Machine Learning-Based Multi-Sensor IoT System for Intelligent Indoor Fire Detection
Abstract
Purpose of the study: This study aims to develop an intelligent indoor fire detection system by integrating low-cost Internet of Things (IoT) sensors with machine learning-based multi-sensor data fusion to improve early fire hazard detection accuracy while reducing false alarms compared to conventional single-sensor fire detection systems.
Methodology: The system is implemented using an ESP32 microcontroller connected to temperature, humidity, flame, and sound sensors for real-time data acquisition. A dataset of 1,500 sensor samples is collected and labeled into Normal, Fire-Risk, and Fire classes. Decision Tree, Support Vector Machine, and Random Forest classifiers are trained and evaluated using Python-based machine learning libraries.
Main Findings: Experimental results indicate that the Random Forest model outperforms the other classifiers, achieving 95% overall accuracy, perfect recall for fire events, and a Macro ROC-AUC score of 0.993. Feature importance analysis reveals that humidity and temperature are the most influential parameters for early fire detection in indoor environments.
Novelty/Originality of this study: This study proposes a lightweight intelligent fire detection framework that integrates multi-sensor Internet of Things data including temperature, humidity, flame, and sound signals with machine learning–based classification for indoor environments. Unlike conventional systems that rely on single-sensor or threshold-based detection, the proposed approach utilizes multi-sensor data fusion and ensemble learning to improve early fire-risk identification while remaining computationally efficient for low-cost platforms such as the ESP32 microcontroller.
References
S. Medved, “Buildings fires and fire safety,” in Building Physics: Heat, Ventilation, Moisture, Light, Sound, Fire, and Urban Microclimate. Cham, Switzerland: Springer, 2021, pp. 407–451, doi: 10.1007/978-3-030-74390-1_6.
V. Kodur, et al., “Fire hazard in buildings: Review, assessment and strategies for improving fire safety,” PSU Res. Rev., vol. 4, no. 1, pp. 1–23, 2020, doi: 10.1108/PRR-12-2018-0033.
M. Yu, et al., “Building fire alarm model based on fire source inversion according to smoke arrival time intervals,” J. Build. Eng., vol. 73, Art. no. 106650, 2023, doi: 10.1016/j.jobe.2023.106650.
R. Kuti, G. Zólyomi, G. László, C. Hajdu, L. Környei, and F. Hajdu, “Examination of effects of indoor fires on building structures and people,” Heliyon, vol. 9, no. 1, Art. no. e12720, Jan. 2023, doi: 10.1016/j.heliyon.2022.e12720.
M. L. Ivanov and W. K. Chow, “Fire safety in modern indoor and built environment,” Indoor Built Environ., vol. 32, no. 1, pp. 3–8, Jan. 2023, doi: 10.1177/1420326X221134765.
M. T. Bashir, “Fire protection and prevention in perspective of human, environment and workplace,” Int. J. Sci. Technol. Res., vol. 10, no. 3, pp. 38–42, Mar. 2021. [Online]. Available: https://web.archive.org/web/20210814084738/http://www.ijstr.org/final-print/mar2021/Fire-Protection-And-Prevention-In-Perspective-Of-Human-Environment-And-Workplace.pdf
L. Deng, et al., “Large-space fire detection technology: A review of conventional detector limitations and image-based target detection techniques,” Fire, vol. 8, no. 9, Art. no. 358, 2025, doi: 10.3390/fire8090358.
G. Tejaswi, R. Bhavani, S. Srihitha, S. Arshiha, and R. V. S. Sarayu, “Predicting fire alarms using multi-sensor data: A binary classification approach,” Turkish J. Comput. Math. Educ., vol. 15, no. 1, pp. 242–255, 2024, doi: 10.61841/turcomat.v15i1.14617.
C. L. Wu, et al., “False-alarm susceptibility of spot-type smoke detectors under realistic fire and nuisance conditions,” Fire Saf. J., Art. no. 104621, 2025, doi: 10.1016/j.firesaf.2025.104621.
S. Chen, J. Ren, Y. Yan, M. Sun, F. Hu, and H. Zhao, “Multi-sourced sensing and support vector machine classification for effective detection of fire hazard in early stage,” Comput. Electr. Eng., vol. 101, Art. no. 108046, 2022, doi: 10.1016/j.compeleceng.2022.108046.
J. Baek, et al., “Intelligent multi-sensor detection system for monitoring indoor building fires,” IEEE Sensors J., vol. 21, no. 24, pp. 27982–27992, Dec. 2021, doi: 10.1109/JSEN.2021.3124266.
H. Zhu, et al., “Automotive fire alarm system based on multi-sensor fusion,” in Proc. Int. Conf. Sensors Inf. Technol., 2024, pp. 159–164, doi: 10.1117/12.3029223.
A. Solórzano, et al., “Early fire detection based on gas sensor arrays: Multivariate calibration and validation,” Sens. Actuators B, Chem., vol. 352, Art. no. 130961, 2022, doi: 10.1016/j.snb.2021.130961.
