The rapid expansion of the low-altitude economy has propelled the increasing cross-domain applications of unmanned aerial vehicles (UAVs). During cross-domain missions, UAVs face challenges in identity management and trajectory privacy protection. The conventional centralized identity management model is susceptible to single-point failures and blockchain-based solutions suffer from performance bottlenecks. Also, the high dynamics of low-altitude networks further exacerbates the risk of trajectory data leakage. To address these problems, this paper proposes a consortium blockchain-based cross-domain authentication and flight trajectory privacy protection scheme, which includes cross-domain authentication, key agreement, dynamic identity changing, mutual authentication and domain notification methods. The proposed blockchain-based method enables trusted cross-domain identity verification, preventing attackers from reconstructing full flight paths with partial information. Formal verification based on the real-or-random model is presented to prove the semantic security of the proposed method. Informal security analysis ensures that the proposed method resists major cyber attacks. The performance of the proposed method is evaluated through simulations. The experimental results show that the proposed method has higher efficiency compared to the existing methods.

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.

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.

Currently, a major issue of WiFi-based sensing technologies is how to adapt to changes in the surrounding environment. The extreme sensitivity ofChannel State Information(CSI) makes many WiFi sensing arts frustrated when applied to the complex and unknown real world. To solve this problem, in this paper, we proposefreeEnvdesigned to automatically identify the micro-environmental changes (even tiny movements of the laptop) using WiFi devices, which can coexist with other WiFi sensing tasks with zero effort. To achieve automatic identification of micro-environmental changes, we quantify micro-environmental changes based on the physical propagation laws of WiFi signals and the main factors that affect CSI measurements. Then, we design a micro-environmental changes identification method, which determines whether the environment has changed by calculating theEarth Mover's Distance(EMD) of theProbability Density Function(PDF) of continuous CSI, without requiring training data. To remove the influence of dynamic human behaviors, we design a human dynamic detection scheme, which is achieved by obtaining the average inter-cluster distance of performingGaussian Mixture Model(GMM) clustering on CSI. We evaluatefreeEnvin real-world scenarios with six different hardware, four different scenarios, and twenty-four ways of micro-environmental changes. The results show that our method is robust to different devices and scenarios, and can achieve the average precision of 96.1% and 93.2% for micro-environmental changes identification and human dynamic behavior detection. By testing on a case study of threshold-based human presence detection,freeEnvcan effectively improve the detection performance.

Exercise speed is a crucial indicator in bodyweight exercise assessment, directly reflecting muscular strength, energy expenditure, and exercise power. Benefiting from ubiquitous infrastructure and non-intrusive properties, WiFi-based sensing has emerged as a promising technique for motion speed estimation. However, existing WiFi sensing systems are significantly influenced by user diversity, positional variations, and orientation changes, which limit their applicability in real-world scenarios. In this work, we propose a learning-based framework, named SpeedFi , to estimate exercise speed from WiFi Channel State Information (CSI). An encoder-decoder network is designed to infer body speed, while temporal–frequency constraints are introduced to model consistent speed dynamics. To enhance user generalization, a domain adversarial strategy is adopted to extract user-invariant features. Moreover, two orthogonal WiFi links are deployed to capture complementary spatial perspectives, and an attention-based fusion module is employed for effective feature integration. A physics-inspired simulation model is further developed to synthesize CSI frequency variations, guiding the design of a diversified data collection strategy covering multiple positions, orientations, and environmental configurations. We conduct extensive experiments involving 10 participants performing 3 types of bodyweight exercises across 5 distinct environments. In controlled settings, SpeedFi achieves an average speed estimation error of 0.113 m/s for peak speeds around 1 m/s. Real-time analysis indicates that the system runs at over 40 FPS on a mid-range GPU, supporting its potential for real-time deployment. Additional tests under multi-person scenarios, complex multipath conditions, and varying WiFi link configurations are conducted to assess the system’s operational boundaries and robustness, validating its applicability across diverse settings.

Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers from being falsely triggered by nearby competing users. In our work, we design a noise-resistant voice interaction interface on earphones, namedEarOE, which takes advantage of the narrow-bandwidth body channel to reconstruct high-fidelity audible speech. Although promising, directly taking the body channel as a voice interaction interface is nontrivial since the limited bandwidth of body-channel speech causes the original timbre information and linguistic content to be lost. To address these issues, we employ an electro-acoustic (EA) model for occlusion effect-based cross-channel correlation analysis. Based on that, we carefully design an attention-based encoder-decoder network to embrace cross-channel correlation for high-quality wide-bandwidth spectrum synthesis. To accommodate individual differences and improve model generalization, we implement a physics-based data augmentation strategy to expand the scale of the training dataset. Through extensive real-world experiments with 28 participants,EarOE can achieve an average Mel-cepstral distance of 8.08, an average modulation spectra distance of 0.82, and an average log-spectral distance of 11.16, outperforming existing solutions.

WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body posture. This poses a substantial challenge when users engage in non-periodic or discontinuous behaviors (e.g., stopping, starting, or turning mid-walk), which can disrupt the extraction of gait-related features and degrade recognition performance. To address this issue, we proposefreeGait, a novel approach designed to mitigate the impact of non-gait behaviors in WiFi-based gait recognition systems. Our solution models this problem as domain adaptation, where we learn domain-independent representations to isolate gait features from behavior-dependent noise. We treat human behaviors with labeled user data as source domains and behaviors without user labels as target domains. However, applying domain adaptation directly is challenging due to the ambiguous classification boundaries in the target domains for WiFi signals. To overcome this, we align the posterior distributions between the source and target domains and constrain the conditional distribution within the target domains to enhance gait classification accuracy. Additionally, we implement a data augmentation module to generate data resembling the labeled data, while supervised learning ensures distinctiveness between users. Our experiments, conducted with 20 participants across 3 different scenarios, demonstrate thatfreeGaitcan accurately predict data across 15 domains by labeling only a small subset from 6 source domains, achieving up to a 45% improvement in user classification accuracy compared to existing methods.

Recent studies show that inaudible attacks pose a non-negligible security risk to smart speakers. While several countermeasures have been proposed to detect the occurrence of the inaudible attack passively, accurately locating the attack source in 3D free space remains an unresolved challenge. Arrow is designed to bridge this gap by attempting to detect the occurrence of inaudible attacks and determine their localization simultaneously. Instead of relying on dedicated hardware components, Arrow is implemented with the microphone array widely deployed on COTS (Commercial Off-The-Shelf) smart speakers. Throughout the spatial information captured by the microphone array, Arrow establishes a spatial mapping model and derives orientation-related features to pinpoint the location of the attack source. Furthermore, to improve the robustness against co-channel interference, Arrow adopt carefully-modulated ultrasonic waveforms to achieve noise-robust attack detection. Through the above technical mechanism, Arrow can significantly improve the security level of voice assistants on smart speakers with nearly zero deployment cost. We implement a prototype of Arrow and conduct a comprehensive performance evaluation. The results show Arrow can achieve 2.5 ○ and 7 ○ error in DoA estimation for horizontal and vertical angles, respectively.

Accurately estimating the velocity (including speed and direction) of moving targets has recently attracted widespread attention in augmented reality, security monitoring and sports health. In particular, the Doppler Frequency Shift (DFS)-based velocity estimation schemes using WiFi devices have shown great potential and have been widely studied. However, previous Fast Fourier Transform (FFT)-based and path-parameter-based arts have inherent limitations in DFS estimation and, worse still, ignore the nonlinear measurement errors caused by the relative orientation between the moving target and the WiFi transceiver. The above limitations make it difficult to meet the requirements for fine-grained velocity estimation in practical applications. To cope with these limitations, in this article, we propose a learning-based velocity estimation framework, named freeDoppler , to achieve fine-grained, multi-target and orientation-independent velocity estimation. Specifically, we construct a WiFi-based Velocity Estimation Network (VEN), which leverages continuous complex-valued Channel State Information (CSI) sequences as input, to fully learn the inherent information of the Doppler effect and accurately predict velocity series. In addition, we adopt the electric field scattering model of Maxwell’s equations to construct a physics-informed CSI Generation Model (CGM), thereby generating large-scale and high-quality simulated CSI samples to improve the generalization of the VEN model. Throughout extensive real-world experiments, freeDoppler can achieve median errors of 7.98 cm/s for speed estimation, 28° for direction estimation and 35 cm for human tracking in one or two moving targets, significantly outperforming the state-of-the-art methods.

