ABSTRACT This study proposes a federated split learning framework for large language models (FedsLLM) integrated with rate‐splitting multiple access (RSMA), aimed at enhancing the efficiency and privacy of LLM training in wireless communication systems. By leveraging low‐rank adaptation (LoRA) to distribute computational loads and a fluid antenna system to dynamically optimize channel capacity, the framework effectively reduces training latency through joint optimization of learning accuracy and communication resources. Experimental results demonstrate that the proposed framework significantly outperforms traditional time‐division multiple access including time division multiple access, frequency division multiple access (FDMA), enhanced bandwidth FDMA, and fairness‐enhanced FDMA across multiple metrics: at a transmit power of 20 dBm, RSMA reduces task completion time by 8.3%; under 20 MHz bandwidth, it achieves a 25% performance improvement; and even with a data volume of 900 Kbits, it maintains a 12% advantage. The adopted alternating optimization algorithm converges rapidly, reaching 95% of the optimal value within only 5 iterations, substantially outperforming the fixed‐point method. Overall, FedsLLM‐RSMA effectively addresses privacy, computational and communication bottlenecks in distributed LLM training. Compared to TDMA, it reduces total training latency by 28% and improves communication efficiency by 35%, while achieving higher model accuracy and faster convergence. This work provides a viable pathway for efficient and scalable deployment of LLMs in 6G networks.

With the rapid advancement of the Internet of Things (IoT) and smart environments, WiFi-based human activity recognition has made significant progress, providing non-intrusive and flexible sensing capabilities. However, most existing methods still depend on manual annotations, which severely limits their scalability and practicality in real-world scenarios where labeled data is scarce or unavailable. To address this, we propose LISAR, a novel two-stage iterative self-supervised WiFi activity recognition framework that learns from unlabeled data. Specifically, we introduce a contrastive pre-training approach by training with the proposed composite loss function, co-InfoNCE Loss, and a physics-informed data augmentation strategy to learn discriminative representations from unlabeled data. In addition, we design a Pseudo-label Confidence-guided Iterative Self-supervised Learning module, PC-ISL. Through iterative updates, this module refines pseudo-labels and enables efficient data utilization, thereby enhancing the model’s discrimination on unlabeled data. Extensive experiments demonstrate that LISAR achieves high-accuracy activity recognition while relying strictly on unlabeled data for representation learning, significantly out-performing state-of-the-art self-supervised methods.

Integrated Sensing and Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.

As a critical preprocessing step, hyperspectral image (HSI) denoising aims to improve the HSI quality for subsequent applications. While unsupervised HSI denoising methods based on Deep Image Prior (DIP) have garnered attention due to their pre-training-free advantage, existing DIP-based approaches typically utilizeL2-norm as data fidelity, making them inefficient in handling complex mixed noise. Moreover, such unsupervised methods only focus on spatial domain priors, lacking a comprehensive characterization of the spatial-spectral correlations inherent in HSIs. To tackle these limitations, we propose a robust deep recovery (RDR) model for HSI denoising with spatial-spectral total generalized variation (SSTGV) prior. Specifically, the truncated-Cauchy loss function is adopted to suppress the interference of outliers and enhance the robustness against sparse noise. Moreover, the SSTGV prior is integrated into the unsupervised RDR model, resulting in complementary effect of deep prior and handcraft prior. To solve the resulting optimization problem, an efficient ADMM algorithm is developed with convergence guarantee. Experimental results demonstrate the significant advantages of our approach in both noise suppression and detail preservation, highlighting its robustness and adaptability for varied HSI denoising applications.

Sedentary lifestyle has become a major health risk in modern society. Long sitting time can be detected by accurate recognition of sitting and standing (sit-stand) activities. WiFi-based sitting time detection has the remarkable advantage of low cost, noncontact, and privacy-protection. However, accurate recognition of sit-stand activities via WiFi signal is still facing two challenges. The first and also tougher challenge is inevitable mistakes in recognition results of traditional machine learning methods, while the second challenge is the difficulty of accurate activity segmentation before activity recognition. To the best of the authors' knowledge, few work addresses the above challenges, particularly the first challenge. A new contactless sitting time detection system is designed accordingly. The system first accurately segments all activities and removes in-seat activities. Then, the mistakes in sit-stand activity recognition are effectively corrected by a new recognition error correction method. The proposed method first creates and updates a correction benchmark that can satisfy both successive correlation and waveform symmetry between sit-stand activities. The recognition results of traditional machine learning methods are then corrected based on the latest correction benchmark. Extensive experiment results demonstrate that compared to related work, the designed system has much better accuracy on both sit-stand activity recognition and sitting time estimation. The experiment results also demonstrate the robustness of the designed system.

