Project

Smart Geophone IoT System for Landslide Detection

The main aim of this project is to investigate micro-seismic signals arising from diverse landslide initiation scenarios by leveraging Smart Geophone IoT systems.

What is the Project All About?

This project encompasses the design and development of IoT systems incorporating a range of sensors. Employing advanced machine learning and deep learning techniques, the goal is to discern patterns indicative of landslide triggers.

Why is this important?

The outcome of this research holds significant promise for the advancement of early warning systems, ultimately contributing to enhanced landslide prediction and prevention measures.

How does it work?

The Smart Geophone IoT system detects and monitors the micro-seismic signals associated with landslides and data is wirelessly transmitted to a central hub, where machine learning models analyze it in real-time. These models learn patterns from historical data to identify seismic signals indicative of landslide triggers. When a potential threat is detected, the system issues alerts, enabling timely response and aiding in the development of effective landslide warning systems.

Publication Details

• Deekshit, V. N., Maneesha Vinodoni Ramesh, P. K. Indukala, and G. Jayachandran Nair. “Smart geophone sensor network for effective detection of landslide induced geophone signals.” In 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 1565-1569. IEEE, 2016.
• Adarsh, P., P. K. Indukala, and Balaji Hariharan. “An Adaptive Thresholding Framework for Detecting the Subsurface Movements in Landslide-prone Sites.” In 2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN), pp. 1502-1507. IEEE, 2023.
• Arjun, D., P. K. Indukala, and KA Unnikrishna Menon. “PANCHENDRIYA: A Multi-sensing framework through wireless sensor networks for advanced border surveillance and human intruder detection.” In 2019 international conference on communication and electronics systems (ICCES), pp. 295-298. IEEE, 2019.