While printers are widely regarded as trusted peripherals, their internal mechanical execution reveals subtle vibrational patterns that can leak document structure. We present VibraPrint, a passive mmWave sensing system that infers high-level document attributes—such as page count, content density, and template type—as well as finer-grained structural cues including line count, per-line text amount, and average word-length trends. These properties emerge because layout-induced actuation patterns imprint low-frequency vibrations on the printer chassis, which are remotely captured using a 60 GHz radar without accessing content, print commands, or firmware. To extract meaningful structure from weak and heavily filtered signals, VibraPrint employs a two-stage recovery pipeline that combines global arc fitting with rhythm-aligned segment-wise refinement. Each segment is encoded using hybrid time–frequency features and processed by a structure-aware Transformer for multi-task inference. Evaluated on 500 print jobs across 20 printer models, VibraPrint achieves a mean page-count error of 1.05, over 90% accuracy for density and template prediction, and reliable estimation of per-line structure under distance and alignment variations. These results reveal a previously unrecognized class of structural side-channel leakage inherent to everyday printing workflows.

With the increasing use of electronic devices, online handwriting verification has become crucial for biometricsbased identity authentication. Traditional methods, which rely on content-dependent verification of the writer's name, are vulnerable to forgery. This paper introduces a content-independent handwriting authentication system, Ph-Wri, designed for commodity smartphones. The core innovation is a multi-path attention feature fusion network that combines both static features (image of the handwritten text) and dynamic features (time-dependent properties during writing), to abstract the handwriting style instead of specific content for recognition, enabling robust user authentication. To extract handwriting style from dynamic writing features, we propose a polarity-aware attention strategy during training. This strategy incorporates Style Channel Attention (SCA) to capture direction-sensitive stylistic features, and Trajectory Spatial Attention (TSA) to highlight key handwriting trajectory regions. In the fine-tuning stage, the Correlation-Aware Attention (CAA) module models inter-channel structural correlations, mitigating the influence of content and enhancing style-consistent representations. By linking content-independent handwriting style to user identity, the system achieves accurate authentication. Extensive experiments on both the self-built CIEHD dataset and the public BiosecurID dataset demonstrate exceptional performance, achieving a 99% Verification Accuracy on CIEHD. Compared to state-of-theart methods that utilize only static or dynamic data, Ph-Wri significantly reduces the Equal Error Rate, showcasing the effectiveness and practicality of the proposed approach.

Accurately monitoring micron-level vibrations with sub-hertz frequency estimation error is critical for early fault detection in industrial equipment. Existing solutions either rely on powered sensors or suffer from limited accuracy in passive operation, restricting scalability and long-term deployment. We presentVibro-Stethos, a fully battery-free sensing system that accurately captures micron-level vibrations with sub-hertz frequency estimation error. It employs a dual-junction fieldeffect transistor (JFET) analog frontend to convert vibration into impedance modulation and encodes this onto passive RFID backscatter. An embedded RFID chip enables selective tag activation and provides path-invariant reference amplitude normalization. A Graph Attention Network (GAT)-based model adaptively fuses features from spatially distributed tags, enabling robust fault classification under tag sparsity and placement variation. Extensive evaluation demonstrates that Vibro-Stethos achieves amplitude measurement errors within 2$\mu$m, frequency estimation errors below 0.1 Hz, and vibration fault classification accuracy of 93.7%. Real-world deployments on transformers further confirm its diagnostic capability. Vibro-Stethos offers a practical, robust, and accurate battery-free solution for pervasive industrial vibration monitoring.

Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neural Network (GNN) methods primarily focus on homogeneous GAD and thus fail to address three key issues: (C1) Capturing abnormal signal and rich semantics across diverse meta-paths; (C2) Retaining high-frequency content in HIN dimension alignment; and (C3) Learning effectively from difficult anomaly samples with class imbalance. To overcome these, we propose ChiGAD, a spectral GNN framework based on a novel Chi-Square filter, inspired by the wavelet effectiveness in diverse domains. Specifically, ChiGAD consists of: (1) Multi-Graph Chi-Square Filter, which captures anomalous information via applying dedicated Chi-Square filters to each meta-path graph; (2) Interactive Meta-Graph Convolution, which aligns features while preserving high-frequency information and incorporates heterogeneous messages by a unified Chi-Square Filter; and (3) Contribution-Informed Cross-Entropy Loss, which prioritizes difficult anomalies to address class imbalance. Extensive experiments on public and industrial datasets show that ChiGAD outperforms state-of-the-art models on multiple metrics. Additionally, its homogeneous variant, ChiGNN, excels on seven GAD datasets, validating the effectiveness of Chi-Square filters. Our code is available at https://github.com/HsipingLi/ChiGAD.

In industrial systems, monitoring micro-scale vibrations is crucial for assessing the operational health of equipment. Existing solutions typically rely on battery-powered sensors or are constrained by LoS requirements. To address these limitations, we propose Vibro-Stethos, a micro-scale,sub-herz vibration sensing system based on battery-free tags. It innovatively utilizes the resistance characteristic of JFET in the ohmic region to convert the voltage generated by PZT vibrations into antenna’s impedance, enabling the modulation of vibration information into the backscatter signal. An integrated RFID chip enhances identifiability and controllability. Additionally, a GCN-based vibration fault recognition model ensures accurate identification of vibration states regardless of tag placement. Experimental results demonstrate that Vibro-Stethos achieves an amplitude error of 2μm and a frequency error of 0.1Hz, with a sensing range of up to 5 meters. It can also recognize seven types of vibration states with 89.3% accuracy and has proven robust and effective in real plant deployments.

WiFi channel state information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors, such as data privacy protection, hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this article, we propose Wi-SFDAGR, a WiFi-based source-free domain adaptation gesture recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods.

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known class during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based open-set gesture recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a twofold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the channel state information (CSI) ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples' neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen's effectiveness.

Handwriting recognition systems have greatly enhanced AIoT applications, especially in human-computer interaction. Wireless-based methods, favored for their non-invasive nature and ease of deployment, are becoming more common. However, existing works, which typically depend on the user’s position, often perform poorly in varied writing positions. Additionally, they do not incorporate user identity information, which could lead to security vulnerabilities by failing to reject unauthorized users. To address these issues, this article introduces RF-Eye , a system that enables contactless, position-independent handwriting recognition and user identification without prior training. Its innovative approach uses each Radio-frequency identification (RFID) tag as a unique viewpoint for observing hand movements and employs pairs of tags to track directional changes. Specifically, building upon the signal transmission model and the Fresnel Zone, we propose a novel feature, DCG , to capture changes in gesture direction and confirm its consistency across different positions. Based on DCG , we develop unique patterns for common handwriting symbols that enhance our recognition algorithm. Moreover, to strengthen the system security, we link these patterns with distinct handwriting styles through the extraction of finer-grained features, thus, preventing the misuse of the system by unauthorized users. Extensive experiments demonstrate RF-Eye ’s efficacy, which achieves recognition accuracies of 93.5%, 95.2%, and 95.8% for 26 lowercase letters, 10 digits, and 10 graphic symbols, respectively, and identifying unauthorized users with 98.6% accuracy.

Cross-Technology Communication (CTC) is an emerging technology that enables physical-layer direct communication from a WiFi sender to other Internet of Things (IoT) receivers via waveform emulation. Previous research has primarily relied on the reverse engineering to identify the suitable WiFi payloads capable of emulating waveforms similar to desired IoT packets (e.g., ZigBee). However, this approach has several limitations, including irreversibility, scalability challenges, symbol misalignment, and an over-reliance on empirical methods. In this work, we present XiTuXi, a one-size-fits-all solution to automatically achieve the CTC by taking advantage of the neural machine translation (NMT). Inspired by the task comparability between CTC and homophony-based cross-linguistic communication, we employ a well-known NMT model called Transformer to learn the rationale behind translating bit sequences for CTC without human intervention. Specifically, we introduce forward engineering as a solution to tackle the challenge of acquiring training datasets. By utilizing XiTuXi, we effortlessly achieved CTC across 30 protocol combinations (including 802.11b, g, n, ax, ah → Zig-Bee, Bluetooth, LoRa, and Sigfox), ultimately freeing experts from the tedious tasks they faced previously.

With mobile phones becoming increasingly prevalent and embedding high-quality microphones, attackers have the ability to employ these microphones to eavesdrop user’s keyboard input. However, existing work usually assumes that keystroke eavesdropping is performed against known environments and victims, which inevitably makes attack systems lack generalization. To reveal the real threat of the acoustic signal-based attack strategy, this paper proposes a keystroke eavesdropping algorithm called KeystrokeSniffer, which is robust to unknown input environments and unknown victims. In particular, to mimic the real input environment of victims, an environment estimation algorithm is first designed by extracting the timbre-related characteristics to predict the keyboard type and identifying large-size key data from collected unlabeled samples to estimate the 3D microphone coordinates. Then, by imitating unknown environments and victim data, this algorithm achieves effective keystroke eavesdropping with a small training set. By further considering the commonalities of different keystroke habits, a robust feature extraction method that reflects the keystroke location is adopted to reduce the impact of individual input habits. Extensive experimental results using various commodity smartphones indicate that the scheme is capable of predicting keyboard input accurately under different unknown scenarios. Specifically, even when both the victims and keyboards are unknown, KeystrokeSniffer can still achieve high Top-5 accuracy, reaching 79.5% in predicting keystrokes and 96.7% in predicting meaningful words, which demonstrates KeystrokeSniffer has excellent generalization capabilities. By setting different parameter values of various impact factors, e.g., noise and hand length factors, the strong robustness of the system is demonstrated, which proves that KeystrokeSniffer can violate privacy in real situations.

As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, namedBreathSign, which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance ofBreathSign. The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks,BreathSigncould achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes,BreathSigndoes not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.

Recent years have witnessed the emerging development of single-antenna wireless respiration detection that can be integrated into IoT devices with a single transceiver chain. However, existing single-antenna-based solutions are all limited by the short sensing range within 2-4 m due to noise interference, which makes them difficult to be adopted in most room-scale scenarios. To deal with this dilemma, we propose a room-scale, noise-resistance, and accurate respiration monitoring system, named Wi-Cyclops , 1 which captures CSI changes induced by respiratory movements only via one antenna on commercial WiFi devices. To push the limits of effective sensing distance, we innovatively supply a new perspective to review the CSI samples along the sub-carrier dimension. From this dimension, we find that the interrelationship between sub-carriers with different timestamps still shows a high correlation even though the SNR decreases. Based on that, we analyze the noise characteristics along the sub-carrier dimension and correspondingly design a series of denoising schemes. Specifically, we carefully design a PCA-based denoising method to filter out ambient noises. After that, considering the low distribution densities of the AGC-induced noise, we then remove it by optimizing the DBSCAN denoising method with the K-Means-based adaptive radius search. Extensive experiments demonstrate that our system can work effectively in three typical family scenarios. Wi-Cyclops can achieve 98% accuracy even when the person is 7 m away from the transceiver pair. Compared with the start-of-art single-antenna-based approaches in real scenarios, Wi-Cyclops can improve the sensing range from 3 m to 7 m, which can meet the requirements of room-scale respiration monitoring. Additionally, to show the high compatibility with smart home devices, Wi-Cyclops is deployed on seven commercial IoT devices and still achieves a low average absolute error with 0.41 bpm.

Mechanical vibration sensing is one of the most critical issues for advanced industrial IoT applications, such as troubleshooting and working condition analysis. Different from status quo solutions, the approaches based on wireless sensing have the advantages of non-invasive and easy to deploy. However, existing wireless works such as RFID and radar are limited by deployment distance or NLoS working scenarios. In this work, we propose a wireless vibration sensing system using COTS WiFi named Wi-Ear, which could monitor multiple devices in LoS or NLoS scenarios. Moreover, Wi-Ear can distinguish the vibration frequency belonging to which device without utilizing any sensor. Especially, in a weak signal scenario where the vibration signal is buried by noise in the raw CSI, Wi-Ear can also detect the vibration frequency effectively. Finally, we implement Wi-Ear with COTS WiFi devices and evaluate it with commercially available motors. For vibration sensing, Wi-Ear can sense weak vibration with an error of 0.1Hz. For device identification, Wi-Ear can identify 10 vibrating devices of 3 different types with an accuracy of 92%. Comprehensive experiments in various scenarios are conducted to show great robustness and stability.

Mechanical vibration monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing invasive solutions usually directly attach sensors to the target, which may affect the operations of delicate devices. Non-invasive video-based approaches incur poor performance in low light conditions, and laser-based ones have difficulties to monitor multiple objects simultaneously. In this work, we proposeRF-Ear$^+$+, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID.RF-Ear$^+$+could accurately monitor the mechanical vibrations of multiple devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device.RF-Ear$^+$+can measure the vibration with a frequency up to 987 Hz at a mean error rate of$0.4\%$. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. What's more,RF-Ear$^+$+can detect the rotating machinery faults based on the constructed spectrogram, which achieves$98\%$accuracy on 6 types of states. To improve the computation efficiency, we optimize the input of model in both time and frequency domains, and thus enable deployment on low-cost edge devices successfully. Comprehensive experiments conducted in lab and wild demonstrate the effectiveness of our system.

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera , a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m × 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based (an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and the situation gets even worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material identification. We design and implement our prototype on commodity RFID device. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3 cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material identification accuracy is higher than 95% even with interference from people moving around.

Mechanical vibration sensing/monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of delicate devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple (up to 8) devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 0.2cm screw loose with 90% accuracy. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. We also show that RF-ear can monitor not just the vibrations but also a large range of mechanical motions. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with outstanding performance.

Desktop is a reliable and amicable object carrier that accompanies us in our daily life, while working, eating and even entertaining. In this work, we devise a contactless but accurate object tracking system on desktop with commercial RFIDs. Comparing with conventional vision or acoustic based solutions, our system needs less computational resources and could be mucheasier for deployment. Moreover, ours could record the true positions for each device instead of the relative positions delivered in most of the previous studies. To this end, recording the user's access to the object on the desktop allows the user to interact with the smart device with simple actions. We present RFdesk, a contactless object location system that accurately locates every objects on the desktop. However, compare to tracking object withcontacted tag, several challenges are tackled before we make the system work. First of all, the signal employed for contactless tracking gets reflected twice which is thus much weaker, making the signal more susceptible to environmental noise and multipath. Another well-known challenge for contactless tracking is multi-target tracking as the signals reflected from multiple targets get mixed and interact. Extensive experiments show that RFdesk canflexibly deploy devices and tags, antennas, and localization itemscan be deployed on different planes. A median error of 3cm can be achieved for target tracking without attaching tags to the targets, even if there are 4 positioning objects on the desktop. Moreover, the average localization error can still be kept within 5.6cm outperforming the state-of-the-art systems by 100%.

In this work, we devise RF-DJ, a contactless music recognition system with the help of COTS RFID device. Since the music is caused by vibration and the vibration can influence the RF signal, our system could accurately recover the frequency of every tone, especially string instruments. Specifically, RF-DJ is immune to noises from the player/instrument motions and the ambient environment. Further more, it can recover the high frequency signal from the relatively low sampling rate data. In our demonstration, we put one tag on the surface of ukulele (not the string) and achieve the overall recognition accuracy of $93%, 90%, 87%, 81%$ when using 1,2,3,4 strings, respectively. Compared to typical machine learning based RF sensing systems, our system is model driven instead of data driven, which requires little training effort and could be applicable across different locations. Last but not the least, our system can also be used for other instruments such as zither, violin and kalimba and shows similarly good performances.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.