Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

logic-driven


Introduction
Fire is a physical process that produces smoke, heat, and fame.Fire is used for many household activities such as cooking, lighting candles, and many more.On the other hand, fre can seriously harm people and damage their property.Systems for detecting fres are necessary to reduce the loss of personal property due to both induced and artifcial fres [1,2].Te International Association of Fire and Rescue Services reported that from 1993 to 2020, there were 1123.6 thousand fre-related fatalities [3].However, the number of deaths from 1993 to 2020 is gradually decreasing.Tankfully, more intelligent fre warning devices have steadily reduced the number of fre injuries.Due to this, numerous novel fre detection and prevention sensors have the capacity to use the fre detection module to identify the fre source in the case of a fre outbreak.Tey may then convey the identifed message to the user over the Internet and a GSM modem, as well as communicate the precise latitude and longitude coordinates of the fre's location to the fre station, thanks to advancements in recent years.As a result, the construction of smart buildings and structures is given more attention globally [4,5].
Fire can create havoc during outbreaks.Te things that require fre to exist are oxygen, fuel, and temperature.It can cause a loss of lives and property damage.Te three components' availability decides the number of fre outbreaks (hearth) to develop.A combination of oxygen, fuel, and heat can cause hearth.So, the fuel and oxygen required to create the fre are abundant in households and industries.During a fre outbreak, the wood, ceiling, papers, or combusted fuel is potential fuel.Te accidental fre outbreak starts slowly, can last long hours, cause huge property damage, and make survival impossible.During fre outbreaks, a large amount of smoke is generated, which causes invisibility.Te ceiling helps to raise the temperature and helps the small fre to develop into a big one.Hence, fre detectors are invented to detect fre in the early stages.However, the fre detectors have some problems that cannot be resolved.Tere are three types of fre detectors which are as follows: smoke, fame, and thermal.Termal detectors are further classifed as spot detectors, fxed temperature line-type detectors, and fxed temp detectors.Spot detectors detect the abnormal rise in temperature over a short period.Fixed temperature line-type detectors are composed of two cables enclosed in an insulated sheathing designed to rupture when exposed to high temperatures.One of the advantages of these detectors is their relatively lower installation cost.On the other hand, smoke detectors emerged in the late 1990s, and their functioning is based on the principles of photosensitivity and ionization.
Te advantage of smoke detectors compared to thermal detectors is that they can detect the fre during the starting stage and help reduce the damage being done.Flame detectors are the line-of-sight devices that are operated on the principle of infrared and ultraviolet rays.Tese are primarily used in vehicles.Fire alarm systems are enabled with the help of these detectors.In the 1980s, the smoke detectors were invented, and with the help of these, a fre alarm system is discovered.Te fre alarm system consists of a single smoke sensor and a buzzer.Many false alarms are triggered due to the lack of novelty in the mechanism used to design the fre alarm system.To overcome this issue, multiple sensors are used.Te fuzzy logic mechanism (IF-THEN rules) can be used to minimize the issue.Hence, the multiple sensors (temperature, fame, and smoke) and fuzzy logic are combined.Most of the alarm systems consist of sirens or any other mode of caution.Te far-of notifcation system uses the GSM communication system because of its cozy and dependable nature.Utilizing a web portal facilitates updating the fre intensity information, which is transmitted through an IoT module.Tis paper presents an enhanced version of a fre detection system based on fuzzy logic.Te application of fuzzy logic is advantageous as it allows for the separation and analysis of information from multiple sensors, simplifying sensor data processing.
Te multisensor fuzzy logic approach for enhanced fre detection holds signifcant utility for smart cities due to its ability to address the unique challenges and requirements posed by the dynamic and complex urban environments.Te following provides key justifcations for the usefulness of this approach in smart cities.
1.1.Comprehensive Detection.Smart cities are characterized by diverse infrastructures, including residential, commercial, and industrial areas.Te integration of multiple sensors (smoke, fame, and temperature) in the fuzzy logic approach enables a comprehensive detection mechanism that considers various fre indicators simultaneously.Comprehensive detection improves the system's ability to identify and respond to fres of diferent origins and characteristics, ensuring a more efective and versatile fre detection system for the diverse settings within a smart city.
1.2.Adaptability to Dynamic Environments.Smart cities are dynamic, with changing environmental conditions, fuctuating population densities, and evolving infrastructures.Fuzzy logic is inherently adaptable and capable of dynamically adjusting to varying conditions and uncertainties in the urban landscape.Te adaptive nature of the fuzzy logic approach ensures that the fre detection system can respond efectively to real-time changes, reducing false alarms and enhancing the system's reliability in dynamic smart city environments.

Reduced False Alarms.
False alarms in smart cities can lead to unnecessary panic, resource wastage, and disruptions.Te multisensor fuzzy logic approach, by considering multiple parameters and employing nuanced decisionmaking, minimizes false alarms by diferentiating between normal variations and actual fre events.Reduction in false alarms enhances the efciency of emergency response teams, prevents unnecessary evacuations, and fosters public trust in the reliability of the fre detection system.

Localization and Rapid
Response.Smart cities often have densely populated areas and critical infrastructures.Te multisensor approach facilitates the localization of fre sources, allowing for targeted responses to mitigate the impact of the fre.Rapid response is critical for minimizing damage and ensuring public safety.By accurately determining the intensity of fres, the system can trigger timely alerts, coordinate emergency services, and streamline response eforts.

Integration with IoT and Smart
Infrastructure.Smart cities leverage the Internet of Tings (IoT) and interconnected devices.Te multisensor fuzzy logic approach aligns well with the principles of IoT, enabling seamless integration with other smart infrastructure components.Integration with IoT facilitates real-time data exchange, enables remote monitoring, and contributes to the overall synergy of smart city systems.Tus, the fre detection system is an integral part of the interconnected urban ecosystem.
Te multisensor fuzzy logic approach for enhanced fre detection is particularly advantageous for smart cities as it addresses the multifaceted challenges posed by dynamic environments, diverse infrastructures, and the need for reliable, adaptive, and accurate fre detection systems in urban settings.
Te following is a summary of this paper's structure.An overview of the literature survey and the signifcant contributions of this study are given in Section 1. 6 [11].In order to reliably identify and detect fre occurrences, several fre detection methods have been proposed in the literature.
Tese methods make use of diferent techniques and algorithms.Some of these methods examine fame geometry, including its position, rate of spread, length, and surface.For instance, a study by the authors in [12] classifed nonrefractory pixels using average intensity, while fre pixels were categorized using color and the presence of smoke.A real-time fre detector was created using a diferent method [13], incorporating color data with registered foreground and background frames.A novel fre color model based on image processing was proposed in [14] to detect fre using statistical measurements efciently.In addition, advanced object detection convolutional neural network (CNN) models were employed for image-based fre detection in [15], ofering robustness and real-time performance.In [16], vision-based indoor fre and smoke detection systems were created using tiny training datasets and photos with diferent pixel densities.Unmanned aerial vehicle (UAV) of computer vision-based fre detection was investigated in [17], with a focus on early identifcation and prevention of fre threats.
CNNs and smoke motion properties-based algorithms for recognizing fames were proposed in [18].Te use of static ELASTIC-YOLOv3 as a fre detection system for urban settings was also presented in [19].Te temporal and spatial dynamic fre textures were analyzed in [20] using 2D and 3D wavelet fragmentation.In addition, the authors in [21][22][23][24][25][26] discussed machine learning and deep learning methods for detecting forest fres.Tese studies highlight the diverse range of techniques and algorithms employed in fre detection research, showcasing advancements in accuracy, efciency, and real-time performance.Recent advancements in Internet of Tings (IoT) technology have revolutionized fre alarm systems, enabling accurate fre point detection and real-time monitoring.Tese techniques have a number of advantages, such as real-time tracking, online upgrades, and simple maintenance.Many nations have intensively explored research on wireless communication technology applications for preventing indoor fres.For instance, an interior fre monitoring system was described in a study [27] that used a ZigBee wireless network to track the temperature, humidity, and smoke concentration at the fre location in real time.
In [28], wireless sensor networks (WSNs) were used to gather precise environmental data, such as temperature, relative humidity, and diferent gas concentrations, and send it to base stations connected to the ground.Te study in [29] employs radial sector scanning using the UV sensor to enhance detection optimization up to 360 °, demonstrated through an embedded system.Te experiment reveals a 53% improvement in detection results, varying with sensor azimuth width and device height.Due to their minimal complexity, low power consumption, and low-cost advantages, LPWAN-based IoT surveillance systems were another suggested alternative [30].Te advantages of integrating IoT into fre detection systems have been emphasized in numerous research studies.For instance, the authors in [31] provided an evaluation of an IoT-based fre alarm navigation system and its uses.Te work in [32] describes the development of an early