Large-scale deep learning models rely on wireless networks for distributed training approaches, which are essential to meet the immense computational and data demands. However, the stochastic nature of wireless environments introduces significant challenges such as variable delays, noise interference, and packet loss, which lead to degraded gradient synchronization and hinder model convergence. In this work, we propose a novel communication-aware distributed training (CADT) framework that integrates real-time channel state information (CSI) feedback into the gradient aggregation process. Unlike conventional methods that assume static or ideal communication conditions, CADT dynamically reweights gradients from each node based on instantaneous channel quality, enabling robust aggregation under adverse wireless conditions. By dynamically adjusting the contribution of each node based on instantaneous channel conditions, CADT effectively compensates for wireless impairments, thereby ensuring more reliable gradient aggregation and significantly improving both convergence speed and final model accuracy. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN using Vision Transformer and ResNet-50 demonstrate that CADT outperforms baseline methods in terms of convergence, accuracy, and communication efficiency. In addition, we provide a rigorous theoretical analysis that establishes convergence guarantees under realistic wireless conditions, thereby advancing the theoretical foundation of distributed optimization in non-ideal communication environments.Our framework offers a practical solution for real-world scenarios such as edge computing, where communication constraints and environmental variability are dominant factors.

Amidst the rapid integration of Medical Internet of Things (MIoT) into health monitoring ecosystems, electrocardiogram (ECG)-based biometric authentication has emerged as a pivotal component in securing smart healthcare architectures, leveraging its inherent biological uniqueness and real-time monitoring capabilities. Current ECG authentication methodologies face three MIoT-specific challenges: 1) Conventional feature extraction struggles with spatial heterogeneity in multi-device 12-lead signals; 2) Single-cycle analysis lacks generalizability across physiological states; 3) Environmental noise degrades edge computing robustness. To address these limitations, this study proposes ST-AuthNet, a lightweight ECG authentication framework that synergistically integrates spatiotemporal attention mechanisms with enhanced residual networks. First, we redesign the ResNet residual block architecture by replacing conventional 1× 1 convolutional downsampling with hybrid 2× 2 average pooling and 1× 1 convolutional operations, effectively mitigating low-amplitude morphological feature loss (e.g., P/T waves) during feature map compression. Next, a multi-head cross-attention mechanism is introduced to dynamically capture inter-lead spatial correlations and intra-PQRST temporal dependencies across ECG waveforms. Finally, an adaptive threshold decision module is developed to optimize model robustness against physiological variability and environmental perturbations through dynamic classification boundary adjustment. Evaluations demonstrate state-of-the-art performance with 99.77% (CYBHI), 88.60% (MIT), 76.33% (MIT2), and 92.44% (HeartID-V) accuracy, significantly outperforming existing methods in cross-scenario biometric verification.

Voice assistants, increasingly integrated into wearable devices with limited human-computer interaction modalities, are susceptible to voice spoofing attacks. Such attacks exploit pre-recorded or synthesized voice commands to trick the assistants into executing actions unauthorized by legitimate users. In this work, we propose GyroTalk, a novel approach extracts individual and reliable features from speech movement sequences of users, using built-in gyroscopes in wearables, to differentiate between legitimate users and malicious attackers. GyroTalk is inspired by two critical insights. First, speech, as a highly intricate motor task, necessitates the synchronized coordination of multiple respiratory, laryngeal, lingual and mandibular muscles. These collective muscle movements propagate throughout the body, providing unique movement signatures. Second, the distinctive speech movement sequences of individual speakers, essential for generating specific words, can be grabbed by embedded IMU of wearables. We conduct a comprehensive evaluation of GyroTalk across various COTS Android devices, including smart phones, watches and glasses. Our experimental results demonstrate that GyroTalk can achieve a mean FAR of 2.23% and a FRR of 2.48%, even in the face of complicated voice spoofing attacks.

This paper studies a prescribed performance platoon control of connected vehicles with multiple performance threshold. A continuous gain function is introduced, combining with finite-time performance function, a novel prescribed performance control (PPC) scheme is developed, which makes the tracking error tend to the prescribed region with different threshold within the user-defined time. Then, a new finite-time reaching law is proposed, based on which, an improved finite-time sliding mode platoon control algorithm is proposed. The suggested control strategy not only ensures the two stability indexes of the platoon system, i.e., finite-time individual vehicle stability and finite-time string stability, but also realizes the asymptotic convergence of the spacing tracking error. Eventually, numerical simulations are carried out, based on which, the feasibility of the proposed scheme are validated.

