EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 723 (2026) E3S Web Conf., 723 (2026) 04007 References
Open Access
Issue
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
Article Number 04007
Number of page(s) 6
Section Intelligent Infrastructure, Iot, Robotics & Sustainable Engineering
DOI https://doi.org/10.1051/e3sconf/202672304007
Published online 08 July 2026
  1. Prabowo, A. R., Tuswan, T., Ridwan, R.: Advanced Development of Sensors’ Roles in Maritime-Based Industry and Research: From Field Monitoring to High-Risk Phenomenon Measurement. Appl. Sci. 11(9) (2021), 3954. https://doi.org/10.3390/app11093954 [Google Scholar]
  2. NFPA: Fire in Industrial or Manufacturing Properties. (2026). https://www.nfpa.org/education-and-research/research/nfpa-research/fire-statistical-reports/fires-in-us-industrial-or-manufacturing-properties [Google Scholar]
  3. Park, S., et al.: Smart Fire Safety Management System (SFSMS) Connected with Energy Management for Sustainable Service in Smart Building Infrastructures. Buildings 13(12) (2023), 3018. https://doi.org/10.3390/buildings13123018 [Google Scholar]
  4. Talaat, F. M., ZainEldin, H.: An improved fire detection approach based on YOLO-v8 for smart cities. Neural Comput. Appl. 35(28) (2023), 20939–20954. https://doi.org/10.1007/s00521-023-08809-1 [Google Scholar]
  5. Ramos, L., Casas, E., Bendek, E., Romero, C., Rivas, F.: Computer vision for wildfire detection: a critical brief review. Multimed. Tools Appl. 83 (2024), 83427– 83470. https://doi.org/10.1007/s11042-024-18685-z [Google Scholar]
  6. Muhendra, R., Amin, A.: Real-Time Monitoring: Development of Low Power Fire Detection System for Dense Residential Housing Based on Internet of Things (IoT) and Cloud Messenger. Sci. J. Inform. 8(2) (2021), 202–212. https://doi.org/10.15294/sji.v8i2.30811 [Google Scholar]
  7. Wang, X., et al.: Evaluation of gas and particle sensors for detecting spacecraft-relevant fire emissions. Fire Saf. J. 113 (2020), 102977. https://doi.org/10.1016/j.firesaf.2020.102977 [Google Scholar]
  8. Pan, J., McElhannon, J.: Future Edge Cloud and Edge Computing for Internet of Things Applications. IEEE Internet Things J. 5(1) (2018), 439–449. https://doi.org/10.1109/JIOT.2017.2767608 [Google Scholar]
  9. Chang, Y.-H., Wu, F.-C., Lin, H.-W.: Design and Implementation of ESP32-Based Edge Computing for Object Detection. Sensors 25(6) (2025), 1656. https://doi.org/10.3390/s25061656 [Google Scholar]
  10. Udurume, M., Hwang, T., Uddin, R., Aziz, T., Koo, I.: Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments. Appl. Sci. 15(2) (2025), 500. https://doi.org/10.3390/app15020500 [Google Scholar]
  11. Shees, A., Ansari, M. S., Varshney, A., Asghar, M. N., Kanwal, N.: FireNet-v2: Improved Lightweight Fire Detection Model for Real-Time IoT Applications. Procedia Comput. Sci. 218 (2023), 2233–2242. https://doi.org/10.1016/j.procs.2023.01.199 [Google Scholar]
  12. Sharobiddinov, D., Siddiqui, H. U. R., Saleem, A. A., Mezquita, G. M., Vargas, D. L. R., Díez, I. D. L. T.: Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2. Sensors 25(20) (2025), 6419. https://doi.org/10.3390/s25206419 [Google Scholar]
  13. Lestariningati, S. I., Kamila, S. T., Agusdian, A.: Development of a Low-Cost Early Fire Detection System with IoT and Telegram Integration. In: Proceedings of the 8th International Conference on Informatics, Engineering, Science & Technology (INCITEST 2025). Advances in Engineering Research, vol. 287. Atlantis Press (2025), 146–158. https://doi.org/10.2991/978-94-6463-924-7_13 [Google Scholar]
  14. Hymel, S., Banbury, C., Situnayake, D., Elium, A., Ward, C., Kelcey, M., Baaijens, M., Majchrzycki, M., Plunkett, J., Tischler, D., Grande, A., Moreau, L., Maslov, D., Beavis, A., Jongboom, J., Reddi, V.J.: Edge Impulse: An MLOps Platform for Tiny Machine Learning. arXiv (2022). https://doi.org/10.48550/arXiv.2212.03332 [Google Scholar]
  15. Espressif Systems: ESP OpenCV Component: OpenCV Library for ESP-IDF. GitHub Repository (2024). https://github.com/espressif/esp-opencv-component [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.