fre detection system that uses UAV and sensor network technology to stop fre occurrences.Opportunistic networks, spurred by the IoT, face storage congestion from handheld devices [33].Tis research develops an AI rule-based fre engine model, tested with advanced classifcation algorithms and implemented via the ONE simulator tool with the MaxProp protocol.By achieving a 98% accuracy through k-fold validation across six algorithms, signifcant improvements over MaxProp are observed.Te node-level delivery ratio rises by 20%, the bufer level by 5%, and the throughput increases by 500 kbps at the network level and by 150 kbps at the bufer level.Tese fndings highlight the efcacy of AI in addressing congestion challenges and enhancing performance in opportunistic networks.Delay tolerant networks (DTNs) are an evolving facet of wireless multihop networking within sensor networks research [34].Tese networks contend with intermittent connectivity and prolonged delays due to mobility.Tis study prioritizes custodian node selection based on storage capacity to reduce packet drops amid limited device storage.By introducing an intelligent history and a bufer-based approach, the study surpasses the MaxProp protocol in simulations, excelling across node, network, and bufer levels.In [35], a cloud-based fre detection method was proposed, utilizing features extracted from the IoT-captured video footage sent to the cloud instead of transmitting the entire video.Binary video descriptors and CNN were employed to develop the fre detection algorithm.In addition, a unique approach to wildfre detection using IoTnetworks with UAV support was presented in [36].Te study's main objective was to assess the efectiveness and dependability of UAV-IoTnetworks for wildfre detection and to provide recommendations for improving fre detection probability while keeping costs to a minimum.Tese research works highlight the signifcant contributions of IoT technologies in advancing fre detection systems, enabling enhanced monitoring, timely alerts, and improved efciency.
Furthermore, Table 1 provides a critical analysis to systematically outline some of the limitations identifed in the existing literature.1.7.Contributions.Fire detection systems play a vital role in safeguarding lives and properties by providing early warning of fre incidents.Traditional fre detection methods often rely on simple threshold-based approaches, which may not be efective in complex environments or situations involving uncertainties.In recent years, the integration of soft computing techniques in fre detection systems has emerged as a promising solution to enhance their performance and reliability.
(i) Particle swarm optimization, fuzzy logic, neural networks, evolutionary algorithms, and other soft computing methods provide efective tools for dealing with uncertainty, imprecision, and missing information.Tese techniques excel in dealing with complex systems and can adapt to dynamic environments.By incorporating soft computing techniques into fre detection systems, it becomes possible to improve these systems' accuracy, responsiveness, and robustness.(ii) Tis research aims to develop an intelligent fre detection system that utilizes soft computing techniques to detect and respond to fre incidents efectively.Te suggested system integrates several sensors, including smoke, fame, and temperature, to capture various elements of fre behavior and characteristics.Tese sensor inputs are then processed using advanced soft computing algorithms to accurately identify and distinguish fre occurrences from false alarms or other environmental factors.(iii) Soft computing techniques enable the system to handle uncertainties associated with fre detection, such as varying environmental conditions, sensor noise, and partial occlusions.Te system can dynamically adapt its decision-making process based on real-time sensor data and historical patterns, thereby enhancing its performance and reducing false alarms.(iv) Furthermore, the intelligent fre detection system incorporates connectivity features, allowing it to communicate with external entities such as consumers and emergency services.In a fre, the system can promptly send alert messages, including the precise location coordinates, to designated recipients through internet-based communication or GSM modems.Tis enables quick response and facilitates timely evacuation, minimizing potential risks and damages.(v) Te research presented in this paper focuses on developing and evaluating an intelligent fre detection system using soft computing techniques.
Trough experimental analysis and validation, the efectiveness and efciency of the proposed system will be assessed, highlighting its potential contributions to fre safety in various applications, including smart buildings, industrial facilities, and public spaces.
Overall, integrating soft computing techniques in fre detection systems represents a signifcant advancement in enhancing these systems' accuracy, adaptability, and responsiveness.Te intelligent fre detection system proposed in this research aims to leverage the benefts of soft computing techniques to provide robust and efcient fre detection capabilities, thereby contributing to improved fre safety standards in diverse settings.

Materials and Methods
Te efciency of the fre detection system is based on the values of the sensors obtained.Te usage of the multiplesensor system has its advantages when compared to the single-sensor-based fre alarm system.Te fuzzy logic system shown in Figure 1 is used because it disentangles the combination of the information from various sensors, making it less demanding to break down the sensor information.We can classify the temperature range into cool, mild, and hot during the fuzzifcation [37].In the fuzzy interface engine, the IF-THEN rule is used to make a decision.Te fuzzy values are then defuzzifed into an output.Te fuzzy sets of each sensor are created using triangular functions [37].
Te density of the smoke is divided into three membership sets: low, high, and medium, as shown in Figure 2. Te smoke sensor MQ2 collects the data in analog voltage forms.Te Arduino board consists of an analog to digital converter, and the values are in the range from 0 to 1023.Te temperature sensor (LM35) readings have been divided into three membership sets: low, medium, and high, as shown in Figure 3. Te range that the sensor can measure is from 16 to 110c. Figure 4 shows that the membership sets of the readings of the fame sensor are also divided into three sets: 1: low, 2: medium, and 3: high.Te fame sensor collects the data in the analog form.Since the Arduino board consists of the analog to digital converter, the range of values is from 0 to 1023.
Te output variables of fuzzy sets are divided into three sets.Tey are as follows: (1) no fre, (2) fre detected, and (3) potential fre.To determine the fre status, these fuzzy sets are used.Tese inputs are fuzzifed using a rule base (IF-THEN).Te system architecture of the device and its subsystems are shown in Figure 5. Te device consists of three sensors, smoke, fame, and temperature, to detect the fre.Te Arduino board, GSM modem, and IoT module are attached.Te fre alert will be sent through an SMS, and it will be updated in the web portal.Te primary goal of this fre detection system is to provide real-time notifcations to individuals when a fre is detected.Te capacity of the fre detection system to give quick input on the fre's status is a noteworthy feature.Once a fre is discovered, this quick 4 Journal of Optimization response is essential.Te device's alarm makes sure that fre events are acknowledged acoustically and that pertinent parties are notifed remotely.A simple fre outbreak signal would have been sufcient, but using the device to visualize the fre's location and provide useful information about the aficted structure adds more context.Smoke density, ambient temperature, and fame intensity are crucial parameters in fre detection systems, as they provide diverse and complementary information that helps in accurately identifying and responding to potential fre incidents [32].Smoke is a byproduct of combustion, and an increase in smoke density often indicates the presence of a fre.Monitoring smoke levels is fundamental to early fre detection.Monitoring ambient temperature helps identify abnormal increases in heat associated with fre.Rapid temperature rises are indicative of fre initiation or progression.Flames are a direct and visible confrmation of a fre event.Monitoring fame intensity provides an immediate indication of the severity of the fre.By combining information from smoke density, ambient temperature, and fame intensity, a multisensor system, especially one driven by fuzzy logic, can make informed decisions, reduce false alarms, and enhance the overall efectiveness of fre detection.Tis comprehensive approach is particularly valuable in smart city environments where diverse conditions and potential sources of fres need to be considered.
Te pseudocode and algorithm for the multisensor fuzzy logic approach for enhanced fre detection in smart cities are discussed as follows (see, Algorithm 1).

Pseudocode for Multisensor Fuzzy Logic Approach. 2.2. Algorithm for Multisensor Fuzzy Logic Approach
(i) Initializing sensors: the values from smoke, fame, and temperature sensors are read (ii) Fuzzifcation: fuzzifcation functions are applied to convert sensor values into fuzzy sets (iii) Rule evaluation: fuzzy logic rules (IF-THEN statements) are used to evaluate the degree of activation for each rule based on fuzzy sets (iv) Aggregation: the rule activations are combined to determine the overall degree of activation (v) Defuzzifcation: the aggregated fuzzy output is then converted back to a crisp value, representing the fre intensity (vi) Decision-making: if the fre intensity is classifed as high, the alarm is triggered, the authorities are notifed, and the online portal is updated

Implementation
An overview of the fre detection system is shown in Figure 6.Te fgure shows that all the sensors are interfaced with the Arduino board and the GSM, GPS, and IoT modules.Te fuzzy logic system is present in MATLAB.MATLAB consists of the fuzzy tool kit where we can implement the rules and design the system.Te embedded Fuzzy Logic Library (eFLL) fles are used for implementing the fuzzy logic system in the Arduino board.Te fow depicts the operation of the fre detection system.Tere are the following three cases for the output: (1) no fre, (2) potential fre, and (3) fre detected.No fre alerts will be sent to the resident during the frst two cases, and during the third case, alerts through SMS and the fre status will be updated in the web portal.During the potential fre output, the buzzer will be activated to notify the people in that area during a fre outbreak.Te fowchart of the fre detection system is illustrated in Figure 6, depicting the sequential steps involved in the detection process.Furthermore, Figure 7 showcases the hardware implementation of the fre detection system, providing a visual representation of the physical components utilized.
In this work, the embedded Fuzzy Logic Library (eFLL) embedded in Arduino is likely utilized to implement a fuzzy logic system for fre detection using multiple sensors.Te following gives a detailed description of how the eFLL fle embedded in Arduino can be used in this context.

