Authors : Pankaj Kumar Gupta; Dr. Manish Kumar Singh

Volume/Issue : Volume 10 - 2025, Issue 10 - October

Google Scholar : https://tinyurl.com/5xrjzt7m

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DOI : https://doi.org/10.38124/ijisrt/25oct813

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Abstract : Fire detection remains a critical component of modern smart infrastructure due to the increasing risks of fire- related incidents in urban and industrial environments. Traditional fire detection systems that rely on single-sensor mechanisms often suffer from delayed responses, false alarms, and poor adaptability in dynamic conditions [1]. To address these limitations, this study proposes a novel AIoT-enabled multi-sensor fire detection and alarm framework that integrates temperature, smoke, gas, and flame sensors with the ESP32 microcontroller and NB-IoT communication technology [3], [10], [11]. The system utilizes cloud-based analytics and threshold-driven decision logic to ensure accurate, real-time alerts while maintaining low energy consumption [2], [5]. Multi-sensor data fusion techniques enhance detection precision by combining complementary sensor inputs to minimize false positives and improve reliability [4], [9]. The prototype was experimentally validated under both fire and non-fire conditions, demonstrating an overall detection accuracy of 94% and reducing false alarms by approximately 30% compared to conventional single-sensor systems [6], [15]. This research provides a scalable, energy-efficient, and intelligent solution suitable for deployment in smart buildings and urban safety networks [7], [8], [13]. The proposed framework paves the way for future integration with predictive analytics, federated learning, and wireless power solutions to further enhance proactive fire prevention and safety management [12], [14].

Keywords : Fire Detection, Multi-Sensor Fusion, Internet of Things (IoT), Smart Buildings, NB-IoT, Cloud Analytics, ESP32 Microcontroller, Alarm System, Data Fusion, Smart Safety Systems.

References :

1. M. Chen, Y. Ma, Y. Li, D. Wu, Y. Zhang, and C. Youn, “Wearable 2.0: Enabling human-cloud integration in next generation healthcare systems,” IEEE Commun. Mag., vol. 55, no. 1, pp. 54–61, Jan. 2017.
2. S. Kim, H. Park, and J. Lee, “IoT-based fire detection framework using multi-sensor fusion,” IEEE Internet Things J., vol. 8, no. 4, pp. 2345–2356, Apr. 2021.
3. R. Singh, P. Kumar, and A. Sharma, “Edge intelligence for smart fire detection systems,” Future Internet, vol. 14, no. 7, pp. 185–198, Jul. 2022.
4. X. Li, Z. Wang, and H. Zhao, “Federated learning approach for IoT-based fire monitoring,” Sensors, vol. 23, no. 3, pp. 554–567, Jan. 2023.
5. M. Ahmed, L. Zhou, and Y. Tan, “Deep learning-enabled thermal and optical fire detection in IoT cloud,” IEEE Access, vol. 12, pp. 12945–12956, 2024.
6. P. Gupta and A. Verma, “IoT-based wireless sensor network for smart fire detection in urban environments,” IEEE Sensors J., vol. 21, no. 12, pp. 13456–13465, Jun. 2021.
7. H. Zhang, Y. Liu, and M. Chen, “AI-enabled multi-sensor fusion for smart building fire safety systems,” IEEE Trans. Ind. Informat., vol. 18, no. 9, pp. 6120–6132, Sep. 2022.
8. J. Torres and L. Rivera, “NB-IoT based framework for large-scale fire detection and emergency alert systems,” IEEE Internet Things J., vol. 9, no. 6, pp. 4450–4461, Mar. 2022.
9. A. Sharma, K. Patel, and R. Singh, “Cloud–edge collaboration for real-time fire event monitoring in smart cities,” IEEE Access, vol. 10, pp. 23145–23158, 2022.
10. F. Liu, Y. Wang, and Z. Li, “Privacy-preserving federated learning for intelligent fire detection using IoT devices,” IEEE Trans. Netw. Serv. Manage., vol. 20, no. 1, pp. 120–133, Jan. 2023.
11. M. Hassan and A. Khan, “Blockchain-enhanced IoT framework for secure fire monitoring systems,” IEEE Internet Things J., vol. 10, no. 2, pp. 2150–2162, Feb. 2023.
12. D. Lee, C. Park, and J. Choi, “Thermal image-based fire detection using convolutional neural networks in smart buildings,” Appl. Sci., vol. 13, no. 2, pp. 845–857, Jan. 2023.
13. R. Das and S. Mukherjee, “Energy-efficient wireless charging solution for IoT-based fire safety systems,” IEEE Trans. Sustain. Comput., vol. 8, no. 3, pp. 412–421, Sep. 2023.
14. P. Kumar and N. Singh, “Multi-sensor fusion and edge intelligence for emergency response in industrial fire detection,” IEEE Trans. Emerg. Topics Comput., early access, pp. 1–12, 2024.

15.A. Zhang, Y. Sun, and K. Wong, “5G-enabled IoT fire monitoring framework with low-latency communication,” IEEE Internet Things J., vol. 11, no. 1, pp. 120–132, Jan. 2024.