Authors: Nitin Patil, Professor Abhishek Popat Bangar, Professor S. B. Bhosale
Abstract: Rockfalls are a recurring hazard in mining areas, road corridors, and hilly regions. They are difficult to predict because they usually show very little pre-failure deformation and collapse within a short time. Internet of Things (IoT) technology, which combines low-cost field sensors, wireless communication, cloud processing, and automated alerting, offers a practical way to monitor such slopes continuously. This review synthesizes five selected scholarly works that cover vibration-based stability assessment of falling-type rock masses, 5G-IoT monitoring with a tangent-angle warning criterion, micro-seismic rockfall detection with failure forecasting, a multi-sensor rockfall early warning system in the Himalaya, and an IoT-based landslide early warning framework deployed in India. The literature suggests that displacement alone is a weak indicator for brittle rock failure, and that dynamic indicators such as dominant frequency and acceleration amplitude ratio, or event-based indicators such as accumulated seismic energy, can show instability earlier. Field deployments also show that communication reliability, power supply, seasonal environmental effects, and the absence of validated alert thresholds are major practical constraints. The paper compares the studies’ methods and contributions, identifies cross-cutting challenges, and proposes directions for future investi-gation. Because the review is based on five selected papers rather than an exhaustive database search, it is presented as a fo-cused narrative review and not as a systematic review.
