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@article{193629,
        author = {Irfana Salih and Rithanya K and Karthikeyan B},
        title = {AI- Assisted IoT-Based fire monitoring system in forest using Anomaly Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1034-1038},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193629},
        abstract = {Forest fires in dense and remote regions pose severe threats to ecosystems, biodiversity, and human safety, while the fire detection systems suffer from delayed response, limited coverage, and high deployment costs. This paper proposes an AI-assisted IoT-based fire alarm system designed for early forest fire detection and real-time monitoring. The system integrates environmental sensors measuring temperature, humidity, smoke, and flame intensity with low-power microcontrollers such as ESP32 or Raspberry Pi. Sensor data are transmitted wirelessly to a centralized platform, where machine learning–based anomaly detection is applied to distinguish genuine fire events from environmental noise, thereby reducing false alarms. Upon detecting critical fire conditions, the system triggers local alarms and transmits instant alerts through cloud services and SMS notifications. Experimental evaluation demonstrates reliable detection performance, rapid alert generation, and improved accuracy under varying environmental conditions. The proposed system offers a scalable, cost-effective, and intelligent solution for forest fire monitoring, contributing to disaster prevention and smart environmental management applications},
        keywords = {IoT, Forest Fire Monitoring, Anomaly Detection, ESP32, Machine Learning, Smart Sensing},
        month = {March},
        }