Recently, there has been an emerging radio frequency fingerprint identification (RFFI) technology that enhances fingerprint distinguishability by deliberately injecting an RF fingerprint into the device's Wi-Fi baseband signal. The current RF fingerprint injection methods are impractical, degrading the communication quality between Wi-Fi devices while offering limited improvements in distinguishability among a set of devices. To address these issues, we propose injecting I/Q imbalance into a short training field (STF) instead of the entire baseband signal. Our findings indicate that this method can effectively preserve the quality of the original wireless communication. Besides, a temperature-independent RF feature differential carrier frequency offset (DCFO) is proposed as an extended feature for the enhancement of fingerprint distinguishability. Building upon these, we introduce a fingerprinting scheme called PR-RFFI that generates distinguishable fingerprints for a set of devices by injecting appropriate I/Q imbalance and DCFO into the STF. Leveraging the short-term invariance of the channel, we design a practical I/Q imbalance extraction method based on the communication-quality preserving injection. Moreover, we design an optimal assignment method for I/Q imbalance and DCFO to maximize the distinguishability of RF fingerprints for all devices. Finally, we implement the PR-RFFI solution and conduct experiments in real-world and simulation scenarios. The experimental results demonstrate that PR-RFFI consistently maintains good communication quality, and achieves over 98% precision, recall, and F1-score.

Leveraging ubiquitous WiFi infrastructures, intrusion detection methods based on Received Signal Strength (RSS) offer compelling advantages, including cost-effectiveness and privacy protection. However, existing RSS-based intrusion detection solutions fall short of accurately estimating and extending the WiFi sensing bound. In this paper, we propose a novel model of motion-disturbed RSS and design an effective R-ratio indicator to extend the intrusion detection bound. Specifically, we first establish a general model of motion-disturbed RSS and derive the blocked area and reflection area in this RSS model. Then, we define the WiFi intrusion detection bound and propose a performance indicator called R-ratio to extend the bound with RSS. Furthermore, based on the statistical properties of noise, we design an efficient filter to further weaken the noise. We also propose two new methods to further extend intrusion detection bound. Extensive experimental results demonstrate that the proposed power sum ratio based intrusion detection method can approximately double the WiFi intrusion detection bound compared to other methods with raw RSS data, and our developed motion-disturbed RSS model can provide valuable insights and guidance to the intrusion detection system.

Gesture recognition provides an effective human-computer interaction that makes device control more intuitive and convenient. Although the research on mmWave radar-based gesture recognition has demonstrated promising results, existing studies have exclusively addressed the cross-domain challenge or the human activity interference problem, and no attention has been paid to the cross-domain problem in the presence of human activity interference. To address these issues, we propose a novel mmWave radar-based gesture recognition system, named GestSAM, which leverages a parameter-free attention mechanism to effectively extract gesture features that are less affected by environmental noise. By integrating this mechanism with deep learning techniques, GestSAM significantly reduces the impact of human activity interference while maintaining robust cross-domain gesture recognition performance. This approach ensures robust, high-accuracy recognition of gestures. In order to evaluate the performance of our system, we construct a dataset containing six different gesture types performed by fifteen volunteers in seven different scenarios and simulate three interference conditions. The experimental results show that under human activity interference, the model achieves average recognition accuracies of 92.79% and 94.62% in cross-user and cross-scenario, respectively.

Vein recognition technologies have become one of the primary solutions for high-security identification systems. However, the issue of biometric information leakage can still pose a serious threat to user privacy and anonymity. Currently, there is no cancelable biometric template generation scheme specifically designed for vein biometrics. Therefore, this paper proposes an innovative cancelable vein biometric generation scheme: ColorVein. Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images. This method significantly enhances the information density of vein images by transforming static grayscale information into dynamically controllable color representations through interactive colorization. ColorVein allows users/administrators to define a controllable pseudo-random color space for grayscale vein images by editing the position, number, and color of hint points, thereby generating protected cancelable templates. Additionally, we propose a new secure center loss to optimize the training process of the protected feature extraction model, effectively increasing the feature distance between enrolled users and any potential impostors. Finally, we evaluate ColorVein’s performance on all types of vein biometrics, including recognition performance, unlinkability, irreversibility, and revocability, and conduct security and privacy analyses. ColorVein achieves competitive performance compared with state-of-the-art methods.

