Respiratory monitoring using wireless technologies has garnered significant attention for its potential in healthcare, smart cockpits, and various applications. Though extensively studied, existing systems face practical challenges in adapting to new data domains without substantial customization efforts. Current solutions attempt to address this limitation through domain-independent feature extraction or cross-domain feature translation, employing either knowledge-based sensing models or data-driven neural networks. However, these approaches typically require additional data collection or model retraining for new domains, significantly hindering their practical deployment. This paper proposes RF-Carer, a fully zero-effort cross-domain respiration monitoring system. Our key innovation lies in building an explainable propagation model to transform any heterogeneous signals under unknown domains into a unified form in the signal processing layer. To further address accidental irrelevant factors, we propose to align the feature spaces while suppressing the noisy ones with contrastive learning. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to 12 domains with 57 cases like unconstrained movements, unknown users, untrained environments, etc.. To the best of our knowledge, RF-Carer is the first zero-effort cross-domain respiration monitoring work with wireless RF signals and would be a fundamental step toward real-world deployments.

This paper presents a control framework designed to enhance the stability and robustness of legged robots in the presence of uncertainties, including model uncertainties, external disturbances, and faults. The framework enables the full-state feedback estimator to estimate and compensate for uncertainties in the whole-body dynamics of the legged robots. First, we propose a novel moving horizon extended state observer (MH-ESO) to estimate uncertainties and mitigate noise in legged systems, which can be integrated into the framework for disturbance compensation. Second, we introduce a three-level whole-body disturbance rejection control framework (T-WB-DRC). Unlike the previous two-level approach, this three-level framework considers both the plan based on whole-body dynamics without uncertainties and the plan based on dynamics with uncertainties, significantly improving payload transportation, external disturbance rejection, and fault tolerance. Third, simulations of both humanoid and quadruped robots in the Gazebo simulator demonstrate the effectiveness and versatility of T-WB-DRC. Finally, extensive experimental trials on a quadruped robot validate the robustness and stability of the system when using T-WB-DRC under various disturbance conditions.

This paper proposes a novel tube-based geometric model predictive control (GMPC) framework on Lie groups for robust and real-time trajectory tracking control of robot manipulators under uncertainties. The proposed method directly computes joint torques from task-space error dynamics, eliminating inverse kinematics and ensuring efficient torque-level control. To guarantee constraint satisfaction and robustness, the tube-based GMPC is formulated to constrain the actual system trajectory within a bounded tube surrounding the nominal path, ensuring both feasibility and input-to-state stability. A disturbance observer is employed to estimate external disturbances, while a weighted whole-body controller is integrated to enhance disturbance rejection and increase the control frequency. The effectiveness of the proposed algorithm is validated through numerical simulation and experimental studies using an industrial robot manipulator. The comparative results demonstrate the stability and robustness of the proposed scheme in manipulator trajectory tracking control.

Robotic soft tissue manipulation in surgery presents significant challenges due to the tissue’s high deformability and the spatial constraints of the surgical environment. While data-driven methods for affordance estimation are common, they often face challenges with data requirements and generalization in surgical scenarios. To address these challenges, a novel framework is proposed that combines a deformation model-based shape controller with an analytical affordance estimation approach for multi-contact scenarios by employing a manipulability metric. The method leverages the deformation Jacobian matrix derived from a linearized deformation model to evaluate the manipulability of candidate multi-contact points, providing a robust and data-efficient solution. For tissue manipulation, a differentiable deformation model is employed to efficiently compute forward and backward deformation processes in real-time. Point-based visual features are constructed to represent and track the tissue deformation, enabling precise control through visual feedback. The proposed framework has been validated through simulations and physical experiments. These results demonstrate the real-time (∼ 60Hz) ability to achieve targeted configuration with high accuracy (< 1mm RMSE for each marker position) and a strong correlation (near-zero p-value) between the predicted affordance and the observed manipulation efficiency, confirming the effectiveness of our approach in soft tissue manipulation and affordance estimation.

The generalized momentum observer (GMO) is widely used for robot collision detection, but its accuracy is significantly affected by external torque residuals caused by dynamic model errors. However, existing studies lack residual compensation strategies specifically designed for the GMO framework. In particular, friction modeling errors in the velocity reversal region severely degrade detection accuracy, and their strong coupling with other residual components (e.g., gravity, Coriolis, centrifugal forces) further increases prediction difficulty. This paper proposes a machine-learning-based external torque residual prediction and compensation method tailored for GMO. First, representative input features are derived through theoretical analysis of the residual formation process, ensuring clearer mapping to residuals and improving prediction accuracy. Second, a frequency-distribution-based residual decomposition method (RD-FD) is introduced to isolate friction-related errors in the velocity reversal region, enabling targeted prediction and enhancing overall estimation accuracy. Finally, an XGBoost-based residual prediction model is developed, and a compensation framework is constructed to refine external torque estimation. Additionally, a time-varying threshold design is proposed to mitigate accuracy loss caused by friction modeling errors, further enhancing collision detection performance. Experimental results show that the proposed method accurately predicts and compensates GMO residuals, significantly enabling lower detection thresholds, which enhances sensitivity and reduces detection delay.

Vision-based grasping, though widely employed for industrial and household applications, still struggles with object stacking scenarios. Current methods face three major challenges: limited inter-object relationship understanding; poor grasping adaptation across different viewpoints; and error propagation. To address the above challenges, we propose D2TriPO-DETR, a dual-decoder transformer with three outputs, of which are object detection, manipulation relationship, and grasp detection. Specifically, a distributed attention perception module and a rotation attention invariance module are designed to address limited interobject relationship understanding and poor grasping adaptation across different viewpoints. These two modules are respectively integrated into the two parallel decoders to output the triple results simultaneously, partly eliminating task-level error propagation. Experimental results on the visual manipulation relationship dataset indicate that D2TriPO-DETR outperforms existing state-of-the-art methods across all metrics, e.g., +6.1% object detection recall, +6.7% manipulation relationship image accuracy, and +1.5% grasp detection accuracy. Extensive real-world experiments and quantitative results validate D2TriPO-DETR’s effectiveness.

Human Activity Recognition (HAR) using mmWave radar provides a non-invasive alternative to traditional sensor-based methods but suffers from domain shift, where model performance declines in new users, positions, or environments. To address this, we propose mmADA, an Active Domain Adaptation (ADA) framework that efficiently adapts mmWave-based HAR models with minimal labeled data. mmADA enhances adaptation by introducing Rényi Entropy-based uncertainty estimation to identify and label the most informative target samples. Additionally, it leverages contrastive learning and pseudo-labeling to refine feature alignment using unlabeled data. Evaluations with a TI IWR1443BOOST radar across multiple users, positions, and environments show that mmADA achieves over 90% accuracy in various cross-domain settings. Comparisons with five baselines confirm its superior adaptation performance, while further tests on unseen users, environments, and two additional open-source datasets validate its robustness and generalization.

LiDAR point cloud semantic segmentation plays a crucial role in autonomous driving. In recent years, semi-supervised methods have gained popularity due to their significant reduction in annotation labor and time costs. Current semi-supervised methods typically focus on point cloud spatial distribution or consider short-term temporal representations, e.g., only two adjacent frames, often overlooking the rich long-term temporal properties inherent in autonomous driving scenarios. In driving experience, we observe that nearby objects, such as roads and vehicles, remain stable while driving, whereas distant objects exhibit greater variability in category and shape. This natural phenomenon is also captured by Li-DAR, which reflects lower temporal sensitivity for nearby objects and higher sensitivity for distant ones. To lever-age these characteristics, we propose HiLoTs, which learns high-temporal sensitivity and low-temporal sensitivity representations from continuous LiDAR frames. These representations are further enhanced and fused using a cross-attention mechanism. Additionally, we employ a teacher-student framework to align the representations learned by the labeled and unlabeled branches, effectively utilizing the large amounts of unlabeled data. Experimental results on the SemanticKITTI and nuScenes datasets demonstrate that our proposed HiLoTs outperforms state-of-the-art semi-supervised methods, and achieves performance close to Li-DAR+Camera multimodal approaches.

The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations such as low lighting conditions, occlusions, and privacy concerns. To address these challenges, we introduce mmYodar+, a novel mmWave-based automatic human detection system. Our system processes mmWave signals to generate a 3D point cloud, which is then transformed into a 2D radar image for easier visualization and analysis. To enhance human profiling, we filter the point cloud using biometric information and expand human-related points in the image based on radar angle resolution, incorporating color to improve the differentiation. Additionally, we employ a deep mutual learning (DML) framework, enabling efficient human detection using a lightweight DNN. Experimental results show that mmYodar+ achieves an average precision of 96.29% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human detection.

