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Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring. 

A novel framework and related methodologies are described to leverage RF power for building intelligent and battery-free devices with communication and computation capabilities. These passive devices are envisioned to make significant impact for the popular vision of smart dust due to extreme low power operation. The communication framework relies on tag-to-tag backscattering with very limited energy resources. The computing framework relies on a novel AC computing methodology that facilitates local data processing with an order of magnitude less power consumption. These enabling technologies, as described in this paper, revitalize the concept of smart dust with significant impact on various application domains such as smart spaces, implantable devices, and environmental/structural monitoring.