MUMBAI, India, Oct. 12 -- Intellectual Property India has published a patent application (202641118583 A) filed by Dr. K. S. Balamurugan; Dr. S. John Joseph; and Rajesh Venkatesan on October 04, 2026, for A Distributed Edge-Ai Enabled Forest Hazard Monitoring And Early Warning System Using Multi-Sensor Fusion And Self-Healing Lora Communication.
Inventors include Dr. K. S. Balamurugan; Dr. S. John Joseph; and Rajesh Venkatesan.
The application for the patent was published on October 09, 2026, under issue no. 41/2026.
Abstract: Forest hazards pose significant risks to ecosystems, biodiversity, wildlife, public safety, and natural resources, with forest fires representing a major recurring threat that requires early detection and timely intervention. As reported by the Forest Survey of India (FSI), an average of approximately 2.13 lakh forest-fire detections were recorded annually through SNPP-VIIRS during the 2021–2024 fire seasons, demonstrating the continuing magnitude of forest-fire hazards in India. Conventional methods such as satellite monitoring, manual surveillance, and drone-based observation provide valuable information but are associated with limitations including human intervention, dependence on Internet connectivity, and power requirements. To address these limitations, the present invention provides ECOGUARD – AI- Powered Resilient Forest Hazard Monitoring and Early Warning System, based on a distributed arrangement of multiple low-power sensor nodes incorporating temperature, humidity, smoke, fire, and airflow sensors, with solar-powered operation and LoRa-based self-healing communication. The sensor nodes continuously acquire environmental measurements and transmit the collected data through the LoRa network to Edge AI Gateway 1, where data preprocessing, multi-sensor fusion, AI-based risk assessment, and decision-making are performed locally without continuous Internet connectivity. Historical data are utilized for risk characterization and identification of recurring high-risk conditions, while wind speed, wind direction, and node-location information are used for localized hazard assessment and determination of the probable direction of fire spread. Upon identification of a fire condition or elevated hazard level, Gateway 1 provides a direct alert to the forest officer for rapid local response, while Gateway 2, implemented using Raspberry Pi, provides remote alert functionality beyond the local gateway environment. The system provides real-time environmental status, interpretable hazard-risk levels, fire detection status, event location, wind speed, wind direction, and probable fire- spread direction. Local data buffering, solar-battery backup, sensor calibration, self-healing LoRa routing, and weather-resistant sensor nodes further support reliable operation in remote forest environments. The proposed system can facilitate earlier detection and localized warning of forest hazards, support timely intervention by forest personnel, improve the safety of field responders and nearby communities, and reduce potential losses to forest resources, wildlife, and biodiversity. By integrating continuous ground-level monitoring, local Edge AI processing, and resilient communication, ECOGUARD provides timely forest-hazard response while reducing reliance on continuous Internet connectivity and centralized monitoring.
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