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Artificial intelligence assisted IoT-fog based framework for emergency fire response in smart buildings

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Abstract

Anthropogenic hazards are unrelenting threat to lives and property, with human irresponsibility emerging as a leading source of urban as well as industrial fires. The complexity of urban structures and crowded layouts make these kinds of fires more lethal. This paper presents an Artificial Intelligence (AI) based framework designed for smart buildings as a solution to the devastating obstacles caused by fire crises. Our system creates a 3D model of the building using floor plans and the A* algorithm for escape route identification. The proposed framework includes a YOLO-based smart monitoring system for the identification and counting of people caught in a fire, with the ability to distinguish between conscious and unconscious persons. The proposed system informs inhabitants in the case of a fire and directs them to the closest exit for a safe evacuation. Moreover, fire and rescue officials receive real-time information on affected persons, such as the number and location of adults and children who are conscious and unconscious. Perhaps most significantly, the suggested framework performs exceptionally well, scoring 96% for precision and 98% for recall in the detection of fire and humans. These findings highlight the effectiveness of the model in locating people within infrastructures affected by fire. The framework considerably outperforms the most advanced algorithms in terms of speed and efficiency for shortest path detection, greatly improving the ability of fire rescue teams to quickly find and aid residents who are trapped in a fire.

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All the datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.

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References

  1. Hegazi, Y.S., Tahoon, D., Abdel-Fattah, N.A., El-Alfi, M.F.: Socio-spatial vulnerability assessment of heritage buildings through using space syntax. Heliyon 8(3), e09133 (2022). https://doi.org/10.1016/j.heliyon.2022.e09133

    Article  Google Scholar 

  2. Nomani, S., Rasel, M., Reedoy, I.K.: Industrial development and climate change: a case study of Bangladesh. Indones. J. Innov. Appl. Sci. 2(1), 68–79 (2022). https://doi.org/10.47540/ijias.v2i1.428

    Article  Google Scholar 

  3. Kumasaki, M., King, M.: Three cases in Japan occurred by natural hazards and lessons for Natech disaster management. Int. J. Disaster Risk Reduct. 51, 101855 (2020). https://doi.org/10.1016/j.ijdrr.2020.101855

    Article  Google Scholar 

  4. Bertolina, C., Farotto, M., Crivellari, S., Giacchero, F., Grasso, C., Bertolotti, M., Maconi, A.: Summary of the 2016 World Health Organization Report and 2021 Compendium on Environmental Diseases. Work. Pap. Public Health (2023). https://doi.org/10.4081/wpph.2023.9604

    Article  Google Scholar 

  5. Kodur, V., Kumar, P., Rafi, M.M.: Fire hazard in buildings: review, assessment and strategies for improving fire safety. PSU Res. Rev. 4(1), 1–23 (2020). https://doi.org/10.1108/PRR-12-2018-0033

    Article  Google Scholar 

  6. Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697–713 (2018). https://doi.org/10.1016/j.scs.2018.01.053

    Article  Google Scholar 

  7. Yar, H., Imran, A.S., Khan, Z.A., Sajjad, M., Kastrati, Z.: Towards smart home automation using IoT-enabled edge-computing paradigm. Sensors 21(14), 4932 (2021). https://doi.org/10.3390/s21144932

    Article  Google Scholar 

  8. Balfaqih, M., Alharbi, S.A.: Associated information and communication technologies challenges of smart city development. Sustainability 14(23), 16240 (2022). https://doi.org/10.3390/su142316240

    Article  Google Scholar 

  9. Saini, M., Sengupta, E., Singh, H.: Artificial intelligence inspired IoT-fog based framework for generating early alerts while train passengers traveling in dangerous states using surveillance videos. Multimed. Tools Appl. (2023). https://doi.org/10.1007/s11042-023-16107-0

    Article  Google Scholar 

  10. Zhang, Y., Zhang, Y., Shi, Z., Fu, R., Liu, D., Zhang, Y., Du, J.: Enhanced cross-domain dim and small infrared target detection via content-decoupled feature alignment. IEEE Trans. Geosci. Remote Sens. (2023). https://doi.org/10.1109/TGRS.2023.3304684

    Article  Google Scholar 

  11. Yar, H., Khan, Z.A., Ullah, F.U.M., Ullah, W., Baik, S.W.: A modified YOLOv5 architecture for efficient fire detection in smart cities. Expert Syst. Appl. 231, 120465 (2023). https://doi.org/10.1016/j.eswa.2023.120465

