, laboratory, office, factory and building is important to human life. We develop an intelligent multisensor based security system that contains a fire fighting robot in our daily life. The security system can detect abnormal and dangerous situation and notify us. First, we design a fire fighting robot with extinguisher for the intelligent building. The fire fighting robot is constructed using aluminum frame. The contour of the robot is cylinder. The diameter is 50 cm, and height is about 130 cm. There are six systems in the fire fighting robot, including structure, avoidance obstacle and driver system, software development system, fire detection, remote supervise system and others. We design the fire detection system using two flame sensors in the fire fighting robot. We design the fire detection system in the fire fighting robot, and program the fire detection and fighting procedure using sensor based method. We design a low cost based obstacle detection modular using IR sensor and ultrasonic sensor in the robot. We think that man-machine interface in the fire fighting robot that must has mobility and convenience, we use touch screen to display system status, and design a general user interface (GUI) on the touch screen. Finally, we implement the fire detection system using fire fighting robot. If fire accident is true, the robot can find out fire source using the proposed method by fire detection system, and move to fire source to fight the fire using extinguish.

New sensors and devices are being incorporated in modern buildings and cities to facilitate the understanding of its dynamics and improve its efficiency. With this in mind, the combination of information technology and sensors capable of capturing and sharing energy data with other devices can help solve power consumption problems. However, large volumes of these data are collected and stored uninterrupted, becoming a challenge to analyze it to confirm trends, identify hidden patterns, and outliers to aid in decision-making. As an alternative to address the issue, this paper presents an interactive visual analytics system to understand energy data coming from a smart building or city. This system provides ways to analyze the data at different levels of time granularities for the identification of energy trends, patterns, and data outliers. Moreover, it combines different algorithms that allow fulfilling predictive analysis. An analysis with domain experts demonstrates the feasibility and advantages of using the system to monitor energy data.

Smart cities are novel and difficult to study. Fires can kill people and destroy resources in cities near forests, farms, and open spaces. Sensor networks and UAVs are used to construct an early fire detection system to reduce fires. The suggested method uses sensors and IoT apps to monitor the surroundings. The suggested fire detection system includes UAVs, wireless sensors, and cloud computing. Image processing improves fire detection in the proposed system. Genuine detection is also improved by rules. Many current fire detection technologies are compared to the suggested system's simulation findings. The approach improves forest fire detection from 89 to 97%.