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May 2, 20260 citationsOpen Access

IoT-Enabled Fire Detection and Automated Water Sprinkler System Using Raspberry Pi

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SNSharanbasamma NachwarSPSujata PatilKLE Technological UniversityDKDr. Sanjeev Kumar

Key Points

  • To develop a smart fire management system that detects and responds to fire incidents automatically.
  • Integrated multiple sensors (flame, temperature, gas) for continuous monitoring.
  • Implemented alerts via email with live updates and video when fire conditions are detected.
  • Executed safety actions like activating a water sprinkler and notifying emergency services when critical temperature limits are reached.
  • Successfully detected fire and gas leaks through sensor integration.
  • Provided real-time alerts with images and updates leading to an immediate response.
  • Activated safety measures, reducing potential property damage and enhancing safety.

Abstract

The proposed project introduces a smart fire management system which automatically detects and controls fire accidents using a Raspberry Pi. The system integrated with multiple sensors, including flame, temperature, and gas sensors, to continuously monitor the environment for fire and gas leakages. When any abnormal condition is detected, such as a sudden rise in temperature, presence of flame, or leakage of gases like LPG, methane, or ethane, the system immediately responds by sending an email alert to the user. The alert includes a live video or image of the affected area and regular updates on the room temperature. Simultaneously, the Raspberry Pi executes safety actions such as switching off the main power supply, activating an exhaust fan to remove smoke or gas, and turning on an automatic water sprinkler to extinguish the fire. If the temperature exceeds a critical limit, an emergency notification is also sent to the fire brigade. Designed for medium-sized spaces, this system provides a low-cost and reliable solution for early fire detection, quick response, and minimizing property damage or loss of life.

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Cite This Study

Nachwar et al. (2026) studied this question.

synapsesocial.com/papers/69f594fc71405d493afffeaahttps://doi.org/10.5281/zenodo.19912825
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