More than 246 million children and adolescents suffer from school violence and bullying, e.g., verbal harassment, social harassment, and physical bullying, every year, according to a report from the United Nations Educational, Scientific and Cultural Organization. School violence and bullying severely harm the physical and emotional well-being of the victims, increasing the risks of depression, anxiety, sleep difficulties, lower academic achievement, dropping out of school, and even suicide attempts. Since school physical bullying always happens in the low-visibility areas spots of surveillance cameras, in this article, we propose to utilize Wi-Fi, a widely deployed infrastructure, to detect school physical bullying. We design residual wavelet transformer networks to conduct noise removal and action feature learning in an end-to-end manner. Besides, we propose two data augmentation methods in the temporal domain of Wi-Fi signals to simulate the different speeds and extents of bullying actions performed. Extensive evaluation of 20-paired volunteers demonstrates that 1) Wi-Fi can effectively detect physical school bullying; 2) the proposed approaches outperform long-short-time-memory networks, ResNet-1D, vision transformer, etc.; and 3) the proposed data augmentation methods can work as plug-and-play modules to improve the detection accuracy of all the above-mentioned approaches.

The classical image-based visual servoing (IBVS) methods exhibit strong robustness to robot modeling and camera calibration errors, but suffer from uncontrollable spatial trajectories and local convergence issues. Although current IBVS variants employ specialized visual features or information to improve trajectory controllability, these approaches impose restrictive geometric constraints, such as requiring coplanar feature points or configurations approximately orthogonal to the optical axis of the camera. Furthermore, despite conclusive evidence of local minima (LM) in the IBVS scheme, there is currently no solution to address this challenge. For the reason, this article first introduces a novel virtual image construction method and proposes a decoupling visual servoing control law based on virtual image, achieving explicit separation of translational and rotational error dynamics. Then, through Monte Carlo simulations, the spatial distribution patterns of local minima in IBVS are systematically studied, and an escape algorithm leveraging virtual image is further designed. To the best of our knowledge, this may be the first attempt to address the issue of local minima only through IBVS to a certain extent. Comparative simulations and experiments validate the effectiveness and superiority of the proposed decoupling control law based on virtual image, and the success rate of the local minima escape algorithm is 100% under different configurations.

In practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck across various scientific and engineering disciplines. A prominent example is the optimization of machining parameters for achieving efficient and precise robotic belt grinding (RBG). This paper presents a closed-loop machining parameter optimization approach, which comprises two key stages: forward multi-task prediction and backward multi-objective parameter optimization. In the first stage, a physics-informed neural network (PINN) method is introduced, which integrates the multi-gate mixture-of-experts multi-task learning method with an RBG mechanism model to simultaneously predict material removal depth and averaged surface roughness. In the second stage, a powered multi-objective particle swarm optimization (MOPSO) method is developed, which combines a standard MOPSO method with a non-linear Powerball technique, to efficiently optimize the RBG machining parameters with a limited number of training iterations based on the learned PINN model. Two optimal machining parameter solutions are generated and recommended for the RBG machining process. The effectiveness and superiority of the proposed closed-loop parameter optimization method are validated through comparative experiments, which demonstrate its advantages in both coprediction accuracy and optimization efficiency.

Improving global shape consistency, local feature adaptability, authenticity, and reliability in surface reconstruction is crucial for key applications such as postservice compressor blade performance evaluation, damage diagnosis, and integrated repair. Traditional surface reconstruction methods typically rely on the original nominal model as a reference, ignoring the model mismatch problem caused by service deformation. Therefore, a load-coupled deformation criterion-driven parametric surface reconstruction algorithm is proposed. Specifically, a deformation criterion is quantitatively constructed by analyzing the coupling mechanisms of aerodynamic and centrifugal loads. An adaptive gridding algorithm for disordered point clouds is then developed, which confers the grid topology dependence while maintaining that the point cloud obeys the deformation distribution. In addition, a segmented trimming bicubic-nonuniform rational B-splines surface fitting algorithm is proposed to reconstruct a smooth and reliable real blade model. Experimental results demonstrate that the load-coupled deformation criterion could effectively guide the surface reconstruction, and the root mean square error is controlled within 0.013 mm, outperforming comparing algorithms.