WiFi based respiratory detection has attracted increasing attentions due to its ubiquity and convenience. In Non-Line-of-Sight (NLoS) scenarios, WiFi signals reflected from human target are blocked by obstacles and become much weaker, thus limiting the sensing range and hindering the practical deployment. The existing best respiratory detection system extended the sensing range by scaling and aligning dynamic components in WiFi signals. However, its dynamic component scaling causes the amplification of noise, while its dynamic component alignment increases computation complexity due to the traversal on all possible rotation angles. To address the above issues, in this paper we first build WiFi sensing range models for respiratory detection in NLoS scenario, find factors that limit the sensing range, and then propose a new respiratory detection system named RaliSense which can further rapidly extend the sensing range in NLoS scenario. The main idea of RaliSense is rapidly aligning dynamic components without amplifying noise, based on change direction vector and CSI ratio sum polarity of dynamic components. The proposed change direction vector is obtained by calculating the direction on which the noisy dynamic components have the maximum variance, and CSI ratio sum polarity is then obtained by summing the dynamic components which have been rotated by the change direction vector. According to the CSI ratio sum polarity, the rotation angle is quickly adjusted for aligning dynamic components. Extensive simulation and experiment results verify the effectiveness of our proposed sensing range models. The results also demonstrate that our proposed system RaliSense can effectively extend sensing range in NLoS scenario, achieving a 22.7% improvement over the best existing work but spending only a quarter of its computation time.

With the increasing connection between internet and human society, millimeter-wave radar based action recognition and user authentication exhibit remarkable prospects in security scenarios. Existing solutions usually focus on one of the tasks and mainly emphasize accuracy without reducing the inference time. In this paper, we propose a dual-task based Polymorphic Lightweight (PolyLite) RadarNet framework, in which the shared features are fed into two split streams for different tasks under joint supervision. The polymorphic concept here means that the trained network with parallel designs can be slimmed as a single-branch structure for inference. By this design strategy, we can not only efficiently extract spatial-temporal features during the training stage but also largely improve the response speed for simultaneously testing human activities and identities. Specifically, we design triple-view (TRIview) video-like data as the input by successively concatenating the range-velocity and range-angle matrices. Then a PolyLite module with linear and lightweight designs in each branch is integrated into our RadarNet framework to learn discriminative representations. Experimental results demonstrate that our approach is able to reach the accuracy over 98${\%}$within 0.21ms inference time. Especially, untrained intruders can also be successfully identified by a simple matching computation. Our code is available athttps://github.com/MagicalLiHua/PolyLite-RadarNet.

Current RF-based solutions have demonstrated that human heartbeat activity induces millimeter-scale chest displacements, changing RF reflection paths and making contact-free heartbeat monitoring possible. However, human heartbeat activity is very weak and can be hidden by out by the body movements or breathing, which poses significant challenges in accurately extracting heartbeat information. To solve this, in this paper we propose a contact-free heartbeat rate estimation system based on the periodic variation feature, namely UWHeart. UWHeart utilizes Impulse Radio-Ultra Wideband (IR-UWB) to capture reflection signals from the monitoring target and outputs the heartbeat rate. This system first filters out the noise unrelated to heartbeat through band-pass filtering. Then, we leverage the periodicity of heartbeat activity to construct autocorrelation matrix features of the denoised signal. Finally, we develop a one-dimensional Temporal Convolutional Network (TCN) model to realize the mapping between the heartbeat autocorrelation features and actual heartbeats, and accurately estimate the heartbeat rate of the monitoring target. We also implement the UWHeart system on commercial IR-UWB equipment to verify our method through a series of experiments, and the experimental results prove the effectiveness of our method.

Cooperative jamming (CJ) is a promising technique for enhancing the physical-layer security in wireless powered sensor networks. The secrecy performance of CJ-aided wireless powered sensor networks is affected by three issues including disguised eavesdropper as cooperative node, estimation error of the channel between sink node and each sensor node, and distance-related limitation on the transmit power of cooperative nodes. To address the above issues, this paper proposes a CJ-aided secure communication scheme for wireless powered sensor networks with disguised eavesdropper and imperfect channel estimation. Since each cooperative node could be the disguised eavesdropper, the proposed scheme incorporates all possible cases of the eavesdropper disguising itself as unfixed cooperative jamming node. The maximization of secrecy rate over all possible cases is formulated for designing secrecy optimization problem. The imperfect channel estimation and distance-related jamming power limitation are both integrated as constraints into the secrecy rate maximization problem. Since the original optimization problem is complex and non-convex, a two-level optimization algorithm is proposed to solve it. Simulation results show that the proposed scheme achieves significant secrecy rate improvement over typical existing schemes.

