An IoT-Based Monitoring System for Detecting Slope Movement

Md. Jobair Bin Alam, Ph.D., P.E., M.ASCE, Luis Salgado Manzano, Rahul Debnath, Ahmed Abdelmoamen Ahmed, Ph.D., Synia Williams, and Markus Hartsfield. "An IoT-Based Monitoring System for Detecting Slope Movement." Geotechnical Frontiers 2025. https://doi.org/10.1061/9780784485996.057 Author F. N. Rahman contributed to the creation and design of this poster for educational/outreach purposes. Shallow landslides pose significant risks to communities, infrastructure, and ecosystems, especially in regions with steep terrain and unstable soil. Often, initial slope movements go undetected, as visible signs of failure develop later. This study presents a real-time monitoring approach using an Internet of Things (IoT)-based system to detect early signs of slope movement and hydrologic changes. A laboratory-scale embankment model (0.55 m × 0.31 m × 0.31 m) made of Fat Clay (CH) was instrumented with tilt sensors, moisture sensors, and tensiometers. The system successfully detected micro-rotations as small as 0.03°, demonstrating high sensitivity. Data were collected and processed through a distributed IoT platform designed for real-time monitoring. The study demonstrates the potential for implementing a low-cost, scalable IoT-based solution for continuous monitoring of failure-prone slopes. Such a system can enhance the resilience and long-term performance of geotechnical infrastructure by enabling timely intervention before major slope failures occur.