Hand hygiene is among the most effective daily practices for preventing infectious diseases such as influenza, malaria, and skin infections. While professional guidelines emphasize proper handwashing to reduce the risk of viral infections, surveys reveal that adherence to these recommendations remains low. To address this gap, we propose UWash, a wearable solution leveraging smartwatches to evaluate handwashing procedures, aiming to raise awareness and cultivate high-quality handwashing habits. We frame the task of handwashing assessment as an action segmentation problem, similar to those in computer vision, and introduce a simple yet efficient two-stream UNet-like network to achieve this goal. Experiments involving 51 subjects demonstrate that UWash achieves 92.27% accuracy in handwashing gesture recognition, an error of$\lt $0.5 seconds in onset/offset detection, and an error of$\lt $5 points in gesture scoring under user-dependent settings. The system also performs robustly in user-independent and user-independent-location-independent evaluations. Remarkably, UWash maintains high performance in real-world tests, including evaluations with 10 random passersby at a hospital 9 months later and 10 passersby in an in-the-wild test conducted 2 years later. UWash is the first system to score handwashing quality based on gesture sequences, offering actionable guidance for improving daily hand hygiene. The code and dataset are publicly available athttps://github.com/aiotgroup/UWash.

Contactless mmWave-based human activity recognition (HAR) is essential for various applications, yet most existing approaches often assume consistent environments. Integrating domain adaptation offers a promising solution to this challenge. This prevailing paradigm works well when the source and target data are centralized on a single server while learning to adapt. However, in more universal and practical situations, such as personal health records, users’ biometric information, and financial issues, the raw data is typically protected by different privacy-preserving policies and is stored by multiple parties. Additionally, labeling RF signals in the target domain is a non-trivial and labor-intensive task for most end-users. To address these problems, this paper introduces FMDA, a federated multi-source domain adaptation framework for mmWave-based HAR. FMDA assesses the contribution of each source and performs weighted parameter aggregation for knowledge transfer. This facilitates unsupervised training of the target HAR model without requiring access to any source domain data. Moreover, the model is optimized by minimizing the generalization gaps between the source and target models, benefiting all participants during the learning process and enhancing overall performance. Extensive experiments demonstrate the effectiveness of FMDA. The results indicate that in the target domain, FMDA achieves comparable performance to supervised learning approaches, while also enhancing the efficacy of source domain models to varying degrees.

Falls pose a significant risk to the health and wellbeing of older adults, driving the development of various fall detection systems. Existing solutions have explored wearable and vision sensors, while non-invasive RF-based approaches have raised a growing interest due to their convenience and privacy considerations. Despite major advancements in RF-based passive estimation, current approaches still face challenges in handling complex real-world scenarios. They often lack the ability to generalize to new domains (i.e., people, position, environment), and struggle to accurately detect and localize a fallen person in the presence of unknown activities from nearby objects (e.g., pet animal and robot vacuum cleaner) or persons. To address these challenges, we present mm-Fall, a novel mmWave-based non-invasive fall detection system that utilizes Range-Angle (RA) energy maps to separate and localize multiple moving targets, and further accurately estimate their states. Unlike previous approaches, mm-Fall is capable of working with new domains and effectively distinguishing falls from non-fall motions that may appear similar. Additionally, it performs well in challenging conditions, such as poor lighting and occluded scenarios. Our design of mm-Fall is evaluated in 13 environments with over 16 individuals performing 24+ types of motions. The results demonstrate an impressive average recall of 0.969 and precision of 0.996 in detecting falls, whether involving single or multiple moving targets simultaneously. The code and dataset will be made publicly available.

Multi-robot coordination in shared workspaces is prone to deadlocks, which can compromise operational capabilities and task efficiency. Accurately determining the timing and spatial locations of deadlocks is essential for effective resolution, yet remains challenging due to dynamic robot interactions and growing system complexity. To this end, a distributed deadlockaware control framework is proposed for robots to detect and avoid deadlocks while maintaining safe task execution. First, deadlocks are characterized by analyzing undesired equilibria in robot dynamics under safety constraints imposed by multiple stacked control barrier functions (CBFs). Our analysis reveals two critical properties: 1) Deadlocks occur at intersections of all active CBF boundaries, and 2) Deadlocks arise when robot stabilizing force are confined within the conical hull formed by active safety forces. These theoretical insights underpin a new detection method that identifies potential deadlocks from conflicts between safety requirements and task objectives. Furthermore, a reactive deadlock avoidance method is designed to help robots escape and prevent entry into potential deadlock regions by adaptively modulating the stabilizing force. A generalized workflow is established to systematically address deadlocks across various multi-robot tasks. Simulation and hardware experiments are conducted on robots collaborating in dense environments to validate the framework's effectiveness in preventing task failures caused by deadlocks.