Passive human tracking plays a critical role in the field of ubiquitous sensing, offering customized services such as real-time location tracking for vital sign monitoring and motion detection. Traditional contact-free tracking systems are primarily designed for Line-of-Sight (LOS) scenarios, requiring a direct path between the radio device and the target. However, in Non-Line-of-Sight (NLOS) scenarios, where obstacles obstruct this direct path, these systems suffer from sensing failures and are unable to accurately obtain the motion trajectory of the sensing target. In this paper, we propose the UWTracking system, which utilizes the Commercial Off-the-Shelf (COTS) Impulse Radio-Ultra Wideband (IR-UWB) radars to enable precise indoor passive human tracking in both LOS and NLOS scenarios. To effectively capture the motion information of a moving target in NLOS scenarios, we present the Reconstructed Distributed- Doppler Frequency Shift (RD-DFS) features. We then binarize the RD-DFS features and design the Distance Extraction Algorithm (DEA) to obtain the target's distance in both scenarios. Subsequently, the Circle Intersection Method with Distance Stretching (CIM-DS) algorithm is developed to determine the indoor position of the sensing target, and the Scanning Angle and Velocity Particle Filter (SAV-PF) algorithm facilitates high-precision trajectory tracking. We implement a prototype of UWTracking system and conduct extensive evaluations to showcase its trajectory tracking performance under various scenarios. The results demonstrate that UWTracking achieves effective real-time tracking, with a median tracking error of 17.65 cm in the LOS scenario and 23.34 cm in the NLOS scenario, outperforming the state-of-the-art trajectory tracking systems based on COTS IR-UWB Radars.

Sedentary behaviors are shown to be hazardous to human health. Detecting sedentary behaviors in a ubiquitous way can be realized by the promising WiFi sensing technique. The accurate detection of sedentary behaviors is determined by the accurate recognition of sit-stand postural transition (SPT). However, according to our findings, SPT recognition errors are inevitable even with advanced machine-learning methods, because different SPTs may result in a similar change in WiFi channel state information (CSI). To effectively reduce SPT recognition errors, in this paper we propose RoSeFi, a robust sedentary behavior monitoring system. We first classify the errors in SPT recognition results into two categories: the errors violating SPT's consistency and the errors violating SPTs' symmetry. To correct the above errors, we reveal two inherent features in the CSI data of SPTs, i.e., contextual association and waveform mirror symmetry. Then a novel metric named WMSF is defined to quantify the degree of waveform mirror symmetry between two SPTs' CSI data. Integrating the above features, the problem of recognition error correction can be modeled as a constrained nonlinear optimization problem (CNOP). To solve the problem, we design a unified error detection/correction scheme, named UEDC, which converts the CNOP into a sequence decoding problem in Hidden Markov Model (HMM). A tailored Viterbi algorithm combined with WMSF is proposed to detect and correct the errors simultaneously. The experimental results show that RoseFi reduces 60-82% SPT recognition errors, gains 15-20% relative improvement in the accuracy of SPT recognition, and eventually reduces the sedentary time estimation errors by 10%-20%, compared with typical existing systems. In addition, our error correction method can be adapted to most existing machine learning based human action recognition methods, effectively improving their performance.

Contactless WiFi based human action recognition exhibits remarkable prospects in the fields such as human-computer interaction and smart home. However, domain dependency restricts its generalization into the real-world deployment. Since it is expensive to label enough new data for retaining a model, it is beneficial to explore few-shot learning for cross-domain sensing with limited target labels. Nevertheless, there are two challenges to be addressed. The first challenge is how to select a suitable dataset from a series of available source domains to prevent negative transfer. The second is to mine action-related characteristics by the feature learning model for the following effective knowledge transfer. In order to tackle the above challenges, we present a cross-domain sensing framework named CDFi, which consists of Nearest Neighbor based Domain Selector (NNDS) and Fine-to-Coarse-Grained Transformer Network (FCGTN). NNDS is proposed to evaluate the source-target domain similarities by measurements among local and global feature distributions. Besides, FCGTN embeds convolution map based hierarchical transformer structures and the modified linear layer into an end-to-end deep network, which can quickly adapt to the unseen domain by few samples. Comprehensive experiments show that CDFi can effectively realize cross-domain action recognition, and achieve about 4 cross-scene cases, respectively, compared to the state-of-the-art.

