Incorporating Edge-AI in IoT-Cloud Framework for Landslide Surveillance and Forecasting
Kapil Aggarwal, Pratibha C. Kaladeep, Bhavesh D. Patel, C.S. Sasireka, J. Arun Kumar, Nookala Venu
Abstract
In landslide-prone regions, the timely detection and precise prediction of impending disasters are paramount for minimizing their devastating impact. By integrating edge devices endowed with AI capabilities into the existing IoT infrastructure, this approach enables real-time data processing and analysis at the source, facilitating swift detection of early warning signs and prompt dissemination of alerts. Augmenting this setup with cloud resources enhances scalability and computational prowess, crucial for conducting comprehensive data analysis and predictive modeling. Anticipated outcomes include the development of a sophisticated monitoring system capable of accurately forecasting landslides, thereby enhancing preparedness and resilience in landslide-prone areas. The project envisions a scalable and adaptable framework that integrates seamlessly into existing infrastructure, offering a cost-effective and reliable solution for landslide surveillance and prediction.