WiFi backscatter communication demonstrates significant potential for the upcoming era of low-power wireless networks. Nevertheless, due to the low-power requirement of backscatter tags, there are limitations in their capacity to eliminate conflicts, posing a significant challenge for WiFi backscatter communication in environments with ambient interference. To address that, we introduce SlickScatter, an interference-insensitive WiFi backscatter system that can retrieve WiFi backscatter signals even in the presence of unknown ambient interference. The core strategy of SlickScatter involves designating a portion of the tag data symbols as pilot symbols. This approach enables the detection of uninterfered subcarriers and the estimation of channel state information and phase errors for all symbols within a packet, facilitating the demodulation of packets affected by interference. We have prototyped and evaluated SlickScatter with 802.11g OFDM WiFi signals, demonstrating its robustness and effectiveness against unknown ambient interference. Compared with a state-of-the-art solution, SlickScatter significantly decreases the frame error rate by 50% and improves the throughput by 1.96× at a distance of 12 m.

Recently, WiFi-based gait recognition technologies have been widely studied. However, most of them work on a strong assumption that users need to walk continuously and periodically under a constant body posture. Thus, a significant challenge arises when users engage in non-periodic or discontinuous behaviors (e.g., stopping and going, turning around during walking). This is because variations of non-gait behaviors interfere with the extraction of gait-related features, resulting in recognition performance degradation. To solve this problem, we propose freeGait, which aims to mitigate the user's non-gait behaviors of WiFi-based gait recognition system. Specifically, we model this problem as domain adaptation, by learning domain-independent representations to extract behavior-independent gait features. We consider human behaviors with labels of users as source domains, and human behaviors without labels of users as target domains. However, directly applying domain adaptation to our specific problem is challenging, because the classification boundaries of the unknown target domains are unclear for WiFi signals. We align the posterior distributions of the source and target domains, and constrain the conditional distribution of the target domains to optimize the gait classification accuracy. To obtain enough source domains data, we build a data augmentation module to generate data similar to the labeled data, and use supervised learning to make the data different between users. We conduct experiments with 20 people and 3 different scenarios, and the results show that accurate predictions of a total of 15 domains data can be achieved by only collecting and labeling a small amount of data from 6 source domains, and user classification accuracy can be improved by up to 45% compared to other existing techniques.

Despite the potential for throughput enhancement with multiple tags, existing WiFi backscatter systems have been limited by inband interference among various tags. In response, we propose MultiRider, the first WiFi backscatter system that can tame in-band interference and support multi-tag parallel communication on commercial OFDM protocol. The principle behind MultiRider lies in its ability to demodulate and reconstruct tag data using just one uncorrupted subcarrier in the spectrum domain. To address the inherent challenges of preamble corruption and data collision due to in-band interference, we design three modules: 1) preamble recovery based on a concurrency-driven backscatter packet structure; 2) subcarrier-level demodulation using uncorrupted subcarriers; and 3) iterative interference cancellation for multiple tags. We prototype and evaluate MultiRider under 802.11g OFDM WiFi signals with commercial adapters and software-defined radios. Comprehensive evaluations illustrate that MultiRider can efficiently solve in-band interference. Notably, it can expand the network capacity of WiFi backscatter by 4× and use 8 channels in the 2.4GHz WiFi band for concurrent communication. Further results reveal that MultiRider can gain 10× network capacity in 35MHz bandwidth and reach 2.29 Mbps system throughput.

