Development and Validation of an IoT-based Measurement System for Monitoring Movement of Critical Structural Infrastructure

Movement-induced natural hazards such as landslides and rockfalls threaten critical infrastructure and human safety in alpine and rural areas. Since such events can hardly be prevented, early geological movement detection is essential for appropriate countermeasures. This work investigates the suitability of an IoT-based, energy-autonomous, and cost-effective sensor network for monitoring geological movements. Based on a systematic literature review, a sensor system was developed focusing on precise MEMS sensor technology, low-cost hardware, energy-efficient operation, edge processing, and long-range wireless communication via LoRaWAN. The system integrates inclination sensors with on-device data processing and wireless transmission to a network server. Validation through laboratory and field tests demonstrated reliable detection of slow, creeping slope movements after adequate system calibration, while maintaining energy efficient operation and wireless data transmission. In addition, data from a similar rockfall protection monitoring system was evaluated. The results confirm that reliable infrastructure monitoring with low-cost IoT sensor networks is technically feasible, provided that the sensors are carefully calibrated. In summary, this work contributes to the development of energy-efficient, low-cost IoT monitoring systems and lays the groundwork for future developments and applications in critical infrastructure monitoring