Traditional indoor location information management methods based on centralized servers have problems such as safe and reliable transmission, personal privacy leaks, location information tampering, and computing and storage loads. These problems have seriously affected the development of personalized services based on indoor location information. In this paper, a novel mobile blockchain-enabled federated learning (MBFL) information management framework for indoor positioning is presented, comprising the mobile blockchain model, the federated learning (FL) model, and the InterPlanetary file storage model. Then, we design the MBFL algorithm, establishing a robust foundation for collaborative model training, efficient block mining, and secure data storage. Moreover, we derive training and mining latency as well as the individual user rewards, and formulate latency-limited resource allocation strategies as a non-cooperative game. We propose an efficient alternating iterative algorithm to achieve the Nash equilibrium of this game. Numerical results demonstrate that the proposed alternating iterative algorithm achieves rapid convergence and strikes an effective balance between economic and time efficiency. Furthermore, when confronted with model poisoning attacks, the MBFL algorithm exhibits superior security performance compared to the traditional FL algorithm. Future work will focus on adapting the MBFL framework for various indoor environments and enhancing consumption and computational efficiency with hybrid consensus mechanisms.

Impulsive noise is always present in real-world image Compressive Sensing (CS) acquisition systems, where existing CS reconstruction performance may seriously deteriorate. In this article, we propose a robust CS formulation for image reconstruction to suppress outliers in the presence of impulsive noise. To address this issue, we consider a novel truncated-Cauchy loss function as the metric of residual error to elevate the reconstruction robustness. Specifically, we design a complementary priors model to incorporate nonconvex nonlocal low-rank prior and deep denoiser prior for high-accuracy image reconstruction. By means of the half-quadratic optimization theory and generalized soft-thresholding technique, we also develop an alternative optimization algorithm for solving the induced nonconvex optimization problem. Numerical simulations demonstrate the robustness and accuracy of the proposed robust CS method compared to some recent CS methods for image reconstruction in impulsive noise.

As two important contents in WiFi-based action perception, detection and recognition require localizing motion regions from the entire temporal sequences and classifying the corresponding categories. Existing approaches, though yielding reasonably acceptable performances, are suffering from two major drawbacks: heavy empirical dependency and large computational complexity. In order to solve these issues, we develop LiteWiSys in this article, a lightweight system in an end-to-end deep learning manner to simultaneously detect and recognize WiFi-based human actions. Specifically, we assign different attentions on sub-carriers, which are then compressed to reduce noise and information redundancy. Then, LiteWiSys integrates deep separable convolution and a channel shuffle mechanism into a multi-scale convolutional backbone structure. By feature channel split, two network branches are obtained and further trained with a joint loss function for dual tasks. We collect different datasets at multi-scenes and conduct experiments to evaluate the performance of LiteWiSys. In comparison to existing WiFi sensing systems, LiteWiSys achieves promising precision with lower complexity.

Millimeter-wave radar based human activity recognition (RADHAR) exhibits remarkable prospects in the field of device-free sensing. However, most existing RADHAR systems only focus on performance improvement, failing to simultaneously lighten the network parameters. In this paper, we propose a dynamic lightweight SlowFast network named DyLiteRADHAR, which can efficiently extract spatial-temporal features and largely reduce the resource consumption for human activity recognition. Specifically, we design triple-view signal maps (TRIview) as the input by successively concatenating the range-velocity, range-azimuth and range-elevation matrices. Then dynamic lightweight network is presented to learn discriminative representations which integrates dynamic convolution and lightweight shuffle net structure into the SlowFast framework. Experimental results demonstrate that the proposed approach DyLiteRADHAR is able to achieve superiority performance with limited computation complexity.

