The escalating frequency and intensity of global wildfire events necessitate advanced early warning systems capable of operating reliably in extreme and volatile environments. Traditional wireless sensor networks deployed for environmental monitoring often suffer from high false positive rates and rapid catastrophic failure when exposed to encroaching fire fronts. This paper provides a comprehensive investigation into the mechanisms of event detection through the integration of cooperative sensing protocols and failure containment strategies within wildfire warning networks. By leveraging distributed intelligence, cooperative sensing enables neighboring sensor nodes to aggregate and cross-verify localized environmental data, thereby dramatically enhancing the accuracy of anomaly detection and reducing latency. Concurrently, failure containment mechanisms are introduced to isolate compromised or destroyed nodes, preventing erroneous data cascades and routing loops from degrading the wider network infrastructure. Through rigorous theoretical analysis and simulated deployment scenarios, this study details the architectural requirements, communication paradigms, and resilience frameworks necessary for next-generation fire detection. The findings demonstrate that combining these two methodologies ensures sustained network functionality and reliable data transmission even as physical network topology rapidly degrades under thermal stress. The structural insights presented in this research offer a vital foundation for designing robust disaster management systems tailored to high-risk forest ecosystems.
References
1. Stojanovski, J., & Mofardin, D. (2025). Diamond open access landscape in Croatia: DIAMAS survey results. Publications, 13(1), 13.
2. Stojanovski, J., Petrak, J., & Macan, B. (2009). The Croatian national open access journal platform. Learned Publishing, 22(4), 263–273.
3. Taubert, N., Hobert, A., Fraser, N., Jahn, N., & Iravani, E. (2019). Open access—Towards a non-normative and systematic understanding. arXiv, arXiv:1910.11568.
4. Yoon, J., Ku, H., & Chung, E. (2024). The road to sustainability: Examining key drivers in open access diamond journal publishing. Learned Publishing, 37(3), e1611.
5. Zendejo, D. S., & Escalona, R. S. (2025). Analysis of best practices compliance and transparency for diamond open access journals. Revista de Comunicacion de la Seeci, 58, e903.
6. Abedin, S.; Biondi, A.M.; Wu, R.; Cao, L.; Wang, X. Structural Health Monitoring Using a New Type of Distributed Fiber Optic Smart Textiles in Combination with Optical Frequency Domain Reflectometry (OFDR): Taking a Pedestrian Bridge as Case Study. Sensors 2023, 23, 1591.
7. Soliman, H.; Haque, A. A Wireless Sensor Network Application in Forest Fire Early Detection: A Smart and Secure Approach. In Proceedings of the 2024 Intelligent Systems and Machine Learning Conference (ISML), Hyderabad, India, 5–6 January 2024; pp. 106–111.
8. Kim, Y.; Evans, R.G.; Iversen, W.M. Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network. IEEE Trans. Instrum. Meas. 2008, 57, 1379–1387.
9. Anh Tuan, N.; Yonghan, A. Edge AI for Smart Energy Systems: A Comprehensive Review. In Edge Computing-Latest Advancements, Challenges, and Applications; IntechOpen: London, UK, 2025.