Sensor and actuator attacks can alter the truth values of vehicle states and control input through false data injection, leading to performance degradation. In this paper, secure control of vehicular platoon control systems (VPCS) subject to joint sensor-actuator attacks, and the unknown disturbances is investigated. First, a novel adaptive sensor fusion algorithm is designed to estimate the actual position with smaller fusion errors. Subsequently, a predefined-time extended state observer (PTESO) is further constructed based on the estimated position to recover the continuous-time states (i.e., velocity and acceleration) and disturbances accurately within a user-defined settling time. Then, a variable exponent predefined-time sliding mode controller (VPTSMC) with few design parameters is developed to guarantee the predefined-time stability of the platoon system, namely string stability and individual vehicle stability. Meanwhile, the singularity phenomenon is also avoided. Finally, a serious of simulations are carried out to show the effectiveness of the suggested control scheme.

The fixed-time control is investigated for an uncertain two-dimensional (2-D) planar vehicular platoon system with global prescribed performance. First, a novel fixed-time prescribed performance control (FxTPPC) method is designed to guarantee the tracking errors converge to the prescribed region with small overshoot inside a predefined time. Moreover, the restriction of PPC method that relies on the initial condition of tracking errors is also released, which means global PPC can be achieved. Then, a new control scheme, by virtue of the composite fixed-time sliding mode surface, is constructed for the third-order 2-D plane platooning system, such that the tracking errors, including distance errors and heading errors, approach to a predetermined region in a given time, while avoiding the singularity problem and improving the convergence rate of the system. In addition, fixed-time string stability and reachability of fixed-time prescribed performance are also achieved. Finally, by constructing numerical simulations and experiment studies, the validity of the proposed scheme is demonstrated in the end.

This paper investigates fixed-time prescribed performance control issue of vehicular platoon subject to input nonlinearities induced by actuator dead-zone and saturation. Due to the occurrence of input nonlinearities, the spacing error increases, which may exceed the desired performance requirement. To deal with the dilemma, by merging an auxiliary variable, an improved prescribed performance control scheme is developed with a remarkable advantage that the performance boundary can be self-adjusted when actuator nonlinearities occur, while guaranteeing the tracking error tend to the predefined region in a fixed time. Then, with the help of an error transformation, an adaptive fixed-time sliding mode control approach is proposed, in which a new compensator with faster convergence is designed to eliminate the influence of actuator nonlinearities in a better way. The rigorous analysis shows that the given scheme is capable of guaranteeing fixed-time compounded individual vehicle stability, fixed-time string stability and reachability of fixed-time prescribed performance. Lastly, numerical simulations and experiments are carried out to verify the feasibility of the proposed platoon control protocol.

Multivariate time series anomaly detection is a longstanding but crucial technology, holding significant potential for system security and stability. Prior studies focus on designing sophisticated architectures, integrating advanced modules (e.g., CNN, LSTM, and Transformer), and identifying anomalies based on point-wise reconstruction errors, as it assumes anomalies cannot be correctly reconstructed. However, reconstruction-based methods are risk of over-generalization, making the assumption untenable. Moreover, the performance of complicated architectures is catastrophically overestimated by the flawed point-adjust protocol. In this paper, we propose a simple but efficient architecture comprising only feed-forward layers. The input time series is hierarchically transformed into multi-scale patches to discover complex temporal information. Triple-views are constructed to capture representation discrepancies among different views as the anomaly criterion, circumventing the overgeneralization issue of reconstruction-based methods. To further amplify the discrepancy, a loss function is designed to encourage the consistency among different views during the training phase. The proposed method is evaluated through quantitative and qualitative experiments, demonstrating its competitive performance on four benchmarks, and providing new baselines to the community without point-adjust protocol.