Library Inclusion.
Te frst step is to include the eFLL library in the Arduino sketch.Tis is typically performed by adding an "#include" directive at the beginning of the sketch to import the necessary header fles from the eFLL library.#include <eFLL.h>3.2.Initialization.After including the library, the fuzzy logic system needs to be initialized.Tis involves setting up fuzzy variables, linguistic terms, fuzzy sets, and fuzzy rules that defne the behavior of the system.Tis initialization is typically performed in the "setup()" function of the Arduino sketch.void setup() { //Initialize fuzzy logic system //Defne input/output variables, linguistic terms, fuzzy sets, and rules //Initialize sensors } 3.3.Sensor Readings.In the "loop()" function of the Arduino sketch, the sensors are read to collect data about the environment, such as temperature, smoke density, or other relevant parameters.Tese sensor readings will serve as inputs to the fuzzy logic system.void loop() { Journal of Optimization //Read sensor data foat temperature � readTemperatureSensor(); foat smokeDensity � readSmokeDensitySensor(); //Pass sensor data to fuzzy logic system //Perform fuzzy inference //Take appropriate action based on fuzzy logic output } 3.4.Fuzzy Inference.Within the "loop()" function, the sensor data are passed to the fuzzy logic system for inference.Te fuzzy logic system evaluates the inputs based on the predefned linguistic terms, fuzzy sets, and rules to determine the degree of fre risk or the likelihood of a fre event.
void loop() { //Read sensor data //Pass sensor data to fuzzy logic system foat freRisk � fuzzyLogicInference(temperature, smokeDensity); //Take appropriate action based on fuzzy logic output } 3.5.Output Processing.Depending on the output of the fuzzy logic system (e.g., the degree of fre risk), appropriate actions are taken.Tis could include activating alarms, notifying authorities, or triggering fre suppression systems.void loop() { //Read sensor data //Pass sensor data to fuzzy logic system //Perform fuzzy inference //Take appropriate action based on fuzzy logic output if (freRisk > threshold) { activateAlarm(); } } 3.6.Further Customization.Te eFLL library provides various functions and methods for customizing the fuzzy logic system, including adjusting linguistic terms, fuzzy sets, rules, and membership functions.Tis allows the system to be tailored to specifc application requirements and environmental conditions.
By following these steps and leveraging the functionalities provided by the eFLL library, the Arduino board can efectively implement a fuzzy logic-based fre detection system using multiple sensors.
Table 2 presents details about general-purpose input/ output (GPIO), which is a set of pins on a microcontroller or microprocessor that can be confgured to serve as either input or output.Tese pins are versatile and can be used for a variety of purposes in electronic circuits and embedded systems.As inputs, GPIO pins can be used to read digital signals from sensors, switches, or other devices.As outputs, they can be used to send digital signals to control actuators, LEDs, relays, or other output devices.Te confguration of a GPIO pin is typically programmable, allowing developers to dynamically set its behavior based on the requirements of the application.In the context of embedded systems and microcontrollers, GPIO plays a crucial role in interfacing with the external world, enabling communication between the microcontroller and various sensors, actuators, and other peripheral devices.Te fexibility of GPIO pins makes them fundamental building blocks in designing and prototyping electronic systems.

