Design and Application of Fuzzy Logic Based Fire Monitoring and Warning Systems for Smart Buildings

Sarwar, Barera; Bajwa, Imran Sarwar; Ramzan, Shabana; Ramzan, Bushra; Kausar, Mubeen

doi:10.3390/sym10110615

Cited by 40 publications

(25 citation statements)

References 16 publications

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“…With respect to the publication year, 63% of the identified articles were published during the last 5 years. The authors of these scientific articles made use in their analyses of different types of sensors, including sensors and actuators related to the primary heating circuits and power generation systems [24]; telecare medicine information systems (TMIS) comprising specialized sensors that provide key health data parameters [99]; distributed sensors [100]; temperature, humidity and flame sensors [101]; string-type strain gauges [49]; temperature and occupancy sensors [54]; wireless sensors [47,102]; environment sensors for measuring indoor illuminance, temperature-humidity, carbon dioxide concentration and outdoor rain and wind direction [103]; sensors for measuring the indoor and outdoor temperature and the humidity [39]; vision sensors [55]; sensor networks [56,104]; binary infrared sensors [83]; motion detectors, light sensors, meteorological sensors for the wind and solar radiation data [105]; light and motion sensors [106]; environmental sensors [107]; in-house and city sensors [108]; meteorological stations [46]; smart home sensors, remote monitoring systems, and data and video review systems [102]; temperature and infrared sensors [109]; temperature sensors [110]; inside and outside home sensors [111]; different sensors and effectors [112]; smart systems for controlling the vibration of building structures by means of smart dampers [113]; virtual sensor based on a fisheye video camera [48]; and indoor and outdoor light sensors [114]. In these papers, the reasons for using the Fuzzy C-Means with the sensor devices in smart buildings were mainly related to monitoring and controlling energy management processes [24,39,46,47,…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…The performance metrics considered in the scientific papers that use the Fuzzy C-Means integrated with sensor devices in smart buildings were evaluated based on experiments and simulations [46,47,103,[107][108][109]111,114]; Root Mean Square Error (RMSE) [24]; computational cost, user anonymity, mutual authentication, off-line password guessing attacks, impersonation attacks, replay attacks, and the assurance of formal security [99]; Inaccuracy Rate, experiment environment dimension and Root-Mean-Square Error (RMSE), and the dependency of the localization approach on the number of wireless nodes (topology) employed to locate the objects [100]; Accuracy [101,110]; Coefficient of Determination (R 2 ) [49]; energy consumption, Electricity Cost, Peak-to-Average Ratio (PAR) [54]; energy saving percentage in different working scenarios [39]; Standard Error of Mean (SEM), Horizontal Illuminance, Daylight Glare Probability, paper-based Landolt test, Freiburg Visual Acuity Test (FrACT), Electric Lighting Energy Consumption, total number of shading and lighting commands [55]; turbulence intensity, draught rates, operative temperature, Predicted Mean Vote (PMV) and Percentage of People Dissatisfied (PPD) [56]; Identification Rate [83]; Energy Consumption and illumination level [105]; energy savings [106]; Detection Accuracy, Energy Consumption, Memory Consumption, Processing Time Estimation [104]; True Positive, False Positive, True Negative, False Negative, and Accuracy [102]; Accuracy and a comparison with the results presented in related works (based on Ultrasonic, Ultrasonic/RFID, ZigBee, Active RFID, Passive RFID) [112]; Fault Detection Index values for certain fault magnitudes, residual values for individual sensors corresponding to different fault magnitudes [113]; and comfort level [48].…”

Section: Unsupervised Learningmentioning

confidence: 99%

“…In their paper [101], Sarwar et al presented a Fire Monitoring and Warning System (FMWS), developed based on a Fuzzy Logic approach, that was designed to detect the actual existence of fire and to send alarms to a system providing a complete infrastructure for fire safety management, namely, the Fire Management System (FMS), using the Global System for Mobile (GSM) Communication technology. The authors made use of temperature, humidity and flame sensors in their study.…”

Section: Unsupervised Learningmentioning

confidence: 99%

See 2 more Smart Citations

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

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Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

Section: Unsupervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

et al. 2019

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“…In our proposed system, we use the centroid defuzzification method developed by Sugeno in 1985. is method is most widely used by researchers for defuzzification of generated fuzzy output. is technique is expressed mathematically as [21] x * � μi(x)x dx…”

Section: (A) Center Of Gravity (B) Center Of Gravity For Singletons (mentioning

confidence: 99%

Smart Wound Hydration Monitoring Using Biosensors and Fuzzy Inference System

Sattar

Bajwa

Amin

et al. 2019

Wireless Communications and Mobile Computing

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Skin wounds either minor or chronic may heal up with different time durations. But, this time duration of healing could not be easily predicted as healing is affected by different factors, e.g., age, nutrition, medication, and surroundings. Despite these factors, wound characteristic also plays a role in the healing process. Wound characteristics include wound size, wound type, internal and external wound environment, body temperature, body oxygenation, wound hydration, and infection. erefore, monitoring of wound healing also required careful consideration of wound characteristics. Although the healthcare domain contains many applications for detection and monitoring of diseases, the wound care domain requires efficient techniques and sensing systems for the identification of wound biomarkers such as temperature, blood pressure, oxygen, and infection status of wound using biosensors. In the current research, we provide a wound care solution based on a biosensor-based sensing system to measure basic biomarkers, considered as major wound characteristics, i.e., body temperature and body oxygenation, and design a fuzzy inference system to predict their effect on wound hydration, which ultimately recommends necessary actions to boost healing.

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“…Design of a fire detection algorithm based on Fuzzy Logic, using temperature sensor to estimate the direction of fire disaster is carried out [12] While the sensor nodes used has the ability to periodically collect ambient temperature, communicate with neighbor sensors, and store the neighbors' information such as Nodes' ID, and the Nodes' coordinate, Temperature sensor is not enough to detect fire outbreak. Sensor node could not know their exact location in the grid.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine-Based Fire Outbreak Detection System

Umoh¹,

Udo²,

Emmanuel³

2019

IJSCAI

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This study employed Support Vector Machine (SVM) in the classification and prediction of fire outbreak based on fire outbreak dataset captured from the Fire Outbreak Data Capture Device (FODCD). The fire outbreak data capture device (FODCD) used was developed to capture environmental parameters values used in this work. The FODCD device comprised DHT11 temperature sensor, MQ-2 smoke sensor, LM393 Flame sensor, and ESP8266 Wi-Fi module, connected to Arduino nano v3.0.board. 700 data point were captured using the FODCD device, with 60% of the dataset used for training while 20% was used for testing and validation respectively. The SVM model was evaluated using the True Positive Rate (TPR), False Positive Rate (FPR), Accuracy, Error Rate (ER), Precision, and Recall performance metrics. The performance results show that the SVM algorithm can predict cases of fire outbreak with an accuracy of 80% and a minimal error rate of 0.2%. This system was able to predict cases of fire outbreak with a higher degree of accuracy. It is indicated that the use of sensors to capture real world dataset, and machine learning algorithm such as support vector machine gives a better result to the problem of fire management.

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Design and Application of Fuzzy Logic Based Fire Monitoring and Warning Systems for Smart Buildings

Cited by 40 publications

References 16 publications

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Smart Wound Hydration Monitoring Using Biosensors and Fuzzy Inference System

Support Vector Machine-Based Fire Outbreak Detection System

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