Design and Implementation of an Arduino Nano–Based Automated Fire and Smoke Detection System with Integrated Water Sprinkler Control

Fire hazards continue to pose serious risks to human life and property, particularly in residential settings where immediate detection and response are crucial. This study presents the design and development of an Arduino Nano–based automated fire and gas detection system integrated with a water sprinkler mechanism. The system utilizes an MQ-2 gas and smoke sensor together with a KY-026 flame sensor to continuously monitor environmental conditions and identify potential fire-related threats.
When elevated gas or smoke levels are detected, the system generates early warning signals through slow visual and audible alerts using a red LED and a passive buzzer. In the presence of an active flame, the system prioritizes fire conditions by activating faster and more intense alarms and automatically operating a water pump to discharge water through a sprinkler for fire suppression. The system employs threshold-based decision logic and priority control within a closed-loop embedded framework to ensure accurate and timely responses.
Experimental testing showed that the prototype reliably detected gas, smoke, and flame conditions and responded appropriately in real time. Due to its low cost, simple design, and expandability, the proposed system is suitable for educational purposes, prototype fire safety demonstrations, and small-scale residential applications.

Fire Detection System, Gas and smoke sensor, Flame Sensor

1. Ogata, K. (2010). Modern control engineering (5th ed.). Prentice Hall. [Google Scholar] [Crossref]

2. Randive, A. S., Patil, S. S., & Deshmukh, P. R. (2022). Arduino-based fire detection and automatic water sprinkler system. International Research Journal of Engineering and Technology (IRJET), 9(4), 987–991. [Google Scholar] [Crossref]

3. Monk, S. (2022). Programming Arduino: Getting started with sketches (2nd ed.). McGraw-Hill Education. [Google Scholar] [Crossref]

4. Jagtap, P. S., Patil, R. R., & Shinde, S. R. (2023). Fire and smoke detection system using Arduino and MQ-2 sensor. International Journal of Advanced Research in Engineering and Technology, 14(2), 45–52. [Google Scholar] [Crossref]

5. Bolton, W. (2015). Mechatronics: Electronic control systems in mechanical and electrical engineering (6th ed.). Pearson Education. [Google Scholar] [Crossref]

6. Tun, T. T., Aung, M. T., & Kyaw, Z. M. (2020). Arduino-based fire detection and alarm system using gas and smoke sensors. International Journal of Scientific and Engineering Research, 11(6), 123–128. [Google Scholar] [Crossref]

7. Patel, A., Shah, R., & Mehta, K. (2023). Design and implementation of sensor-based automated safety systems using microcontrollers. International Journal of Modern Engineering Technology and Science, 7(3), 101–108. [Google Scholar] [Crossref]

8. Paul, R., Das, S., & Mukherjee, A. (2025). Real-time fire monitoring system using Arduino technology. Journal of Embedded Systems and Applications, 12(1), 33–40. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet