In this paper, we present LiBre, a LiDAR-based system for real-time respiration monitoring that remains accurate and continuous even under device motion. LiBre addresses the core challenge of disentangling large-scale device movement from subtle thoracoabdominal motions by integrating three key components: (i) an Object-Centric Feature Extraction module that produces clean, geometrically consistent human point clouds and enables multi-target sensing with minimal environmental interference; (ii) a Contrastive Registration framework that combines standard Iterative Closest Point (ICP) and masked Iterative Closest Point (MICP) to decouple device motion from respiration-induced displacements; and (iii) a Directional Residual Projection strategy that automatically estimates the RoI and projects residual motion along the dominant respiratory axis, eliminating the need for manual annotation. Following a brief autonomous stationary initialization phase to establish the respiratory RoI and optimized weights, we implement a complete prototype and validate its real-time performance. Experiments with 10 participants demonstrate that LiBre achieves respiration monitoring with < 1 BPM error at sensing distances up to 4 m, under device motion speeds up to 30 cm/s, and at orientation angles up to 60°, while supporting multi-person scenarios. The system processes each frame within 120 ms, meeting the requirements for real-time mobile health monitoring in practical applications.

Federated multimodal learning is an emerging advancement in artificial intelligence, enabling the integration of data from diverse modalities while preserving data privacy. However, limited labeled data and modality heterogeneity on the clients pose significant challenges for effective federated multimodal model training. To address these challenges, this paper introduces FedAF, a novel alignment-augmented fusion framework tailored for federated multimodal learning. FedAF extracts unbiased and complementary information from multiple modalities with small data, enabling effective modality fusion and feature alignment for improving system performance. The framework introduces a three-stage strategy. First, FedAF utilizes labeled data to create unbiased anchor points, addressing disparities in client feature distributions. Second, FedAF employs a weighted enhancement contrast fusion scheme to improve feature clustering and reduce feature overlap. Finally, a multimodal semisupervised algorithm mitigates data heterogeneity and overfitting. Extensive experiments demonstrate that FedAF significantly outperforms baseline methods, showcasing its effectiveness in federated multimodal learning scenarios.

Metasurfaces are a transformative class of artificial electromagnetic materials with significant potential in communication, sensing, and security. However, existing design methods require detailed physical properties as input and lack flexibility under complex constraints, limiting their applicability. In this paper, we propose MetaGen, a general, efficient, and user-friendly generation framework for intelligent metasurface elements. MetaGen employs a fine-tuned large language model to translate natural language instructions into formatted physical properties and integrates a diffusion-based model to generate metasurface elements. Furthermore, we develop a metasurface element dataset with granular frequency sampling and extended geometric parameters to enable MetaGen to learn the complex relationships between metasurface element geometries and electromagnetic responses. Experimental results demonstrate that MetaGen effectively satisfies complex constraints, achieving electromagnetic responses closely aligned with target specifications.

As a promising paradigm, federated learning has been applied to multimodal sensing tasks due to its deployment convenience. However, the recent advances in multimodal federated learning emphasize learning a high-quality multimodal model but overlook the model usage requirements of massive unimodal clients. Moreover, the privacy risk in model sharing and client data heterogeneity impact the efficacy of federated learning. In this paper, we propose a novel multimodal federated learning system named MASA. As a departure from existing approaches, MASA simultaneously enhances the model learning efficiency of both multimodal and unimodal clients while ensuring their data privacy. First, we employ a gated cross-modal distillation scheme to achieve performance-aware knowledge transfer across modality-heterogeneous clients. To enhance the system security, MASA integrates a lightweight split-shuffle mechanism to realize the anonymization and encryption of model aggregation. Moreover, to reach personalized collaboration while protecting privacy, MASA features an attention-based spontaneous client clustering mechanism to form client cluster structures securely and distributedly. We evaluate our MASA on four public multimodal datasets for human activity recognition. The results show that our MASA outperforms leading multimodal federated learning methods on the model performance of both multimodal and unimodal clients.

After over a decade of intensive research, wireless sensing technology is nearing commercialization. However, the inherent openness of the wireless medium exposes this technology to security flaws and vulnerabilities. In this paper, we introduce RISiren to reveal the risk. RISiren is a pioneering end-to-end black-box attack system leveraging programmable metasurface with a high level of stealthiness. The key insight of RISiren lies in its ability to generate malicious multipath using metasurface, thereby disrupting wireless channel metrics influenced by genuine human activities and facilitating malicious attacks. To ensure the effectiveness of RISiren, we propose a novel metasurface configuration strategy aiming at creating human-like activities that stem from a comprehensive analysis of how human activities impact wireless signal propagation. We have implemented and validated RISiren using commercial Wi-Fi devices. Our evaluation involved testing our attack strategies against five state-of-the-art systems (including five different types of recognition frameworks) representative of the current landscape. The experimental results show that the adversarial wireless signals generated by RISiren achieve over 90% attack success rate on average, and remain robust and effective across different environments and deployment setups, including through wall attack scenarios.

The complexity and volatility of real-world radio environments often hamper wireless networks from achieving optimal performance. Recently, intelligent metasurfaces have been explored to dynamically reshape the radio propagation environment. However, existing systems are limited to standalone metasurfaces, only enabling one-time signal redirection/reshaping effects within their direct line-of-sight. They cannot effectively scale to cover larger areas. In this paper, we propose RFMagus, which employs a network of metasurfaces to overcome the limitation. We carefully optimize the configurations of the networked metasurfaces so that they can cooperatively and coherently propagate the analog signals towards the target regions. We have implemented the networked metasurfaces and deployed them in a variety of real-world environments. Experimental results demonstrate that RFMagus can effectively expand the coverage, improve the throughput, and operate transparently to different wireless standards.

The quality of a learning-based Wi-Fi sensing system is bounded by the quantity and quality of training data. However, obtaining sufficient and high-quality data across different domains is difficult due to extensive user involvement. We present CARING, a federated-learning-based framework to support collaborative and cross-domain Wi-Fi sensing. A key challenge of CARING is to allow the effective exchange and learning of knowledge across local models that are derived from heterogeneous data sources with uneven data distributions. We overcome this challenge by first extracting the activity-related representation to train local models. The shared global model aggregates received local model parameters and sends them back to individual devices for fine-tuning locally in the deployed environment. By leveraging the crowdsourced knowledge, CARING allows local models to quickly adapt to domain changes using just a few samples seen at test time. We demonstrate the benefit of CARING by applying it to activity recognition across three public datasets collected from 5 environments, 7 deployments, 31 users, and 29 activities. Experimental results show that CARING is highly effective and robust, improving the alternative approach for using single-sourced training data by up to 47%, giving an accuracy of over 80% (up to 100%) for various cross-domain scenarios.

Phased array beamforming has been extensively explored as a physical layer primitive to improve the secrecy capacity of wireless communication links. However, existing solutions are incompatible with low-profile IoT devices due to cost, power and form factor constraints. More importantly, they are vulnerable to eavesdroppers with a high-sensitivity receiver. This paper presents Protego, which offloads the security protection to a metasurface comprised of a large number of 1-bit programmable unit-cells (i.e., phase shifters). Protego builds on a novel observation that, due to phase quantization effect, not all the unit-cells contribute equally to beamforming. By judiciously flipping the phase shift of certain unit-cells, Protego can generate artificial phase noise to obfuscate the signals towards potential eavesdroppers, while preserving the signal integrity and beamforming gain towards the legitimate receiver. A hardware prototype along with extensive experiments has validated the feasibility and effectiveness of Protego.

Millimeter-wave (mmWave) technology is emerging as the most promising candidate to support a wide range of applications with high data rate demand. However, due to the high directivity of mmWaves, its links are highly susceptible to barriers from walls and the movement of people. To address these issues, this paper introduces a low-cost and reconfigurable metasurface placed in the environment to reshape and resteer mmWave beams. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, the metasurface can reshape and resteer mmWave beams, thereby enabling a fast mmWave beam relay through the wall or redirects the beam power to another direction when a human body blocks the line-of-sight path. Preliminary simulated results show our designed metasurface can perform accurate beam steering within a field-of-view of [-60°, 60°]. And even with the small-size prototype (16 × 16 array of unit-cells), the metasurface enables up to 10.8 dB signal strength improvement.

Beamforming can improve the communication and sensing capabilities for a wide range of IoT applications. However, most existing IoT devices cannot perform beamforming due to form factor, energy, and cost constraints. This paper presents RFlens, a reconfigurable metasurface that empowers low-profile IoT devices with beamforming capabilities. The metasurface consists of many unit-cells, each acting as a phase shifter for signals going through it. By encoding the phase shifting values, RFlens can manipulate electromagnetic waves to "reshape" and resteer the beam pattern. We prototype RFlens for 5 GHz Wi-Fi signals. Extensive experiments demonstrate that RFlens can achieve a 4.6 dB median signal strength improvement (up to 9.3 dB) even with a relatively small 16 × 16 array of unit-cells. In addition, RFlens can effectively improve the secrecy capacity of IoT links and enable passive NLoS wireless sensing applications.

Human respiration monitoring acts as a crucial role to indicate people's daily health. Compared with traditional respiration monitoring methods, device-free wireless respiration sensing technology is emerging as a promising modality due to the less privacy intrusive and without on-body sensors. However, due to the intrinsic nature of relying on weak reflection signals for sensing, the sensing range is limited. Meanwhile, reliable sensing performance only can be achieved when the environment with little or even no interference. In this work, we propose a WiFi-based respiration system to simultaneously enlarge the sensing range and mitigate the interference. The basic idea is to employ a reconfigurable metasurface to dynamically manipulate electromagnetic waves in the environment to achieve beamforming and beam steering. Our system thus enhances the sensing range and reduces the energy of reflected signals from interferers to ensure reliable performance. Proof-of-concept experiments demonstrate the effectiveness of our scheme.

RFID-based positioning is emerging as a promising solution for inventory management in places like warehouses and libraries. However, existing solutions either are too sensitive to the environmental noise, or require deploying a large number of reference tags which incur expensive deployment cost and increase the chance of data collisions. This paper presents CSRP, a novel RFID based positioning system, which is highly accurate and robust to environmental noise, but relies on much less reference tags compared with the state-of-the-art. CSRP achieves this by employing an noise-resilient RFID fingerprint scheme and a compressive sensing based algorithm that can recover the target tag's position using a small number of signal measurements. This work provides a set of new analysis, algorithms and heuristics to guide the deployment of reference tags and to optimize the computational overhead. We evaluate CSRP in a deployment site with 270 commercial RFID tags. Experimental results show that CSRP can correctly identify 84.7% of the test items, achieving an accuracy that is comparable to the state-of-the-art, using an order of magnitude less reference tags.

Target material identification is playing an important role in our everyday life. This paper introduces a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. Comprehensive real-life experiments demonstrate that we can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors.