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Design and Implementation of IoT-Enabled Intelligent Fire Detection System Using Neural Networks

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Artificial Intelligence and Mobile Services – AIMS 2023 (AIMS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14202))

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Abstract

Fire detection systems are considered an integral part of any building. However, most fire detection systems use a single passive sensor that usually faces some unavoidable problems, especially with the use of simple processing systems using threshold and trend algorithms. Although more than a single sensor and fire information are used in some existing systems, the real-time fire information and firefighting forecasting are not monitored. Such information facilitates good decision making in firefighting and rescue operations. This paper develops a fast and smart fire detection and monitoring system that can detect and monitor fire incidents with low probability of detection error. The system involves IoT sensors that detect all necessary fire information including heating release rate smoke level, and \({CO}_{2}\) level. Moreover, a fire detection and monitoring model based on artificial neural networks is developed to identify fire information in real-time. The proposed system was tested in a chamber box with around 20 experiments. The positive fire detection rate was high with fast fire detection rate.

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References

  1. Alshallan, A.: Fires and their economic repercussions. Alriyadh newspaper (2022). https://www.alriyadh.com/1968905. Accessed 23 Nov 2022

  2. The Geneva Association Staff: World Fire Statistics, The Geneva Association (2014)

    Google Scholar 

  3. Grant, C., Grant, C., Hamins, A., Bryner, N., Jones, A., Koepke, G.: Research roadmap for smart fire fighting. US Department of Commerce, National Institute of Standards and Technology (2015)

    Google Scholar 

  4. Naser, M.Z.: Mechanistically informed machine learning and artificial intelligence in fire engineering and sciences. Fire Technol. 57(6), 2741–2784 (2021)

    Article  Google Scholar 

  5. Huang, X., Wu, X., Usmani, A.: Perspectives of using artificial intelligence in building fire safety. In: Naser, M., Corbett, G. (eds.) Handbook of Cognitive and Autonomous Systems for Fire Resilient Infrastructures, pp. 139–159. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98685-8_6

  6. Baba, M.C., Grado, J.J.B., Solis, D.J.L., Roma, I.M., Dellosa, JT.: A multisensory arduino-based fire detection and alarm system using GSM communications and RF module with an android application for fire monitoring. Int. J. Innov. Sci. Res. Technol. (IJISRT), 964–968. www.ijisrt.com. ISSN-2456-2165. https://doi.org/10.5281/zenodo, 6433836

  7. Cheng, C., Sun, F., Zhou, X.: One fire detection method using neural networks. Tsinghua Sci. Technol. 16(1), 31–35 (2011)

    Article  Google Scholar 

  8. Zhairui, G., Zhengyu, F.: An algorithm of neural network for fire detection. Microcomput. Appl. 19(11), 37–38 (2003)

    Google Scholar 

  9. Alqourabah, H., Muneer, A., Fati, S.M.: A smart fire detection system using IoT technology with automatic water sprinkler. Int. J. Electr. Comput. Eng. (2088-8708) 11(4) (2021)

    Google Scholar 

  10. Solórzano, A., et al.: Early fire detection based on gas sensor arrays: multivariate calibration and validation. Sens. Actuators B Chem. 352, 130961 (2022)

    Article  Google Scholar 

  11. Baek, J., et al.: Real-time fire detection algorithm based on support vector machine with dynamic time warping kernel function. Fire Technol. 57(6), 2929–2953 (2021)

    Google Scholar 

  12. Jiang, H., Li, Y., Li, D.: Indoor environment monitoring system based on linkit one and yeelink platform. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 933–937. IEEE, October 2016

    Google Scholar 

  13. Rodrigues, M.J., Postolache, O., Cercas, F.: Indoor air quality monitoring system to prevent the triggering of respiratory distress. In: 2019 International Conference on Sensing and Instrumentation in IoT Era (ISSI), pp. 1–6. IEEE, August 2019

    Google Scholar 

  14. Wang, Z., Zhang, T., Huang, X.: Numerical modeling of compartment fires: ventilation characteristics and limitation of Kawagoe’s law. Fire Technol., 1–24 (2022)

    Google Scholar 

  15. Wang, Z., Zhang, T., Huang, X.: Predicting real-time fire heat release rate by flame images and deep learning. In: Proceedings of the Combustion Institute (2022)

    Google Scholar 

  16. Tam, W.C., et al.: Generating synthetic sensor data to facilitate machine learning paradigm for prediction of building fire hazard. Fire Technol., 1–22 (2020)

    Google Scholar 

  17. Wang, D., Yang, Y., Ning, S.: DeepSTCL: a deep spatio-temporal ConvLSTM for travel demand prediction. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, July 2018

    Google Scholar 

  18. Azad, R., Asadi-Aghbolaghi, M., Fathy, M., Escalera, S.: Bi-directional ConvLSTM U-Net with densley connected convolutions. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, p. 0 (2019)

    Google Scholar 

  19. Zhang, T., Wang, Z., Zeng, Y., Wu, X., Huang, X., Xiao, F.: Building Artificial-Intelligence Digital Fire (AID-Fire) system: a real-scale demonstration. J. Build. Eng. 62, 105363 (2022)

    Article  Google Scholar 

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Correspondence to Mohammed Balfaqih .

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Almohammedi, A.A., Balfaqih, M., Nahas, S., Bokhari, A., Alqudsi, A. (2023). Design and Implementation of IoT-Enabled Intelligent Fire Detection System Using Neural Networks. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2023 . AIMS 2023. Lecture Notes in Computer Science, vol 14202. Springer, Cham. https://doi.org/10.1007/978-3-031-45140-9_6

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  • DOI: https://doi.org/10.1007/978-3-031-45140-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45139-3

  • Online ISBN: 978-3-031-45140-9

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