Intelligent Aircraft Hangar Fire Detection and Location System Based on Wireless Sensor Network

Authors

  • Abbas Abdullahi Air Force Institute of Technology, Kaduna
  • Mathias Usman Bonet Air Force Institute of Technology, Kaduna
  • Ubadike O. C. Air Force Institute of Technology, Kaduna
  • Ameer Muhammed Air Force Institute of Technology, Kaduna
  • Ubadike O. A. Air Force Institute of Technology, Kaduna

DOI:

https://doi.org/10.4108/eetsc.3742

Keywords:

Smart City, Aircraft Hangar, Fire Protection, Safety, WSN, Data Visualization, Machine Learning, GUI

Abstract

Aircraft hangar fire detection systems are essential for protecting both the facility's assets and the contents of an aircraft. In terms of predicting a fire outbreak at an aircraft hangar, the Intelligent Aircraft Hangar Fire detection is considered as a high-performance system that is designed based on the principle of a wireless sensor network (WSN), which operates by employing three sensor nodes at different locations inside the aircraft hangar to transmit gas concentrations in the air to a base station (BS) and send the resulting data from the sensor nodes to a server for analysis and visualization of the risk level. The server uses Machine Learning (ML) techniques to analyze the acquired data along with the sample gas data and displays the report in real time. When the smoke (gas) concentration is high, the server predicts a fire outbreak by displaying a high concentration zone on the Graphic User Interface (GUI). By this, the server automatically issues a warning and identifies the potential fire location. The technology is built to protect aircraft assets, hangar buildings, and human (personnel) life. A crucial part in the early detection of fire is played by the intelligent system

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References

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Published

05-10-2023

How to Cite

[1]
A. Abdullahi, M. U. Bonet, U. O. Chiedu, A. Muhammed, and U. O. Arinze, “Intelligent Aircraft Hangar Fire Detection and Location System Based on Wireless Sensor Network ”, EAI Endorsed Trans Smart Cities, vol. 7, no. 2, p. e5, Oct. 2023.

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