Smart-devices' grip-state has shown great potential to enable various intelligent applications, including virtual keyboard and automatic UI adaption. However, smartphones nowadays often lack the capability to detect the grip-state, resulting in a poor experience of human-computer interaction. To implement an effective and efficient grip-state detection, we need to tackle a number of technical challenges such as effective features extraction and fusion from multimodal data, nonalignment of different modal data, and limited labeled data availability. In this work, to address these challenges, we design a two-stage grip-state detecting system, named WowSense, for high-accuracy, real-time detection of phone's grip-states using IMU (Inertial Measurement Unit) and CS (Capacitivc Screen) data. Our system WowSense consists of the multimodal alignment stage and the grip-state classification stage. In the first stage, we employ a novel augmentation method to capture subtle features from IMU and CS data. Additionally, we utilize contrastive learning to extract consistent information across these two modalities using a large amount of unlabeled data. In the second stage, we design an attention-based classifier to capture complementary information using only a small amount of labeled data. We implement our system in OpenHarmony and our extensive experimental results demonstrate the superiority of our system, which achieves 95 % accuracy with only 40 % of the data labeled on a self-collected dataset and 92.5 % accuracy with a latency of only around 10ms when running in real-time on a phone,

Accurate and quick relative-distance measurement is crucial for supporting various intelligent transparent services, such as multi-device collaboration, screen rotation, and multi-device mirroring. Unfortunately, current methods often rely on single-modality sensing, resulting in various limitations: BLE-based and WiFi-based methods suffer from coarse-grained estimation, and ultrasound-based approaches suffer from limited sensing range. In this work, we aim at designing a distance-measurement system that enjoys long range, high accuracy, and small delay. Our designed system, named BAIR, relies on low-energy Bluetooth (BLE), acoustic sensors, and inertial measurement units (IMU) equipped on commercial smartphones for fine-grained and real-time relative distance estimation. BAIR effectively aligns multiple sensory signals with different sampling rates via the improved Kalman filter technology. To mitigate IMU's integration errors, BAIR calculates the average velocity over a preceding period and uses this, alongside accumulated velocity data from the IMU, significantly improving distance prediction accuracy. We implemented our BAIR system on smartphones and conducted extensive experiments to evaluate its performance. Specifically, in static scenarios, BAIR achieves a mean average error (MAE) of 11 cm. In moving scenarios, the cumulative distribution function (CDF) values for 95%, 80%, and 50% are 31 cm, 13 cm, and 8 cm, respectively. The memory footprint of BAIR is 16.41 MB. We release a video demo on YouTube11https://youtu.be/7Fbmn4ALaI0.

Due to contactless and convenient experiences, WiFi-based device-free fingerprints localization technologies have extensively attracted research attention. However, they are studied based on an assumption that the user is stationary and face a major challenge in the presence of users motions. That is because users motions induced CSIWiFi variations results in inconsistent location fingerprints during training and prediction, leading to system ineffective. To solve this problem, in this paper, we propose freeLoc, which aims to free users motions (even unseen) while maintaining accurate localization. Specifically, we construct a domain adaptation network that defines different users and motions as different domains, and learns domain-independent representations to extract location fingerprints independent of users motions. Unfortunately, collecting sufficient amounts of WiFi data is difficult. To reduce the cost of labeling data and ensure the performance of domain adaptation network, we utilize adversarial autoencoder to build a data augmentation module to introduce data diversity. We deploy experiments in a real scenario, and the results show that only by labeling three motions of three users, we can achieve accurate localization (the nearest locations are about one meter away) for a total of 36 domains including 6 users and 6 motions. Compared to other existing technologies, freeLoc can improve location prediction accuracy by up to 35%.

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which are vulnerable to various acoustic attacks and may suffer high-security risks. In this work, we presentAccuth$^+$+, a novel authentication system on the wrist-worn device that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks.Accuth$^+$+captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks.Accuth$^+$+is an effective complement to existing wearable authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments.Accuth$^+$+achieves over 92.85% averaged identification accuracy among 15 human participants and an averaged equal error rate (EER) of 1.91% for spoofing attack detection.

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.

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which makes these systems vulnerable to various acoustic attacks and suffer high-security risks. In this work, we present Accuth, a novel authentication system that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks. Accuth captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks. Accuth is an effective complement to existing authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments, Accuth achieves over 90% identification accuracy among 15 human participants and an average equal error rate (EER) of 3.02% for spoofing attack detection.