Abstract:
This work presents LINCS-Dams, a cost-effective proto-type for dynamic monitoring of embankment dams. The experimen-tal setup uses affordable sensors, including micro-electromechanical sys-tem (MEMS) in-place inclinometers (IPIs), water-level gauges, and vibration accelerometers. Sensor outputs are managed by a finite state machine (FSM) that defines accident driven alert and alarm levels while dynamically adjusting each sensor’s data sampling frequency, optimizing energy consumption and ensuring timely responses. The main goal of this work is implementing and validating the proposed system and assessing its value to small embankment dams, which often lack regular monitor-ing. Our cost-effective AIoT approach combines sensor networks with intelligent monitoring for early detection and adaptive response, par-ticularly valuable for embankment dams facing increased climate-driven risks. Experimental results confirm that the prototype delivers reliable response and effective dynamic event detection.
