MUMBAI, India, July 30 -- Intellectual Property India has published a patent application (202641087476 A) filed by Akula Manoj Kumar; Dr. P. Sekhar Babu; Dr. P A Abdul Saleem; Dr. T Praveen; Dr. T Rakesh; and Mr. B Prem Kumar on July 17, 2026, for Autonomous Solar-Powered Multi-Sensor Iot And Ai-Based Landslide Early Warning And Prediction System With Hybrid Lora-Gsm Communication Network.
Inventors include Akula Manoj Kumar; Dr. P. Sekhar Babu; Dr. P A Abdul Saleem; Dr. T Praveen; Dr. T Rakesh; and Mr. B Prem Kumar.
The application for the patent was published on July 24, 2026, under issue no. 30/2026.
Abstract: The present invention relates to an Autonomous Solar-Powered Multi-Sensor IoT and AI-Based Landslide Early Warning and Prediction System with Hybrid LoRa- GSM Communication Network for real-time monitoring, prediction, and early warning of landslide events in vulnerable regions. The system comprises a plurality of environmental and geotechnical sensors including a rain sensor, rain gauge, soil moisture sensor, water level sensor, MPU6050 tilt sensor, and flex sensor configured to continuously monitor parameters associated with slope stability. An ESP32-based processing unit acquires and preprocesses sensor data and transmits the data through a hybrid communication network comprising LoRa and GSM modules. The system further includes a cloud-based IoT platform for data storage, visualization, and remote monitoring. An Artificial Intelligence (AI) and Machine Learning (ML) engine performs multi-sensor data fusion and analyzes historical and real-time data to predict landslide risk and classify warning levels. A solar power subsystem comprising a solar panel, rechargeable battery, and power management circuitry enables autonomous operation in remote and off-grid locations. Upon detection of abnormal conditions or elevated landslide risk, the system automatically generates alerts through SMS notifications, cloud-based messages, mobile applications, and local audible alarms. The invention provides continuous monitoring, intelligent prediction, reliable communication, and energy-efficient operation, thereby improving disaster preparedness, reducing false alarms, and enhancing the safety of communities, infrastructure, and assets located in landslide-prone areas.
Disclaimer: Curated by HT Syndication.
