IoT Enabled Real Time Fire Monitoring and Response in Urban Areas
Keywords:
IoT, Deep learning, Fire detection, Urban safety, Real-time monitoringAbstract
Safety and mitigating potential damage. This study investigates an Internet of Things (IoT)- enabled framework designed for real-time fire monitoring and response, utilizing sophisticated deep learning methodologies. The paper examines the integration of intelligent sensors that continuously gather environmental data, encompassing temperature, smoke, and gas. Levels to promote early fire detection. The proposed system utilizes a deep learning model to accurately classify and predict fire incidents, significantly improving Response Time (RT)s. Furthermore, the architecture incorporates communication protocols that facilitate rapid data transmission to emergency response teams and urban management systems, ensuring timely intervention. Our approach underscores the significance of data fusion from many IoT devices, which enhances situational awareness and decision-making processes during fire emergencies. By addressing critical challenges such as scalability, interoperability, and the reduction of false alarms, this research provides a holistic solution for urban fire safety, ultimately advancing smarter and safer cities.
References
Ivanov, M. L., & Chow, W. K. (2023). Fire safety in modern indoor and built environment. Indoor and Built Environment, 32(1), 3-8. https://doi.org/10.1177/1420326X2211347
Khan, F., Xu, Z., Sun, J., Khan, F. M., Ahmed, A., & Zhao, Y. (2022). Recent advances in sensors for fire detection. Sensors, 22(9), 3310. https://doi.org/10.3390/s22093310
Vasconcelos, R. N., Franca Rocha, W. J. S., Costa, D. P., Duverger, S. G., Santana, M. M. M. de, Cambui, E. C. B., … & Cordeiro, C. L. (2024). Fire detection with deep learning: A comprehensive review. Land, 13(10), 1696. https://doi.org/10.3390/land13101696
Cheng, G., Chen, X., Wang, C., Li, X., Xian, B., & Yu, H. (2024). Visual fire detection using deep learning: A survey. Neurocomputing, 596, 127975. https://doi.org/10.1016/j.neucom.2024.127975
Morchid, A., Oughannou, Z., Alami, R. El, Qjidaa, H., Jamil, M. O., & Khalid, H. M. (2024). Integrated internet of things (IoT) solutions for early fire detection in smart agriculture. Results in engineering, 24, 103392. https://doi.org/10.1016/j.rineng.2024.103392
Mohapatra, H., & Rath, A. K. (2021). An IoT based efficient multi-objective real-time smart parking system. International journal of sensor networks, 37(4), 219–232. https://doi.org/10.1504/IJSNET.2021.119483
Intorelli, A., Braig, D., & Moquin, R. (2009). Real-time data fusion and visualization in support of emergency response operations. 2009 IEEE conference on technologies for homeland security (pp. 417–424). IEEE. https://doi.org/10.1109/THS.2009.5168067
Avgeris, M., Spatharakis, D., Dechouniotis, D., Kalatzis, N., Roussaki, I., & Papavassiliou, S. (2019). Where there is fire there is smoke: A scalable edge computing framework for early fire detection. Sensors, 19(3), 639. https://doi.org/10.3390/s19030639
Patel, H., Patel, S., Shah, M., & Sharma, L. (2018). Real time multi sensor technique for false alarm reduction using IoT. Journal of analysis and computation (JAC), 11(1), 1-20. https://doi.org/10.57046/RFPT1725
Mohapatra, H., Mohanta, B. K., Nikoo, M. R., Daneshmand, M., & Gandomi, A. H. (2022). MCDM-based routing for IoT-enabled smart water distribution network. IEEE internet of things journal, 10(5), 4271–4280. https://doi.org/10.1109/JIOT.2022.3216402
Metrics