Q. Su, G. Hu, and Z. Liu, “Research on fire detection method of complex space based on multi-sensor data fusion,” Meas. Sci. Technol., vol. 35, no. 8, Art. no. 085107, 2024, doi: 10.1088/1361-6501/ad437d.
A. Rehman, et al., “Smart fire detection and deterrent system for human savior by using Internet of Things (IoT),” Energies, vol. 14, no. 17, Art. no. 5500, 2021, doi: 10.3390/en14175500.
S. K. Mekni, “Design and implementation of a smart fire detection and monitoring system based on IoT,” in Proc. Int. Conf. Appl. Autom. Ind. Diagn., 2022, pp. 1–5, doi: 10.1109/ICAAID51067.2022.9799505.
B. E. Raju, K. R. Chandra, K. V. N. Gupta, K. N. V. Rao, R. Devi, and P. V. Kumar, “Fuzzy logic-enhanced multi-sensor hardware module for real-time fire detection and notification,” in Proc. 7th Int. Conf. Contemp. Comput. Inform. (IC3I), 2024, pp. 1–6, doi: 10.1109/IC3I61595.2024.10828880.
A. Al-Dahoud, M. Fezari, A. A. Alkhatib, M. N. Soltani, and A. Al-Dahoud, “Forest fire detection system based on low-cost wireless sensor network and Internet of Things,” WSEAS Trans. Environ. Dev., vol. 19, pp. 506–513, 2023, doi: 10.37394/232015.2023.19.49.
A. A. Almohammedi, et al., “Design and implementation of IoT-enabled intelligent fire detection system using neural networks,” in Lecture Notes in Computer Science, vol. 14202, 2023, doi: 10.1007/978-3-031-45140-9_6.
U. Dampage, et al., “Forest fire detection system using wireless sensor networks and machine learning,” Sci. Rep., vol. 12, Art. no. 46, 2022, doi: 10.1038/s41598-021-03882-9.
././// J. Desikan, et al., “Hybrid machine learning-based fault-tolerant sensor data fusion and anomaly detection for fire risk mitigation in IIoT environment,” Sensors, vol. 25, no. 7, Art. no. 2146, 2025, doi: 10.3390/s25072146.
J. C. N. Bittencourt, D. G. Costa, P. Portugal, and F. Vasques, “Towards lightweight fire detection at the extreme edge based on decision trees,” in Proc. IEEE 22nd Mediterranean Electrotech. Conf. (MELECON), Jun. 2024, pp. 873–878, doi: 10.1109/MELECON56669.2024.10608598.
F. Khan, et al., “Recent advances in sensors for fire detection,” Sensors, vol. 22, no. 9, Art. no. 3310, 2022, doi: 10.3390/s22093310.
A. Doshi and Y. Rai, “IoT-based fire and gas monitoring system,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 7, pp. 3110–3117, 2021, doi: 10.22214/ijraset.2021.37026.
M. N. Khan, et al., “Real-time environmental monitoring using low-cost sensors in smart cities with IoT,” Int. J. Future Mach. Res., vol. 6, pp. 1–11, 2024, doi: 10.36948/ijfmr.2024.v06i01.23163.
R. F. Chisab, A. A. Majeed, and H. S. Hamid, “IoT-based smart wireless communication system for electronic monitoring of environmental parameters with a data-logger,” Int. J. Electr. Electron. Eng. Telecommun., vol. 12, no. 6, pp. 450–458, 2023, doi: 10.18178/ijeetc.12.6.450-458.
S. Chitram, et al., “Enhancing fire and smoke detection using deep learning techniques,” Eng. Proc., vol. 62, no. 1, Art. no. 7, 2024, doi: 10.3390/engproc2024062007.
J. Pincott, et al., “Indoor fire detection utilizing computer vision-based strategies,” J. Build. Eng., vol. 61, Art. no. 105154, 2022, doi: 10.1016/j.jobe.2022.105154.
A. Gaur, et al., “Video flame and smoke based fire detection algorithms: A literature review,” Fire Technol., vol. 56, pp. 1943–1980, 2020, doi: 10.1007/s10694-020-00986-y.
S. Suklabaidya and I. Das, “Processing IoT sensor fire dataset using machine learning techniques,” in Proc. Int. Conf. Intell. Syst. Adv. Comput. Commun. (ISACC), Feb. 2023, pp. 1–7, doi: 10.1109/ISACC56298.2023.10084317.
A. Secilmis, N. Aksu, F. A. Dael, I. Shayea, and A. A. El-Saleh, “Machine learning-based fire detection: A comprehensive review and evaluation of classification models,” JOIV: Int. J. Inform. Visualization, vol. 7, no. 3-2, pp. 1982–1988, 2023, doi: 10.30630/joiv.7.3-2.2332.
U. Ahad, Y. Singh, P. Anand, Z. A. Sheikh, and P. K. Singh, “Intrusion detection system model for IoT networks using ensemble learning,” J. Interconnection Netw., vol. 22, no. 3, Art. no. 2145008, 2022, doi: 10.1142/S0219265921450080.