Simulation Results
Te system is tested in a closed room.Trough the introduction of controlled experiments involving a fame (using a candle), smoke (burning paper), and heat (using a hair dryer), the obtained results demonstrate that the system signifcantly improves the reliability of fre detection as shown in Figure 8. Te system successfully sends fre alerts to the residents, and the fre status is updated at regular intervals of 45 seconds, triggered by the presence of a specifc percentage of smoke and fame as depicted in Figure 9. Tese fndings afrm the system's efectiveness in enhancing the accuracy and responsiveness of fre detection capabilities.If the probability of the fre is less than 0.5, then the status shown is potential fre; if it is more than 0.5, the status shown is fre detected.An SMS will be sent to the mobile number and the fre department with the coordinates of latitude and longitude as shown in Figure 10.Te web portal can be accessed from anywhere to know the fre status.Tere are a series of screenshots sent by the device during the fre alert situations.
Table 3 presents user-size information for data collection and disseminating alerts.Tis table categorizes user size into three categories: small, medium, and large, each with a brief description of the corresponding user base.It outlines different methods for data collection and alert dissemination tailored to each category.For instance, small user sizes may utilize surveys and manual monitoring, while large user bases could beneft from real-time sensor networks and continuous monitoring.Te table aims to guide the selection of appropriate strategies based on the scale of the user population, providing fexibility and adaptability in data collection and alerting mechanisms.
Te multisensor fuzzy logic approach for enhanced fre detection in smart cities holds several advantages over classical approaches.Fuzzy logic excels in handling uncertainty and imprecision, which are common in dynamic smart city environments.It allows for a more nuanced interpretation of sensor data, accommodating variations in fre characteristics that may not be well-defned by rigid rules.Integrating multiple sensors, such as those measuring smoke, fame, and temperature, allows for a more comprehensive analysis of fre indicators.Fuzzy logic can effectively combine information from diferent sensors, providing a holistic view that enhances detection accuracy.Fuzzy logic employs rule-based decision-making, allowing for dynamic adjustments based on changing environmental conditions.Te system can adapt its responses to evolving fre scenarios, leading to improved responsiveness.Fuzzy logic's ability to model complex relationships and incorporate multiple parameters reduces the likelihood of false alarms.It provides a more nuanced evaluation of sensor inputs, distinguishing between actual fre events and benign fuctuations.In summary, the fuzzy logic approach, especially when applied to a multisensor system, has the potential to outperform classical techniques by providing a more adaptive and nuanced approach to fre detection, potentially leading to higher detection rates (DRs), lower false alarm   rates (FARs), and improved overall accuracy rate (AR) in smart city environments as presented in Table 4.In the table, the efcacy of the proposed approach is compared with that of the random forest algorithm [27], gradient boosting algorithm [30], and image processing approach [32].Te parameters DR, FAR, and AR are evaluated according to the following expressions: where TP and FN indicate true positives (the number of correctly detected fre instances) and false negatives (the number of missed fre instances), respectively.
where FP and TN indicate false positives (the number of nonfre instances incorrectly identifed as fres) and true negatives (the number of correctly identifed nonfre instances), respectively.

Conclusion
An intelligent fre detection system using a soft computing technique is successfully designed and tested.Intelligent systems are at the forefront of fre detection and alarm technology, representing the latest advancements in this feld.Tese intelligent systems, as opposed to conventional alarm systems, use microprocessors and system software to monitor and regulate the operations of each alarm and signaling device.Each intelligent fre alarm device essentially performs the same responsibilities as a small computer, supervising and controlling a variety of input and output devices to ensure efective functioning and improved fre protection measures.Tree smoke, fame, and temperature sensors are included in the fre detection system to help in fre detection and to help deliver accurate information to  Te SMS is sent using the GPS and GSM modem.When a fre is discovered, an SMS containing the latitude and longitude coordinates is sent to the resident and the fre rescue service.Te status concerning the fre is updated every 45 seconds on the online portal with the assistance of IoT.Te information regarding the fre will be updated on the web page from where we can fnd the data regarding the intensity of the fre.Te latitude and longitude coordinates would be helpful for the fre department to reach that exact place within less time.

Figure 4 :
Figure 4: Te membership sets of the fame values.

Figure 7 :
Figure 7: Hardware implementation of the system.

Figure 8 :
Figure 8: Screenshot for the fre status on the web page.

Figure 9 :
Figure 9: Location of the fre detected place.

Figure 10 :
Figure 10: Screenshot of SMS sent by the GSM modem.

Table 1 :
Critical analysis table: limitations in existing studies.
Pseudocode for the multisensor fuzzy logic approach.
Figure 6: Flowchart of the fre detection system.

Table 2 :
GPIO vs devices/components design in the proposed prototype.

Table 3 :
User size for data collection and disseminating alerts.
Small Limited user base, e.g., <100 users Surveys/questionnaires and manual monitoring E-mail notifcations and SMS alerts Medium Moderate user base, e.g., 100-500 users Automated data logging and periodic sampling Mobile applications and push notifcations Large Large user base, e.g., >500 users Real-time sensor networks and continuous monitoring Web portals and social media notifcations

Table 4 :
Comparison of diferent methods.By introducing fame (a matchstick), smoke (a paper coil burning), and temperature (a fashlight), an experimental study is obtained.Information about the situation is relayed to remote areas using GSM connectivity.