Project presentation demonstrates real-time environmental monitoring and fire detection using wireless sensor networks, indicating improved response to fire hazards.
Project presentation. This project presents a Wireless Sensor Network (WSN)-based system for early forest fire detection and environmental monitoring. The system uses NI WSN sensor nodes to collect real-time environmental data, including air temperature, soil temperature, soil moisture, carbon monoxide (CO), and carbon dioxide (CO₂). LabVIEW is used for data acquisition and visualization, while Python performs fire detection using a rule-based algorithm. The collected data are transmitted via TCP, stored in Firebase Realtime Database, and automatic Email and SMS alerts are generated when fire conditions are detected. The system provides reliable real-time monitoring, reduces false alarms by combining multiple sensor readings, and is designed to be scalable for future AI and multi-hop network integration.
No takes yet. Share an insight, caveat, or question.
Taha et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: