The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to any one of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of$\text{7.61}~cm$, a mean orientation error of 9.83 degrees, a mean tracking error of$\text{6.12}~cm$, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to anyone of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of 7.61 cm, a mean orientation error of 9.83 degrees, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. The design of RED further addresses practical issues, such as parameterizing the RUM model, making it robust to dynamic and noisy environments, and considering how the doppler shift may affect our system. We implement RED with COTS RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.6 percent and the detection latency is 0.68 seconds in average.

RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.

The development of Internet-of-Things (IoT) technologies in recent years brings us unprecedented opportunities for innovations in the power industry. This demo abstract introduces our research and practice with Pavatar - IoT for the power industry. Pavatar includes a series of system deployments in the core sections of Global Energy Internet (GEI), for the purposes of automatic surveillance and remote diagnosis of ultra-high-voltage converter stations (UHVCSs). Pavatar incorporates technologies like lower-power or battery-free sensing, cross-technology communication, edge computing, machine learning, and enhances the user experience with 3D virtual reality. The deployed system significantly reduces the manpower cost and enhances the operational efficiency of the UHVCS.