Development and Laboratory Evaluation of Low-Cost IoT-Based Early Warning System for Sustainable and Resilient Infrastructure Monitoring

Natural disasters such as floods and earthquakes cause severe physical, social, and economic losses, highlighting the critical need for timely and reliable early warning systems. Conventional water level and structural health monitoring technologies are often costly, limiting deployment to high-priority infrastructure only. This paper presents the development and validation of two low-cost Internet of Things (IoT) systems for multi-hazard disaster monitoring and early warning, explicitly supporting UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities) by enabling equitable monitoring of rural or minor bridges. The proposed system achieves a significant cost reduction (approximately $300 compared to conventional systems typically exceeding $5000), highlighting its potential for scalable and sustainable deployment. The first system integrates a Raspberry Pi, Pi Camera, Lidar Lite V3, and ADXL355 accelerometer to simultaneously capture floodwater images, measure water levels, and record bridge vibrations, with distance measurements recorded at user-defined intervals and vibration data sampled up to 100 Hz. Laboratory repeatability and uncertainty analyses of the Lidar Lite V3 indicate a root mean square error of ~2.4 cm over a 0–25 cm range, demonstrating stable performance for flood monitoring and sufficient accuracy for early warning applications using low-cost sensing systems. The ADXL355 accelerometer is validated through harmonic excitation tests (0.1–2 Hz) and real earthquake recordings, confirming its suitability for low-frequency structural response monitoring. The second system combines a Raspberry Pi, an HX711 amplifier, and a CDP25 displacement transducer to measure bridge-bearing displacements up to 25 cm, with data acquisition at sampling rates of up to 80 Hz, with laboratory tests demonstrating consistent and repeatable measurements during both loading and unloading cycles. The IoT framework is resilient, incorporating solar power and local data storage to ensure operation during power or network outages. Unlike prior studies focusing on individual sensors, this work delivers a fully integrated multi-sensor platform with formalized early warning logic based on predefined thresholds. The results demonstrate the feasibility of scalable, real-time, low-cost monitoring for disaster risk reduction and infrastructure resilience, providing a sustainable solution for community-scale early warning applications.