Heart rate provides aln important reference for human physical conditions and psychological changes. MmWave-based heart rate estimation has increasingly attracted attention in recent years due to its non-intrusiveness and cost-effectiveness. However, when the subject locates far away from the mmWave radar and also deviates from it, the low accuracy of heart rate estimation becomes a major concern. This paper presents MMHeart, a new heart rate estimation and heartbeat waveform reconstruction system based on MIMO mmWave radar. In order to effectively improve the accuracy of heart rate estimation, MMHeart first calculates appropriate range bins based on positioning results, then estimates candidate heart rates in all channels, removes abnormal candidates based on spectrum kurtosis, and finally estimates the heart rate by clustering the remaining candidates. Then in order to reconstruct more accurate heartbeat waveform, MMHeart first segments the signal by trough detection, then resamples heartbeat waveform template, and finally fine-tunes the start and end points of each segment. Our extensive experiments show that in long-range and large-deviation scenarios, MMHeart can improve the accuracy of heart rate estimation by at least 54.1% compared to mmEGC and PiVimo, while it can improve the accuracy of cardiac cycle duration by 52.2% compared to mmEGC.

With the growing popularity of Internet of Things (IoT) systems, device-free action recognition begins to attract extensive attention due to its friendly feasibility in broad applications, such as human–computer interaction and smart elderly care. Considering abundant information in the vision modality, existing methods adopt the cross-model methods for performance enhancement. However, the dependency of synchronous multimodal data in the collection and recognition stage brings into the vision weaknesses, such as sensitivity to occlusion and privacy invasion. In this article, we integrate multi-attention structure and auxiliary video information into a novel end-to-end deep learning framework named MuAt-Va, in which video soft labels learned in advance are utilized to teach the multi-attention WiFi feature training process without vision information involved during the test. Specifically, in order to enlarge the application scope and reduce the data cost, we beforehand acquire videos under a satisfactory condition only once, and then leverage teacher–student mechanism to guide the WiFi stream. Instead of straightforwardly concatenating multiantenna channel state information (CSI) from homogeneous wireless signals as previous works, we design a CSI subcarrier-wise, temporal-wise, and view-wise attention module to assign different weights on the basis of data characteristics for the sensing task. Our experiments with multiple subjects data in two scenes demonstrate that MuAt-Va can accurately recognize human actions with more superior performances.

With the widespread deployment of commercial wireless devices, researchers begin to focus on device-free sensing tasks. In the field of action perception, existing WiFi-based sensing works mostly follow the framework in which action instances of channel state information (CSI) are first extracted and then classified. As for the part of human action detection, a majority of works adopt threshold based sliding window or frame-by-frame detection methods. However, it is hard for the former approach to set a reasonable threshold for all samples. As for the latter, it costs a relatively substantial amount of labor to label each moment of the time sequences. In order to overcome the above problems, we design an end-to-end context-aware faster region-based convolutional neural networks (RCNN) framework named Wisense to simultaneously detect the temporal boundaries as well as classify the actions. More specifically, Wisense consists of backbone net, region proposal net (RPN), pooling layer, and the prediction net, which directly regresses the action location along the time axis and classifies the action types. For the sake of wireless signal temporal detection, we transform the input into 1-D feature map and extract multiscale 1-D anchors. Besides, in order to sufficiently mine the context information, we extend the boundaries of region proposals and further establish the temporal pyramid features. Experimental results conducted in three indoor scenes validate the effectiveness of our proposed Wisense.

Respiration is a vital indicator of the state of the human body. Monitoring human respiration enables the realization of a variety of intelligent applications, including smart medical and sleep monitoring. Traditional methods that are dependent upon wearable devices are more costly and inconvenient for users. Recent studies have evidenced that low-cost commodity WiFi devices can be used to accomplish contactless respiration monitoring. In this article, we present BreatheBand, a fine-grained and robust respiration monitoring system based on commercial WiFi signals. We first remove the time-varying phase shift in the channel state information (CSI) by developing the Multi-antenna CSI–Subpopulation Genetic algorithm. Then we separate human respiratory components from WiFi signals by employing subcarrier selection and Independent Component Analysis. Next, applying a Mixed Cluster Gaussian–Hidden Markov Model, we generate a respiration signal resembling that of wearable devices. Finally, we integrate the BreatheBand system into commercial WiFi infrastructure. The results show that the BreatheBand’s respiration signal is remarkably identical to the signal collected by the wearable device in various scenarios. In particular, the mean absolute error of the BreatheBand’s respiration rate is approximately 0.1 bpm, outperforming state-of-the-art algorithms.

