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Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things
Shuo Zhang1, Demin Gao1, Haifeng Lin1
1College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|November 27, 2019
Summary
This study introduces a novel wildfire detection system using sound spectrum analysis and the Internet of Things (IoT). The system distinguishes between crown and surface fires by analyzing sound frequencies, achieving a 70% recognition rate in simulations.
Area of Science:
- Environmental Science
- Acoustic Engineering
- Computer Science
Background:
- Optical spectrum analysis for wildfire detection faces limitations in forest environments due to obstructions.
- Prompt wildfire detection is crucial for mitigating hazardous natural disasters.
- Existing systems struggle with efficiency and timely monitoring in complex terrains.
Purpose of the Study:
- To propose a novel wildfire detection system utilizing sound spectrum analysis.
- To differentiate between crown and surface fires based on acoustic signatures.
- To develop a sustainable power source for remote sensing applications.
Main Methods:
- Implemented a wireless acoustic detection system leveraging the Internet of Things (IoT).
- Developed a novel tree-energy device for sustainable power generation from living trees.
- Applied sound spectrum analysis and classification algorithms to collected acoustic data.
Main Results:
- Identified distinct sound frequency ranges for crown fires (0-400 Hz) and surface fires (0-15,000 Hz).
- Achieved a recognition rate of approximately 70% in simulation experiments.
- Highlighted factors affecting classification accuracy, including sensor distribution and data transmission.
Conclusions:
- Sound spectrum analysis offers a viable alternative for wildfire detection in obstructed environments.
- The developed IoT-based system demonstrates potential for improved wildfire monitoring.
- Further research is needed to optimize sensor networks and mitigate transmission challenges for enhanced accuracy.
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