    Article  Google Scholar 

  12. Deepa, K.R., Chaitra, A.S., Jhansi, K., Anitha Kumari, R.D., Ashwini Kumari, P., Kodabagi, M.M.: Development of Fire Detection surveillance using machine learning and IoT. In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 2022, pp. 1–6. IEEE (2022). https://doi.org/10.1109/MysuruCon55714.2022.9972725

  13. Zhang, Y., Zhang, X., Huang, X.: Design a safe firefighting time (SFT) for major fire disaster emergency response. Int. J. Disaster Risk Reduct. 88, 103606 (2023). https://doi.org/10.1016/j.ijdrr.2023.103606

    Article  Google Scholar 

  14. Yar, H., Ullah, W., Khan, Z.A., Baik, S.W.: An effective attention-based CNN model for fire detection in adverse weather conditions. ISPRS J. Photogramm. Remote Sens. 206, 335–346 (2023). https://doi.org/10.1016/j.isprsjprs.2023.10.019

    Article  Google Scholar 

  15. Ouache, R., Bakhtavar, E., Hu, G., Hewage, K., Sadiq, R.: Evidential reasoning and machine learning-based framework for assessment and prediction of human error factors-induced fire incidents. J. Build. Eng. 49, 104000 (2022). https://doi.org/10.1016/j.jobe.2022.104000

    Article  Google Scholar 

  16. Withington, J.: A Disastrous History of the World: Chronicles of War, Earthquake, Plague and Flood. Hachette, London (2010)

    Google Scholar 

  17. Khan, T., Aslan, H.İ.: Performance Evaluation of Enhanced ConvNeXtTiny-Based Fire Detection System in Real-World Scenarios (2023). https://openreview.net/forum?id=A-E41oZCfrf

  18. Badina, S., Babkin, R., Bereznyatsky, A., Bobrovskiy, R.: Spatial aspects of urban population vulnerability to natural and man-made hazards. City Environ. Interact. 15, 100082 (2022). https://doi.org/10.1016/j.cacint.2022.100082

    Article  Google Scholar 

  19. Sengupta, E., Saini, M., Singh, M., Singh, J.: An exploration into Artificial intelligence based advancement in education field. In: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022, pp 1250–1255. IEEE (2022). https://doi.org/10.1109/ICACITE53722.2022.9823801

  20. Yan, F., Jia, J., Hu, Y., Guo, Q., Zhu, H.: Smart fire evacuation service based on Internet of Things computing for Web3D. J. Internet Technol. 20(2), 521–532 (2019)

    Google Scholar 

  21. Wu, C.C., Yu, K.M., Chine, S.T., Cheng, S.T., Huang, Y.S., Lei, M.Y., Lin, J.H.: An intelligent active alert application on handheld devices for emergency evacuation guidance. In: 2013 Fifth International Conference on Ubiquitous and Future Networks (ICUFN), July 2013, pp 7–11. IEEE (2013). https://doi.org/10.1109/ICUFN.2013.6614766

  22. Sun, Q., Wan, W., Yu, X.: The simulation of building escape system based on Unity3D. In: 2016 International Conference on Audio, Language and Image Processing (ICALIP), July 2016, pp. 156–160. IEEE (2016). https://doi.org/10.1109/ICALIP.2016.7846656

  23. Wang, C., Luo, J., Zhang, C., Liu, X.: A dynamic escape route planning method for indoor multi-floor buildings based on real-time fire situation awareness. In: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), 2020, pp. 222–229. IEEE (2020). https://doi.org/10.1109/ICPADS51040.2020.00039

  24. Wehbe, R., Shahrour, I.: A BIM-based smart system for fire evacuation. Future Internet 13(9), 221 (2021). https://doi.org/10.3390/fi13090221

    Article  Google Scholar 

  25. Wächter, T., Rexilius, J., König, M., Hoffmann, M.: Dynamic evacuation system for the intelligent building based on beacons and handheld devices. In: 2021 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 2021, pp. 117–124. IEEE (2021). https://doi.org/10.1109/ICT-DM52643.2021.9664046

  26. Yar, H., Hussain, T., Agarwal, M., Khan, Z.A., Gupta, S.K., Baik, S.W.: Optimized dual fire attention network and medium-scale fire classification benchmark. IEEE Trans. Image Process. 31, 6331–6343 (2022). https://doi.org/10.1109/TIP.2022.3207006

    Article  Google Scholar 

  27. Dilshad, N., Khan, T., Song, J.: Efficient deep learning framework for fire detection in complex surveillance environment. Comput. Syst. Sci. Eng. 46(1), 749–764 (2023). https://doi.org/10.32604/csse.2023.034475

    Article  Google Scholar 

  28. Dreher, C.R., Wächter, M., Asfour, T.: Learning object–action relations from bimanual human demonstration using graph networks. IEEE Robot. Autom. Lett. 5(1), 187–194 (2019). https://doi.org/10.1109/LRA.2019.2949221

    Article  Google Scholar 

  29. Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. In: International Conference on Machine Learning, 2018, pp. 4055–4064. PMLR (2018)

  30. Wang, J., Xu, C., Yang, W., Yu, L.: A normalized Gaussian Wasserstein distance for tiny object detection (2021). arXiv preprint arXiv:2110.13389. https://doi.org/10.48550/arXiv.2110.13389

  31. Saini, M., Sengupta, E., Singh, M., Singh, H., Singh, J.: Sustainable Development Goal for Quality Education (SDG 4): a study on SDG 4 to extract the pattern of association among the indicators of SDG 4 employing a genetic algorithm. Educ. Inf. Technol. 28(2), 2031–2069 (2023). https://doi.org/10.1007/s10639-022-11265-4

    Article  Google Scholar 

  32. Yu, J., Zhou, X.: One-dimensional residual convolutional autoencoder based feature learning for gearbox fault diagnosis. IEEE Trans. Ind. Inform. 16(10), 6347–6358 (2020). https://doi.org/10.1109/TII.2020.2966326

    Article  Google Scholar 

  33. Banerjee, C., Mukherjee, T., Pasiliao Jr., E.: An empirical study on generalizations of the ReLU activation function. In: Proceedings of the 2019 ACM Southeast Conference, 2019, pp. 164–167 (2019). https://doi.org/10.1145/3299815.3314450

  34. Lu, L., Shin, Y., Su, Y., Karniadakis, G.E.: Dying ReLU and initialization: theory and numerical examples (2019). arXiv preprint arXiv:1903.06733. https://doi.org/10.48550/arXiv.1903.06733

  35. Wang, S., Sun, G., Zheng, B., Du, Y.: A crop image segmentation and extraction algorithm based on Mask RCNN. Entropy 23(9), 1160 (2021). https://doi.org/10.3390/e23091160

    Article  Google Scholar 

  36. Zhao, J., Zhu, H., Niu, L.: BiTNet: a lightweight object detection network for real-time classroom behavior recognition with transformer and bi-directional pyramid network. J. King Saud Univ. Comput. Inf. Sci. 35(8), 101670 (2023). https://doi.org/10.1016/j.jksuci.2023.101670

  37. Arkin, E., Yadikar, N., Xu, X., Aysa, A., Ubul, K.: A survey: object detection methods from CNN to transformer. Multimed. Tools Appl. 82(14), 21353–21383 (2023). https://doi.org/10.1007/s11042-022-13801-3

    Article  Google Scholar 

  38. Ghoshal, B., Tucker, A., Sanghera, B., Lup Wong, W.: Estimating uncertainty in deep learning for reporting confidence to clinicians in medical image segmentation and diseases detection. Comput. Intell. 37(2), 701–734 (2021). https://doi.org/10.1111/coin.12411

    Article  MathSciNet  Google Scholar 

  39. Abdelmutalab, A., Wang, C.: Pedestrian detection using MB-CSP model and boosted identity aware non-maximum suppression. IEEE Trans. Intell. Transp. Syst. (2022). https://doi.org/10.1109/TITS.2022.3196854

    Article  Google Scholar 

  40. Martinez, P., Al-Hussein, M., Ahmad, R.: A scientometric analysis and critical review of computer vision applications for construction. Autom. Constr. 107, 102947 (2019). https://doi.org/10.1016/j.autcon.2019.102947

    Article  Google Scholar 

  41. Haghani, M.: Optimising crowd evacuations: mathematical, architectural and behavioural approaches. Saf. Sci. 128, 104745 (2020). https://doi.org/10.1016/j.ssci.2020.104745

    Article  Google Scholar 

  42. Sasikala, M.N., Shruthi, C., Mohana, A., Harika, M., Supriya, S.: An adaptive edge detecting method for satellite imagery based on canny edge algorithm. Int. J. Adv. Eng. Res. Sci. 7(4) (2020)

