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

本项目旨在通过对飓风“哈维”和“厄玛”过后多个地点后退洪水的实时现场数据采集，实验考察低成本便携式无线雷达传感器在现实环境中进行洪水监测的有效性和可靠性。提出的自动水监测解决方案基于便携式射频传感器，可以快速部署并集成到无线传感器框架中。与传统的水测量技术（通常基于声学传感器、压力传感器或老式浮子和轴角解码器）相比，该解决方案不需要与水接触，因此具有更大的测量范围，并且具有抗生物积聚和腐蚀的坚固性。与目前正在研究的用于水监测的微波技术相比，该方案利用先进的半导体技术、高线性解调、自适应直流调谐架构和嵌入式信号处理，具有成本更低、硬件尺寸更小的特点。此外，该系统可以使用电池供电，因此不需要连续连接到电力线，而电力线在飓风发生时可能很脆弱。对飓风洪水后退潮的现场监测试验以及与常规测量结果的比较将验证所提出的方法。如果在现实环境中被证明是成功的，那么该技术可以作为传感器网络广泛部署在许多城市、郊区和农村社区，帮助政府和房主自动准确地获得大范围内的实时洪水水位信息，而无需派遣志愿者到危险地区进行人工检查。除了对国家基础设施有利外，对许多家庭来说，它对当地的洪水监测和家庭保护也很有价值。此外，从这些传感器网络收集的大数据将成为了解洪水历史、路径和范围的宝贵资源。它可能使来自电气和海岸工程的研究人员之间的跨学科合作努力成为可能，并可能导致传感器技术和海岸科学的跨学科工具。

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.