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Volume 19, Issue 5
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Introduction

Forest fires have been a major hazard to forest management, needing sophisticated monitoring and management techniques. By creating an embedded intelligent video analysis system, this research proposed a complete strategy for addressing this difficulty.

Methods

The system's hardware architecture was explained, and the operating system software was detailed, using a software and hardware design based on the ZynqSoC. At the same time, an emphasis on forest fire prevention applications was maintained. Furthermore, the study investigated a unique technique for forest fire detection using Arduino as a field data collector and a fuzzy logic algorithm to improve accuracy.

Results

The proposed IoT-Fog-Cloud collaboration infrastructure offered a patented contribution to real-time wildfire monitoring, prediction, and forecasting. The framework achieved excellent accuracy in determining wildfire proneness levels and real-time alert production by utilizing fuzzy K-nearest-neighbor classification and Holt-Winter's forecasting model.

Conclusion

The findings demonstrated the integrated system's ability to reduce the impact of wildfires, serving as a significant reference for future forest fire prevention scenarios.

© 2025 Bentham Science Publishers
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2024-07-02
2025-07-03

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/content/journals/eng/10.2174/0118722121294357240624114638
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Study on Integrated Systems for Enhanced Forest Fire Prevention: An Embedded Intelligent Video Analysis and Collaborative IoT–Fog–Cloud Framework Approach
Recent Pat Eng 19, E020724231491 (2025); https://doi.org/10.2174/0118722121294357240624114638
/content/journals/eng/10.2174/0118722121294357240624114638
/content/journals/eng/10.2174/0118722121294357240624114638
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  • Article Type:     Research Article
Keyword(s): embedded intelligent video analysis; fog computing and cloud framework; Forest fire prevention; fuzzy logic algorithm; internet of things (IoT); real-time monitoring; ZynqSoC

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