Ubiquitous wireless sensing has facilitated a variety of intelligent applications, yet its widespread deployment is constrained by manual calibration of the node locations, whereas existing localization methods generally depend on multiple fixed anchors for device-free topology estimation or require dedicated transceivers for device-based schemes, which limit accuracy and practical scalability. In this paper, we proposeNodeLoc, a universal two-stage node localization framework that integrates local topology estimation with global calibration through robot-assisted alignment, achieving multi-node localization with only one fixed anchor and without requiring any receivers on the robot. Specifically, the framework first establishes coarse-grained relative topology through inter-node signal measurements, and then employs the robot’s trajectories to refine global positions. Furthermore, to address inherent challenges of node localization (i.e., the non-uniqueness of local topology and the ambiguity in global position mapping), we design an optimized localization algorithm by exploring the geometric constraints and trajectory calibration of multiple receivers. A prototype system based on the proposed framework is implemented and evaluated in real-world indoor environments. Experimental results demonstrate that the system achieves 80% localization errors within 0.61mand orientation errors within 18.7°, while providing higher accuracy and significantly improved robustness and scalability compared to state-of-the-art approaches.

Traditional liquid detection approaches are often time-intensive and invasive, typically requiring the opening of containers for examination. While recent initiatives have proposed several innovative solutions, including camera-based and vibration sensor-based techniques, these approaches still face limitations in terms of convenience. The development of radio frequency (RF) technology, particularly millimeter-wave (mmWave) radar, offers a promising solution for non-invasive and contactless liquid detection. In particular, during the past few years, a number of radar-based sensing systems have been developed to detect or identify liquids. However, little work has been done on the simultaneous detection of multiple liquids. To fill this gap, we design a novel composite reflection model, which overcomes the detection challenges due to composite interference and environmental reflections, by utilizing the consistency and uniqueness of the reflection signals from multiple liquid targets. Based on the proposed model, we develop a system namedMultiScanner, which is able to detect different types of liquids in multi-target scenarios, exhibiting high location independence without the need for extensive data training. Extensive experiments validate the effectiveness ofMultiScanner, achieving up to 95.91% accuracy in detecting 10 hazardous-normal liquid combinations in 2-target scenarios. Moreover, even in more complex 5-target scenarios, an detection accuracy of 86.49% can be obtained. To the best of our knowledge, this is the first study that uses RF signals for multi-liquid detection.

This study introduces a novel approach for preventing overexertion in home fitness through fine-grained detection of respiratory parameters. To overcome the robustness limitation associated with using a composite signal for wireless sensing, we introduce an optimization-based signal separation model. This model effectively disentangles composite signals into static and dynamic components, while preserving the intricate details of target movements or activities. Specifically, by constructing a reference signal derived from the dominant static component, we eliminate time-varying phase shifts and leverage the invariant property of the dynamic component’s amplitude for precise separation. A system calledFinerSenseis developed, which is able to accurately and robustly detect fine-grained respiratory parameters such as respiration rate, depth, and inhalation-to-exhalation ratio with accuracy rates exceeding 97%, 95%, and 91%, respectively. Extensive experiments show that the developed system outperforms state-of-the-art baselines significantly, empowering users to optimize exercise intensity and duration while mitigating the risk of overexertion. We believe that this work is able to facilitate the seamless transition of wireless sensing systems from laboratory prototypes to practical and user-friendly applications.

During the last decade, there have been lots of efforts on wireless sensing using Wi-Fi signals, which can be divided into two categories, i.e., the pattern-based approach and the model-based approach. Recently, more and more attention has been paid on the model-based approach, mainly due to its superiority of no need for collecting a large dataset or retraining the model for new environments. However, existing models are mainly designed for Line-of-Sight (LoS) scenarios, which are not applicable to Non-Line-of-Sight (NLoS) scenarios, such as through-wall sensing. To bridge this gap, we put forward a through-wall wireless sensing model to reveal the sensing mechanism of Wi-Fi signals in NLoS scenarios. In particular, arefraction-awareFresnel zone model is developed by taking into account both the reflection propagation and the refraction propagation of Wi-Fi signals. For the first time, we discover that the geometric distribution of Fresnel zones becomes uneven, due to the difference in dielectric constants between the air and the wall. Specifically, some areas become denser and other areas become sparser, leading to thesqueeze effectandstretch effectof Fresnel zones. Inspired by the insight, we further put forward a new metric namedcompression-ratioto quantify the through-wall sensing capability of Wi-Fi signals. Meanwhile, a set of algorithms are developed to guide the deployment of Wi-Fi sensing systems. To validate the proposed model, we implement a through-wall respiration sensing prototype system. Experiments show that the respiration detection performance varies significantly when the user locates in different areas. Specifically, for two sensing locations (one in the compression area and the other in the expansion area) symmetrically distributed on both sides of the transceivers’ connection line, the difference in mean absolute errors (MAE) can exceed 3 times.