J. Jose and J. E. Judith, “Unveiling the IoT’s dark corners: Anomaly detection enhanced by ensemble modelling,” Automatika, vol. 65, no. 2, pp. 584–596, 2024, doi: 10.1080/00051144.2024.2304369.
R. A. Khan, et al., “Fire and smoke detection using capsule network,” Fire Technol., vol. 59, pp. 581–594, 2023, doi: 10.1007/s10694-022-01352-w.
F. Daghero, et al., “Dynamic decision tree ensembles for energy-efficient inference on IoT edge nodes,” IEEE Internet Things J., vol. 11, no. 1, pp. 742–757, 2024, doi: 10.1109/JIOT.2023.3286276.
H. Wang, J. Li, and K. He, “Hierarchical ensemble reduction and learning for resource-constrained computing,” ACM Trans. Des. Autom. Electron. Syst., vol. 25, no. 1, pp. 1–21, 2019, doi: 10.1145/3365224.
S. Uppal, S. Raheja, and N. R. Das, “Fire detection alarm system using deep learning,” in Proc. 13th Int. Conf. Cloud Comput. Data Sci. Eng. (Confluence), Jan. 2023, pp. 54–58, doi: 10.1109/Confluence56041.2023.10048842.
A. Hassan and A. I. Audu, “A lightweight CNN model for vision-based fire detection on embedded systems,” FUOYE J. Eng. Technol., vol. 9, no. 4, pp. 624–628, 2024, doi: 10.4314/fuoyejet.v9i4.9.
S. Sawant, B. Chauhan, S. Kumbhar, G. Chaudhari, and P. Thakar, “Integrated fire detection system using machine learning and IoT,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 12, no. 5, pp. 2091–2100, 2024, doi: 10.22214/ijraset.2024.60063.
M. Milli and M. Milli, “Reducing false positives in building fire detection systems via multiple metal oxide sensors,” IEEE Access, early access, 2025, doi: 10.1109/ACCESS.2025.3606584.
,.... N. Dilshad, T. Khan, and J. Song, “Efficient deep learning framework for fire detection in complex surveillance environment,” Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 749–764, 2023, doi: 10.32604/csse.2023.034475.
A. K. Vishwakarma and M. Deshmukh, “CNNM-FDI: Novel convolutional neural network model for fire detection in images,” IETE J. Res., vol. 71, no. 4, pp. 1105–1118, 2025, doi: 10.1080/03772063.2025.2453877.
N. D. Ismail, et al., “A systematic literature review of vision-based fire detection, prediction and forecasting,” J. Kejuruteraan, vol. 37, no. 1, pp. 191–218, 2025, doi: 10.17576/jkukm-2025-37(1)-13.
M. S. Mohammed, A. H. Abbas, and N. A. Abdullah, “Intelligent surveillance systems for fire detection in open areas: A survey,” Iraqi J. Sci., vol. 65, no. 5, pp. 2813–2827, 2024, doi: 10.24996/ijs.2024.65.5.36.
F. A. Nafis, et al., “An efficient IoT-based fire detection system using quantized deep learning model on resource-constrained devices,” in Proc. 27th Int. Conf. Comput. Inf. Technol. (ICCIT), Dec. 2024, pp. 3182–3187, doi: 10.1109/ICCIT64611.2024.11021729.
H. L. Farhan and A. S. Daghal, “Improving the detection and warning fire system on the smart campus area using ANFIS,” Al-Furat J. Innov. Electron. Comput. Eng., vol. 3, no. 2, pp. 422–436, 2024, doi: 10.46649/fjiece.v3.2.28a.7.6.2024.
H. T. Thai, N. Y. Tran-Van, K. H. Le-Minh, and K. H. Le, “An edge-based fire detection system for real-time IoT applications,” in Proc. IEEE Int. Conf. Commun. Netw. Satell. (COMNETSAT), Nov. 2023, pp. 646–651, doi: 10.1109/COMNETSAT59769.2023.10420588.
D. Kaliyev, O. Shvets, S. Grigoryeva, and A. Alimkhanova, “Intelligent forest fire detection using CNN and UAVs,” in Proc. IEEE 6th Int. Symp. Logist. Ind. Inform. (LINDI), Oct. 2024, pp. 131–134, doi: 10.1109/LINDI63813.2024.10820427.
R. D. Shirwaikar and L. Mathews, “CNN-based video surveillance for fire and localization detection,” in Proc. Int. Conf. Cogn. Comput. Inf. Process. (CCIP), Dec. 2022, pp. 1–8, doi: 10.1109/CCIP57447.2022.10058625.
S. Khirani, A. Souahlia, A. Rabehi, et al., “Advanced evaluation of pre-trained CNN models for accurate forest fire detection,” Nat. Hazards, vol. 122, Art. no. 234, 2026, doi: 10.1007/s11069-026-07976-3.
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