  43. Suneetha, A., Srinivasa Reddy, E.: Robust Gaussian noise detection and removal in color images using modified fuzzy set filter. J. Intell. Syst. 30(1), 240–257 (2020). https://doi.org/10.1515/jisys-2019-0211

    Article  Google Scholar 

  44. Goyal, B., Dogra, A., Agrawal, S., Sohi, B.S., Sharma, A.: Image denoising review: from classical to state-of-the-art approaches. Inf. Fusion 55, 220–244 (2020). https://doi.org/10.1016/j.inffus.2019.09.003

    Article  Google Scholar 

  45. Wang, P., Liang, J., Wang, L.V.: Single-shot ultrafast imaging attaining 70 trillion frames per second. Nat. Commun. 11(1), 2091 (2020). https://doi.org/10.1038/s41467-020-15745-4

    Article  Google Scholar 

  46. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2009). https://doi.org/10.1109/TPAMI.2009.167

    Article  Google Scholar 

  47. Maiettini, E., Pasquale, G., Rosasco, L., Natale, L.: On-line object detection: a robotics challenge. Auton. Robots 44, 739–757 (2020). https://doi.org/10.1007/s10514-019-09894-9

    Article  Google Scholar 

  48. Stanev, V., Oses, C., Kusne, A.G., Rodriguez, E., Paglione, J., Curtarolo, S., Takeuchi, I.: Machine learning modeling of superconducting critical temperature. NPJ Comput. Mater. 4(1), 29 (2018). https://doi.org/10.1038/s41524-018-0085-8

    Article  Google Scholar 

  49. Srivastava, S., Divekar, A.V., Anilkumar, C., Naik, I., Kulkarni, V., Pattabiraman, V.: Comparative analysis of deep learning image detection algorithms. J. Big Data 8(1), 1–27 (2021). https://doi.org/10.1186/s40537-021-00434-w

    Article  Google Scholar 

  50. Yacouby, R., Axman, D.: Probabilistic extension of precision, recall, and F1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems, 2020, pp. 79–91 (2020). https://doi.org/10.18653/v1/2020.eval4nlp-1.9

  51. Bankoff, G., Chakravarty, S., Jordan, S.: The warming city: the increasing risk of summer fires in Delhi. Disasters 48(1), e12601 (2024). https://doi.org/10.1111/disa.12601

    Article  Google Scholar 

  52. Joshi, S.K., Saini, A.K.: Fire as a disaster: review of various prevention, protection and management techniques. In: Fifth World Congress on Disaster Management: Proceedings of the International Conference on Disaster Management, 24–27 November 2021, New Delhi, India, vol. IV, p. 242. Taylor & Francis (2023)

  53. Joshi, V., Phulwani, P.: Consumer's safety concerns on fire hazards and readiness of electric vehicle batteries in India. In: Handbook of Evidence Based Management Practices in Business, pp. 479–485. Routledge, New York (2023)

  54. Juyal, S., Abbasi, T., Abbasi, S.A.: An analysis of failures leading to fire accidents in hospitals; with specific reference to India. J. Fail. Anal. Prev. (2023). https://doi.org/10.1007/s11668-023-01668-x

    Article  Google Scholar 

  55. Tyukavina, A., Potapov, P., Hansen, M.C., Pickens, A.H., Stehman, S.V., Turubanova, S., et al.: Global trends of forest loss due to fire from 2001 to 2019. Front. Remote Sens. 3, 825190 (2022). https://doi.org/10.3389/frsen.2022.825190

    Article  Google Scholar 

  56. Ray, S., Thakur, V., Bandyopadhyay, K.: India's Insurance Sector: Challenges and Opportunities, Working Paper, No. 394. Indian Council for Research on International Economic Relations (ICRIER), New Delhi (2020)

  57. Deshpande, R.S.: Disaster management in India: are we fully equipped? J. Soc. Econ. Dev. 24(Suppl 1), 242–281 (2022). https://doi.org/10.1007/s40847-022-00225-w

    Article  Google Scholar 

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  1. Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India

    Munish Saini, Eshan Sengupta & Suraaj Thakur

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  1. Munish Saini
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The original online version of this article was revised: The algorithm table headings “Algorithm 1: Fire and person detection module, Algorithm 2: Floor plan extraction and Algorithm 3: Nearest escape route detection” were missing in the article, the headings have been included now.

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Saini, M., Sengupta, E. & Thakur, S. Artificial intelligence assisted IoT-fog based framework for emergency fire response in smart buildings. Cluster Comput 27, 7915–7938 (2024). https://doi.org/10.1007/s10586-024-04374-x

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