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

Ali H.A. Ever E. Kizilkaya B. Raza Khan M.T. Rehman M.U. Ansari S. Imran M.A. Yazici A.
March 2026 Elsevier B.V.

Internet of Things (The Netherlands)
2026 #36

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.

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

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Computer Engineering, Middle East Technical University Northern Cyprus Campus, Mersin 10, 99738, Turkey
School of Computing Science, University of Glasgow, Glasgow, G12 8RZ, United Kingdom
James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, 010000, Kazakhstan

Computer Engineering
School of Computing Science
James Watt School of Engineering
Department of Computer Science

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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026