Wi-Fi signals, in contrast to cameras, offer privacy protection and occlusion resilience for some practical scenarios such as smart homes, elderly care, and virtual reality. Recent years have seen remarkable progress in the estimation of single-person 2D pose, single-person 3D pose, and multi-person 2D pose. This paper takes a step forward by introducing Person-in- WiFi 3D, a pioneering Wi-Fi system that accomplishes multi-person 3D pose estimation. Person-in- WiFi 3D has two main updates. Firstly, it has a greater number of Wi-Fi devices to enhance the capability for capturing spatial reflections from multiple individuals. Secondly, it leverages the Transformer for end-to-end estimation. Compared to its predecessor, Person-in- WiFi 3D is storage-efficient and fast. We deployed a proof-of-concept system in$4m\times 3.5m$areas and collected a dataset of over 97K frames with seven volunteers. Person-in- WiFi 3D at-tains 3D joint localization errors of 9I.7mm (I-person), I08.Imm (2-person), and I25.3mm (3-person), comparable to cameras and millimeter-wave radars. The project page is at https:/laiotgroup.github.ioIPerson-in-WiFi-3D.

Wi-Fi is well-known in communication and deployment for indoor localization. Recently, Wi-Fi has been exploited for human action understanding, e.g., elder fall detection, smoking detection, and hand-gesture recognition. Many researchers have made great efforts to associate their own expertise on Wi-Fi signals with deep networks like convolutional neural networks, recurrent neural networks, and Transformers for representation learning, and demonstrate that expertise can promote action understanding accuracy. However, expert knowledge always relies on the existing personal understanding and assumptions of Wi-Fi signals, which limits the scalability of associated models and may introduce subjective bias to the models. Besides, requiring expertise raises an inescapable barrier and cost to the model design. Recent years have witnessed the great value of backbone networks, such as ResNet, in advancing the research progress in computer vision. We believe a backbone network for Wi-Fi signals will also play a crucial role. In this article, instead of proposing novel algorithms with expertise, we present that simple U-shape deep networks, such as FCN, U-Net, and U-Net++, are efficient and unified backbones for Wi-Fi-based human action understanding tasks, i.e., action recognition, action detection, and action segmentation. Results on three public data sets, Wi-Fi activity recognition, action recognition and indoor localization, and human-to-human interaction, show that all these U-shape deep networks have superior or competitive performance compared with original papers as well as state-of-the-art approaches, e.g., >97% recognition accuracy,90% segmentation accuracy. We envision this work breaks the barrier of network design and facilitates human action understanding from Wi-Fi signals.

This paper introduces a pioneering framework for music representation learning, which harnesses knowledge graph embeddings to enrich genre classification. Leveraging metadata from publicly available datasets like FMA and OpenMIC-2018, the constructed knowledge graph delineates intricate relationships among genres, artists, and instruments, offering valuable insights for genre representation. Within this framework, we propose two models tailored for distinct genre classification scenarios: fixed-set genre classification and open-set genre classification. These models exploit the knowledge graph to unveil correlations among different genres and integrate this knowledge into the audio representation. Notably, our approach is the first to merge audio data with high-level knowledge for music genre classification. Experimental results demonstrate that our proposed methods outperform state-of-the-art approaches, achieving an average genre classification accuracy of 68.07% on the FMA-medium dataset and 42.4% for open-set classification on the FMA-large dataset.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bound to the tag’s movement, e.g., the localization of tags. However, it imposes inevitable uncertainty on many sensing tasks relying on the features extracted from the RFID signals. These traditional sensing measurements limit the fidelity of RFID sensing fundamentally and prevent its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150-dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware. It requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

In this paper, we propose KGenre, a knowledge-embedded music representation learning framework for improved genre classification. We construct the knowledge graph from the metadata in the open-source FMA-medium and OpenMIC-2018 datasets, with no extra information/effort required. KGenre then mines the correlation between different genres from the knowledge graph and embeds such correlation in audio representation. To our knowledge, KGenre is the first method fusing the audio with high-level knowledge for music genre classification. Experimental results demonstrate the embedded knowledge can effectively enhance the audio feature representation, and the genre classification performance surpasses the state-of-the-art methods.

The detection of human objects can be crucial for various real-world applications, such as surveillance and autonomous driving. However, traditional vision-based approaches suffer from limitations such as low lighting conditions, occlusions, and privacy concerns. To overcome these limitations, we propose a novel automatic object detection system, called mmYodar, which utilizes millimeter-wave (mmWave) radar signals. Our system collects mmWave signals and calculates a 3D point cloud, which is transformed into a radar image for easier visualization and analysis. To improve the system's human profiling capability, we expand the corresponding points in the image with color based on the radar angle resolution. Then, a designed deep mutual learning framework is employed to detect human objects from the expanded image. Experimental results show that mmYodar achieves nearly real-time detection with an average precision of 90.35% in various scenarios, including indoor and outdoor environments, various lighting conditions, and in the presence of occlusions. These results demonstrate the effectiveness of using mmWave radar signals for reliable and accurate human object detection. Our code and dataset are available at https:llgithub.comlbrave20005lmmYodar.

Radio frequency identification (RFID)-assisted management systems have been widely applied in warehousing, logistics, retailing, etc. In these scenarios, RFID-aided applications, e.g., object tracking and human behavior sensing, rely on a high-efficiency tag reading to realize accurate analyses and timely responses. However, serious tag collisions in those large-scale RFID systems will inevitably lead to significant decreases in the tag reading rates. To meet the strict timeliness requirements of those practical applications, we aim to treat the individual reading rate for each item tag differently and focus more attention on those user-interactive ones. However, due to unpredictable user behaviors, it is impractical to infer the user-interactive tags in advance. In addition, keeping focusing on them for continuous monitoring despite user movements and multipath-prevalent environments is also challenging. To solve these problems, we propose Spotlight, the first concurrent rate-adaptive reading system in passive RFIDs. Spotlight screens the ID-agnostic user-interactive tags by proposing a multichannel feature for narrow-band RFID systems without any hardware or protocol modification and achieves rate-adaptive reading by implementing real-time MU-MIMO beamforming. Substantial experiments with 1000+ COTS RFID tags exhibit that Spotlight outperforms the commercial reader by$2.7\times $and the SDR-based reader by$6.12\times $. In addition, Spotlight first proposes the online parallel decoding method to realize concurrency among multiple users, which breaks the commercial protocol’s throughput ceiling (37%) and achieves up to 59% throughputs.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring ‘multi-path-free’ signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost – it requires no extra device. We implement CPIX and study three major RFID sensing applications: tag localization, device calibration and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of device calibration and human behaviour sensing.

The audio adversarial example has been demonstrated to be an effective attack which leads to prediction errors of the intelligent voice control system (e.g., deep neural network based speech recognition service), despite resembling a valid input to our human beings. An ideal adversarial example attack should have four major advantages, including 1) utilizing a universal adversarial perturbation against arbitrary voice commands, 2) tricking a model to get an incorrect and targeted result, 3) imperceptible to users even in a silent place and 4) validating in an over-the-air (OTA) scenario as well. However, existing studies mainly involve several but not all of these criteria. In this paper, we propose UTIO, a universal, targeted, imperceptible and OTA audio adversarial example design, which leverages one perturbation to fool a speech recognition model in OTA scenarios. Moreover, a variety of speeches can be misled to a targeted threat command imperceptibly. To harvest such benefits, we leverage two targeted loss functions to generate adversarial perturbations, and employ the psychoacoustic principle to further conceal the attack. Finally, we actively embed additional distortions, occurred during the physical propagation, in the process of perturbation generation to make UTIO still valid in an OTA scenario. Extensive experiments show that UTIO can perform 94.15% success attack rate locally, i.e., without physical propagation, while retaining 93.44% attack rate in an OTA scenario. In addition, three types of defensive strategies are also introduced to resist against our attack.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bounded to the tag’s own movement, e.g., the localization of tags. However, it imposes inevitable uncertainty to many sensing tasks relying on the features extracted from the RFID signals, which limits the fidelity of RFID sensing fundamentally and prevents its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of the RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150 dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware, requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

We study a new problem, refined localization, in this article. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber–physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, prelearning process, and high computation overhead. Also, vision-based approaches cannot differentiate objects with similar colors and shapes. This article presents a new refined localization system, called Trio, which uses passive radio frequency identification (RFID) tags for low cost and easy deployment. Trio utilizes RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., < 1 cm errors for several types of main stream tags.