Studies have proven that applying patch stickers generated through adversarial training to target objects can effectively deceive classifiers or target detectors. These ’Print-and-paste’ adversarial attacks however have three shortcomings. First, touching the target object physically is required, which may be infeasible in practice. Second, stickers might be taken as evidence to identify attackers. Third, the attack effect decreases significantly in poor light, especially at long distances. To overcome above limitations, we introduce OptiCloak, a car vanishing attack, which fools the Object Detector (OD) of a vision-based autonomous driving systems with transient projection pattern. We establish three digital-to-physical mapping models to compensate the distortions caused by perspective deformation, double image and partial light reflection in real-world. Furthermore, to avoid adversarial functionality degeneration caused by the loss of patch details in long-range attacks, we utilize MeanShift Filtering to constrain the ’resolution’ of pixels in a patch during training. We propose a gradient-free patch updating approach, which utilizes ZO-AdaMM to approximate gradients and model parameters through confidence scores of OD, making OptiCloak can work well in both white-box and black-box scenarios. We deploy OptiCloak in real-world driving scenarios, and the extensive experimental results demonstrate that OptiCloak achieves similar Attack Success Rates (ASRs) as printed patches in bright environments, while significantly improving the attack performance in gloomy environments. This effect is validated across all settings, including different angles, imaging devices, and film transparency rates. In black-box settings, the average ASR can reach 71%, with a maximum attack distance of approximately 10m.

Accurate and continuous breathing rate detection is crucial as it can help people to assess their physical health and provide early warning and diagnosis for potential human diseases. Traditional breathing detection approaches involving intrusive devices are uncomfortable for long-term continuous monitoring. While contactless detection approaches utilizing radio-frequency (RF) signals or acoustic signals mainly focus on sensing the changes of chest and abdomen displacements, which are not a good indicator recording breathing event due to existing false body movements. In this article, we present Wi-Tracker, a contactless breathing detection system based on commercial off-the-shelf (COTS) smartphones, which detects breathing event through capturing the Doppler effect caused by human exhaled airflow on the reflected acoustic wave. Specifically, Wi-Tracker uses the speaker on smartphone to transmit ultrasound signals and its microphone to receive the reflected acoustic signals recording breathing event. Then, we adopt a cumulative power spectral density (CPSD) method to extract fine-grained breathing pattern from the received signals. Finally, we design algorithms to accurately capture the breathing event from the extracted breathing pattern. We evaluate Wi-Tracker with six volunteers for a period of one month. Experimental results show that Wi-Tracker is able to achieve contactless breathing detection with a mean estimation error (MEE) of 0.17 bpm, which is even better as compared to RFID-based or WiFi-based approaches.

Voice assistants are popular to wearable devices with limited input and output capabilities, however vulnerable to voice attacks, which cheat a voice assistant by playing forged voice commands without user awareness. In this paper, we propose VOGUE, which captures unique yet stable pattern of speech movement sequences of speakers with embedded gyroscope in wearable devices, to distinguish between registered legal user and malicious attackers (human or machines). The design of VOGUE is based on two key observations. First, speech, as a type of highly complex motor task, inherently requires coordinated actions of many orofacial, laryngeal, pharyngeal, and respiratory muscles, and the collective movements of muscles propagate to distant body segments. Second, to generate a certain word, the speech movement sequence of a speaker is known to be distinctive, and can be captured by inertial sensors. We implement VOGUE on three kinds of COTS android devices including smart glasses, watches and phones, and conduct comprehensive evaluation on the performances. Experimental results show that VOGUE achieves a mean false-acceptance rate (FAR) and false- rejection rate (FRR) of 2.23% and 2.48%, respectively, even under sophisticated voice impersonation attacks.

Rhythmic movement disorder (RMD) is closely related to health problems like insomnia, daytime fatigue, anxiety disorder, and depression, or even causes severe injuries resulting from the movements. To obtain detailed information of RMD related abnormal movements for early diagnosis, there are generally three categories of solutions: 1) using camera to record image data; 2) wearing various smart devices; and 3) deploying dedicated hardware to capture sensor data. But none of such are widely accepted for different reasons due to privacy, inconvenience and excessive overhead. We believe one of the essential features in a feasible solution is nonintrusiveness, in which movement data collection should be carried out without the awareness of targets. In addition, it should be fairly accurate and low cost. In this work, we propose Wi-PSG, a contactless and nonintrusive sleep monitoring system, which exploits channel state information (CSI) from existing WiFi infrastructures to detect RMD related movements. Specifically, we introduce new set of sensitivity metrics and reconstruct the collected CSI into an ideal subcarrier sensitive to all target movements. With the estimated CSI background model derived from static propagation paths, nonmovement interference can be canceled from RMD movement detection.We then train the classifier for distinguishing different kinds of RMD movements using both time and frequency features extracted from CSI signals. We implement Wi-PSG with a pair of WiFi devices and wireless access point. We evaluate Wi-PSG with nine volunteers over a one-month period. The extensive experiments demonstrate that Wi-PSG can achieve a recognition accuracy of above 92%, even under challenging scenarios.