Video surveillance systems have demonstrated their great importance in security protection these years. However, due to limited budget, installing cameras in every place of surveillance region is not practical and then blind areas become inevitable. As a result, eliminating blind areas with the lowest cost has become a tough challenge. To the best of our knowledge, this is the first work that fixes the blind spots of video surveillance systems based on WiFi sensing technique. By taking existing WiFi infrastructure as the sensing device, this paper attempts to eliminate blind spots with tiny hardware cost. Moreover, in order to completely fix blind area with minimum loss of video communication QoS, the WiFi sensing device’s location boundary that satisfies the above objective is modeled and estimated. To that end, a visitor-disturbed channel model is first derived for precisely describing the inherent relation between the appearance of visitor and the change of wireless channel. Then a location boundary model satisfying both blind-area elimination and QoS maximization is further derived. Based on the derived model, a practical system is designed to estimate the real location boundary. The simulation and experiment results have not only verified the correctness of our derived location boundary model, but also showed its good performance on both blind-area elimination and communication QoS optimization.

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Due to robustness against multi-path effect, channel state information (CSI) of Orthogonal Frequency Division Multiplexing (OFDM) systems is supposed to provide accurate distance measurement for indoor localization. However, we find that the original CSI ranging model is biased, so the model cannot be used to directly derive Cramer-Rao lower bound (CRLB) of positioning error for CSI-ranging based localization scheme. In this paper we first analyze the estimation bias of the original CSI ranging model according to indoor wireless channel model. Then we propose a negative power summation ranging model which can be used as an unbiased ranging model for both Line-Of-Sight (LOS) and Non-LOS scenarios. Subsequently, based on the proposed model, we derive both the CRLB of ranging error and the CRLB of positioning error for CSI-ranging localization scheme. Through simulation we validate the bias of the original ranging model and the approximately zero bias of our proposed ranging model. Through comprehensive experiments in different indoor scenarios, localization errors by different ranging models are compared to the CRLB, meanwhile our proposed ranging model is demonstrated to have better ranging and localization accuracy than the original ranging model.

A large-scale fully digital receive antenna array can provide very high-resolution direction of arrival (DOA) estimation, but resulting in a significantly high RF-chain circuit cost. Thus, a hybrid analog and digital (HAD) structure is preferred. Two phase alignment (PA) methods, HAD PA (HADPA) and hybrid digital and analog PA (HDAPA), are proposed to estimate DOA based on the parametric method. Compared to analog PA (APA), they can significantly reduce the complexity in the PA phases. Subsequently, a fast root multiple signal classification HDAPA (root-MUSIC-HDAPA) method is proposed specially for this hybrid structure to implement an approximately analytical solution. Due to the HAD structure, there exists the effect of direction-finding ambiguity. A smart strategy of maximizing the average receive power is adopted to delete those spurious solutions and preserve the true optimal solution by linear searching over a set of limited finite candidate directions. This results in a significant reduction in computational complexity. Eventually, the Cramer-Rao lower bound (CRLB) of finding emitter direction using the HAD structure is derived. Simulation results show that our proposed methods, root-MUSIC-HDAPA and HDAPA, can achieve the hybrid CRLB with their complexities being significantly lower than those of pure linear searching-based methods, such as APA.

In wireless sensor networks, network lifetime is among the most important criteria. Network lifetime mainly depends on the link scheduling established at the Medium Access Control layer. Indeed, the avoidance of transmission conflicts enables energy savings since there are no message retransmissions. We are interested in deterministic allocation of the wireless medium for data collection in tree based sensor networks. In this paper, we consider a generalization of the distance 2-edge coloring problem, in which transmission and interference edges are taken into account. We propose a distributed algorithm for this problem, called D2EC, which ensures that conflicts are avoided. We also carry out simulations to compare D2EC with a random allocation strategy of the wireless medium. The simulation results show that there is a significant reduction on packet loss by using D2EC. Moreover, D2EC extends the lifetime of 250% in the best case regarding the random allocation of the wireless medium.

Localization is a fundamental issue which is studied for many wireless networks applications like robotic networks. However, the existing range-free indoor localization algorithms haven't provided sufficient accuracy. In this paper, we propose a Selective 3- Anchor DV-hop algorithm with the following idea: the normal node first selects any three anchors to form a 3-anchor group, then it calculates the candidate positions based on each 3-anchor group, and finally according to the relation between candidate positions and the minimum hop counts to anchors, the normal node chooses the best candidate position. Simulation results show that our new algorithm achieves a better precision, compared with the existing relative algorithms.