An edge-intelligent three-tier framework for real-time forest fire detection, integrating WSNs, WMSNs, and UAVs

Date

2026-03-01

Author

Ali, Hamzeh Abu
EVER, ENVER
Kizilkaya, Burak
Raza Khan, Muhammad Toaha
Rehman, Masood Ur
Ansari, Shuja
Imran, Muhammad Ali
Yazıcı, Adnan

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Forest fires are becoming prevalent, threatening ecosystems, economies, and public safety while creating an urgent demand for rapid and reliable detection systems. Conventional approaches such as watchtowers, manual patrols, and satellite imaging suffer from limited coverage, delays, and inadequate precision. To address these challenges, we propose a three-tier, edge-centric framework that integrates wireless sensor networks (WSNs), wireless multimedia sensor networks (WMSNs), unmanned aerial vehicles (UAVs), and lightweight machine learning (ML) and deep learning (DL) models for efficient detection. In the first tier, scalar sensors provide early hazard identification; in the second, smart sensors execute a lightweight ML model for intermediate verification, achieving a 94% F1-score with a minimal feature set; and in the third, UAVs equipped with sensors, cameras, and a compact convolutional neural network (CNN) deliver final confirmation. The CNN achieves state-of-the-art results with a 100% F1 score on the FireMan-UAV-RGBT dataset and 99.5% on UAV-FFDB while remaining compact (1.6 MB) and efficient (157 ms inference on Raspberry Pi 5), enabling real-time edge deployment. Simulations show reduced end-to-end delay (813.59 ms) compared to WSN-only (865.84 ms) and WMSN (1066.18 ms) baselines, improved throughput (7.05 kbps vs 3.80 kbps and 3.06 kbps), and a 100% delivery ratio. Real-world WSN testbed experiments further validate the framework, achieving a 97% delivery ratio, 144.39 ms latency (vs. 258.37 ms in simulations), and energy consumption of 0.0559 J/s (closely matching 0.0442 J/s in simulations). These results collectively demonstrate the practicality and effectiveness of the framework for real-time forest fire monitoring and rapid emergency response.

Subject Keywords

Deep learning (DL), Edge computing, Forest fire detection, Machine learning (ML), Unmanned aerial vehicles (UAVs), Wireless sensor networks (WSNs)

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105027209610&origin=inward
https://hdl.handle.net/11511/118589

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Graduate School of Natural and Applied Sciences, Article