RAPID: Low-cost Smart RF Sensor for Autonomous Floodwater Level Monitoring

Texas Tech University, Lubbock TX

Investigators

Abstract

This project aims to experimentally investigate the efficacy and reliability of low-cost portable wireless radar sensors for floodwater monitoring in realistic environment by collecting real-time on-site data of receding floodwater at multiple locations after hurricane Harvey and Irma. The proposed automatic water monitoring solution is based on portable radio frequency sensors that can be quickly deployed and integrated into a wireless sensor framework. Compared with traditional water gauging technologies that are typically based on acoustic sensors, pressure sensors, or older float and shaft angle decoders, the proposed solution does not require contact with water and thus offers larger measurement range and robustness against biological buildup and corrosion. Compared with existing microwave technologies being investigated for water monitoring, the proposed solution features much lower cost and smaller hardware size taking advantage of advanced semiconductor technology, high-linearity demodulation, adaptive DC-tuning architecture, and embedded signal processing. Furthermore, the proposed system can operate with battery power and thus does not require continuous connection to powerlines, which could be vulnerable in the event of hurricanes. Field tests of monitoring receding water after hurricane flood and comparison with results from conventional measurements will validate the proposed approach. If proven to be successful based on measurement in realistic environment, the proposed technology can be widely deployed as a sensor network in many urban, suburban, and rural communities to help government and homeowners automatically and accurately obtain real-time flood level information across a large region, without sending volunteers to dangerous areas for manual check. In addition to benefiting the national infrastructure, it will be valuable to many households for local flood monitoring and home protection. Furthermore, the big data collected from a network of such sensors will serve as a valuable resource for the understanding of the history, pathways, and extent of floodwater. It may enable strong interdisciplinary collaborative effort between researchers from electrical and coastal engineering and may result in transdisciplinary tools in sensor technology and coastal science.