As the ultra high frequency (UHF) passive radio frequency identification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily access the tags, e.g., retrieve or modify IDs or other data in the memory, via standard commands. To deal with this type of attack, we propose a physical-layer tag protection framework, namely Arbitrator2.0, that involves two operating mode, i.e., one is to passively listen on RF channels and identify unauthorized readers, the other is working as normal reader to access tag information but resilient to one-antenna eavesdropper. Our solution does not need to modify RFID tags or the underlying communication standards. In this study, we have implemented a prototype Arbitrator2.0 over the universal software radio peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. The results show that Arbitrator2.0 can effectively diminish the unauthorized access attacks and prevent eavesdropping.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user’s fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we leverage two key observations in designing RF-Mehndi. The first one is when tags are nearby, their interrogated currents can change each other’s circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user’s fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

Presents corrections to the acknowledgement section for the above named article.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multi-path-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and study two major RFID sensing applications: tag localization and human behavior sensing. CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work. It also significantly improves the quality of human behaviour sensing.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal-and multi-person cases, respectively.

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

There have been increasing interests in exploring the sensing capabilities of RFID to enable numerous IoT applications, including object localization, trajectory tracking, and human behavior sensing. However, most existing methods rely on the signal measurement either in a low multipath environment, which is unlikely to exist in many practical situations, or with special devices, which increase the operating cost. This paper investigates the possibility of measuring `multipath-free' signal information in multipath-prevalent environments simply using a commodity RFID reader. The proposed solution, Clean Physical Information Extraction (CPIX), is universal, accurate, and compatible to standard protocols and devices. CPIX improves RFID sensing quality with near zero cost - it requires no extra device. We implement CPIX and evaluate its effectiveness on improving the performance on tag localization. The results show that CPIX reduces the localization error by 30% to 50% and achieves the MOST accurate localization by commodity readers compared to existing work.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user's fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we have two key observations in designing RF-Mehndi. The first observation is when tags are nearby, their interrogated currents can change each other's circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user's fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect hand approaching behaviors by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of hand movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID, a commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios. Extensive evaluations demonstrate that APID can achieve high detection accuracy in both scenarios.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90 percent with up to ten human objects).

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

We study a new problem, refined localization, in this paper. Refined localization calculates the location of an object in high precision, given that the object is in a relatively small region such as the surface of a table. Refined localization is useful in many cyber-physical systems such as industrial autonomous robots. Existing vision-based approaches suffer from several disadvantages, including good lighting conditions, line of sight, pre-learning process, and high computation overhead. Also vision-based approaches cannot differentiate objects with similar colors and shapes. This paper presents a new refined localization system, called Trio, which uses passive Radio Frequency Identification (RFID) tags for low cost and easy deployment. Trio provides a new angle to utilize RF interference for tag localization by modeling the equivalent circuits of coupled tags. We implement our prototype using commercial off-the-shelf RFID reader and tags. Extensive experiment results demonstrate that Trio effectively achieves high accuracy of refined localization, i.e., <; 1 cm errors for several types of main stream tags.

As the Ultra High Frequency (UHF) passive Radio Frequency IDentification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily modify the tags via standard commands, e.g., IDs or other data in the memory. To deal with this type of attack, we propose a physical-layer RF signal based reader authentication solution, namely Arbitrator, that involves passively listening on RF channels, analyzing the communication signals, identifying unauthorized readers and jamming the commands from such readers. Our solution does not need to modify RFID devices or the underlying communication standards, hence fully compatible with the existing RFID infrastructure. In this study, we have implemented a prototype Arbitrator over the Universal Software Radio Peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. Our results show that Arbitrator can detect unauthorized RFID readers with high accuracy, and thus effectively diminish the unauthorized access attacks.

We provide the first solution to an important question, "how a physical-layer authentication method can defend against signal replay attacks''. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

An important function of smart environments is the ubiquitous access of computing devices. In public areas such as hospitals, libraries, and airports, people may want to interact with nearby computing systems to get information, such as directions to a hospital room, locations of books, and flight departure/arrival information. Touch screen based displays and kiosks, which are commonly used today, may incur extra hardware cost or even possible germ and bacteria infection. This work provides a new solution: users can make queries and inputs by performing in-air handwriting to an array of passive RFID tags, named RFIPad. This input method does not require human hands to carry any device and hence is convenient for applications in public areas. Besides the mobile and contactless property, this system is a cost-efficient extension to current RFID systems: an existing reader can monitor multiple RFIPads while performing its regular applications such as identification and tracking. We implement a prototype of RFIPad using commercial off-the-shelf UHF RFID devices. Experimental results show that RFIPad achieves >91% accuracy in recognizing basic touch-screen operations and English letters.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations the relative locations of objects are more important than their absolute locations. However, state-of-art relative localization methods need continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a relative localization approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device. Hence our method is called Human Movement based Relative Localization (HMRL). The basic idea of HMRL is that when people pass between reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMRL can obtain the order of tags along a specific horizontal direction. HMRL can also get the order of tags in a vertical direction using hyperbolic positioning. We implement HMRL with commodity off-the-shelf RFID devices. The experimental results show that HMRL achieves high accuracy for relative localization of passive tags.

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Smart environments and security systems require automatic detection of human behaviors including approaching to or departing from an object. Existing human motion detection systems usually require human beings to carry special devices, which limits their applications. In this paper, we present a system called APID to detect arm reaching by analyzing backscatter communication signals from a passive RFID tag on the object. APID does not require human beings to carry any device. The idea is based on the influence of human movements to the vibration of backscattered tag signals. APID is compatible with commodity off-the-shelf devices and the EPCglobal Class-1 Generation-2 protocol. In APID an commercial RFID reader continuously queries tags through emitting RF signals and tags simply respond with their IDs. A USRP monitor passively analyzes the communication signals and reports the approach and departure behaviors. We have implemented the APID system for both single-object and multi-object scenarios in both horizontal and vertical deployment modes. The experimental results show that APID can achieve high detection accuracy.

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

Identifying the object that attracts human visual attention is an essential function for automatic services in smart environments. However, existing solutions can compute the gaze direction without providing the distance to the target. In addition, most of them rely on special devices or infrastructure support. This paper explores the possibility of using a smartphone to detect the visual attention of a user. By applying the proposed VADS system, acquiring the location of the intended object only requires one simple action: gazing at the intended object and holding up the smartphone so that the object as well as user's face can be simultaneously captured by the front and rear cameras. We extend the current advances of computer vision to develop efficient algorithms to obtain the distance between the camera and user, the user's gaze direction, and the object's direction from camera. The object's location can then be computed by solving a trigonometric problem. VADS has been prototyped on commercial off-the-shelf (COTS) devices. Extensive evaluation results show that VADS achieves low error (about 1.5° in angle and 0.15m in distance for objects within 12m) as well as short latency. We believe that VADS enables a large variety of applications in smart environments.

Multi-user MIMO (MU-MIMO) is proposed in 802.11ac to achieve more than 3x faster than 802.11n. In the real world no-one gets close to theoretical speeds. The primary reason for this anomaly are the various overheads of channel access and channel state information (CSI) feedback. In order to achieve concurrent data transmission, (CSI) feedback from users is required. However, this overhead can easily overwhelm the actual channel time spent on data transmission in large-scale network. Moreover, due to spontaneous uplink traffic, which makes the problem even more challenging.

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior IDentification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, an image-based human count estimation protocol and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variance in the RSS values of the tag backscattered RF signal. Thus, based on the received RF signal, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a painting. The RFID reader periodically emits RF signal to identify all tags and the tags simply respond with their IDs via C1G2 standard protocols. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90%).

Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior Identification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, a multi-RSS based tag localization protocol, and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

Radio Frequency Identification (RFID) techniques are widely used in many ubiquitous applications. The most important usage of RFID techniques is to read the tags within a reader's interrogation area such that the objects attached with those tags can be identified. In real practice, it is common that a tag is physically in the interrogation range, but cannot be read by the reader, due to the multipath effect and other interference. This phenomenon, namely the hidden tag problem, is a big challenge to achieve high identification rate. To address this problem, most prior works depend on empirical or measurement-based methods to tune the transmission power for readers. Such a case-by-case solution is impractical for generic implementation. In this paper, we theoretically and experimentally explore the reasons why hidden tag problem occurs. To alleviate its impact, we propose a unified and measurable model, PAL, to formulate this problem and its impact. Different from previous works, our solution is generic and fully compatible with existing EPC C1G2 protocol. The analysis and measurement based on our model can help to design and deploy RFID systems with high identification rate.

Cold chain logistics is of great importance for transporting temperature and vibration sensitive products. However, fine-grained surveillance remains challenging in cold chain logistics, due to the lack of multi-dimensional information that reflects the status of monitored objects. In this paper, we propose a multi-dimensional information sensing surveillance framework, named MISS, to timely detect abnormal events that occur in cold chain logistics. The sensed information, including temperature and acceleration etc., can be integrated to provide accurate detection on the abnormal events. By adopting minimum entropy and AVC algorithms, we can classify various status in cold chain logistics. We further perform real implementations and evaluations on a prototype, and examine the effectiveness of MISS.