Massive MIMO has emerged as a cornerstone technology for 5G-Advanced and future 6G networks, yet its practical deployment remains limited by hardware cost and power consumption. Switch-based architectures, which share a small number of RF chains among many antenna elements, provide a scalable alternative, but create a new bottleneck: only a subset of antennas is observable at any given moment, leaving the channel state of the remaining elements unknown. Lacking this information prevents the system from exploiting advanced physical-layer functions such as digital beamforming or multi-stream MIMO. In this paper, we present ARGUS, a generative channel reconstruction framework that infers the CSI of unobserved antennas from partial observations. The key idea is that all antenna responses are governed by the same underlying wireless propagation environment, enabling the task to be formulated as a generative inference problem. We employ a variational autoencoder to capture the latent spatial structure and reconstruct unobserved channels through sampling. Extensive experiments show that our reconstructed CSI incurs less than 2.5% achievable rate loss, and real-world measurements demonstrate a more than 90% antenna-selection match rate, confirming the practicality of the proposed approach.

The Industrial Internet of Things (IIoT) has been widely deployed to enable real-time monitoring and automation. Within IIoT-driven production, supply chain management plays a critical role, necessitating verifiable provenance to ensure the authenticity and traceability of goods across multi-stakeholder networks. While blockchain provides a tamper-proof foundation, traditional storage structures suffer from unsecured data integrity, poor query efficiency, and scalability over provenance data. To address these challenges, we propose vProChain, an efficient provenance verification system to support verifiable and parallel queries over graph-structured provenance data. First, we design an Adaptive DAG Verkle Tree (ADVT) that deterministically maps supply chain dependencies into a graph-native authenticated data structure, enabling constant-size proofs and low-overhead verification. Second, we introduce the Merkle Inverted Patricia Trie (MIPT) to facilitate fast, verifiable multi-dimensional Boolean queries. Third, we develop a parallel provenance query algorithm that accelerates multi-hop path retrieval via consistent hashing and weighted bipartite matching. Finally, formal security analysis and extensive empirical evaluations demonstrate that vProChain can provide provable cryptographic guarantees for the soundness of provenance proofs and the completeness of query retrievals, while achieving high query efficiency in a large-scale IIoT environment.

This paper presents ArmNet, a mobile sensing system for capturing the trajectory of the wrist using measurements from wrist-worn IMU devices, specifically targeting robust interaction within Internet of Things (IoT) ecosystems. Unlike existing solutions that directly map IMU data to joint positions, ArmNet integrates physical kinematic constraints with neural network modeling. This hybrid approach is crucial for resource-constrained IoT devices where computational overhead and sensor limitations are primary concerns. Specifically, kinematic priors capture spatial dependencies between the elbow and wrist, generating physics-guided intermediate features that reduce the solution space. These features, together with raw IMU data, are fed into a recurrent neural network to learn joint displacement vectors, which are then integrated into continuous trajectories. Extensive experiments show that ArmNet achieves robust and accurate arm tracking, generalizing well across users and motion patterns, thereby enabling a new modality for seamless human-computer interaction in smart environments.

Facial Expression Recognition (FER) serves as a foundational sensory interface for Human-Centric IoT, supporting applications such as smart healthcare monitoring and affective intelligent environments. However, real-world performance is often hindered by theSemantic Gap, where models confuse visually similar expressions that arise from fundamentally different physiological muscle movements. To bridge this gap, we propose the Physical-Semantic-Aware Multimodal Framework (PSM-FER), which introduces 3D Blendshape (BS) coefficients as explicit physical priors to encode high-level muscle motion semantics. Our framework utilizes two synergistic pathways:Direct Physical Gating(DPG) for robust feature modulation andSemantic-Guided Spatial Attention(SGSA) for anatomical spatial recalibration. Additionally, an auxiliary physical regression task enforces anatomical consistency by regularizing the latent features to follow underlying biomechanical laws. Extensive experiments on the RAF-DB dataset demonstrate that PSM-FER achieves an accuracy of 92.37%, establishing a robust and interpretable foundation for affective sensing in complex IoT ecosystems.

In an era marked by technological innovation, visual applications have become ubiquitous in everyday life. Harnessing the power of computer vision, these applications process and interpret video data from edge cameras, facilitating tasks such as object detection and vehicle counting. Yet, implementing complex deep learning models on cameras with limited computational capacity poses significant challenges. Furthermore, the bandwidth constraints and fluctuating nature of wide-area networks present substantial difficulties for video analysis systems dependent on cloud computing. This paper first characterizes the relationship between different parameter combinations (such as frame rate and resolution) and video analysis accuracy through offline analysis. It proposes a video stream analysis configuration selection scheme, SPStream, for slowly changing scenes, and a configuration file switching strategy, SPStream+, for rapidly changing scenes. These strategies use idle resources at the camera edge end to select the optimal configuration in real-time, adjust video encoding quality, and dynamically switch configuration files based on the changing states of object motion. Finally, a real-time video stream analysis system for vehicle counting and pedestrian detection suitable for both scenarios is designed, which saves bandwidth to the greatest extent while meeting the accuracy requirements of users and achieving high accuracy of video analysis.

The lack of actionable guidance is a fundamental limitation in Action Quality Assessment (AQA), as traditional methods provide overall scores without offering specific insights for improvement. Moreover, existing interpretable approaches often rely on expensive supervised spatial annotations or yield noisy, unsigned saliency maps. To address these challenges, we propose Learning Interpretability Based Supervoxels (LIBS), a novel framework for generating instructional feedback. Distinguishing itself from fully supervised methods, LIBS employs an unsupervised soft-clustering mechanism to segment videos into coherent supervoxels without requiring pixel-level mask annotations. This allows for scalable, fine-grained spatio-temporal analysis while preserving action continuity. Furthermore, we introduce a sensitivity propensity analysis to quantify the contribution of each supervoxel. Unlike traditional attribution methods, this mechanism explicitly decomposes the quality score into positive (strengths) and negative (flaws) components, enabling the system to decode abstract scores into concrete, actionable instructions. Experimental validation across multiple datasets demonstrates that LIBS achieves superior interpretability and efficiency compared to state-of-the-art baselines, marking an improvement from diagnostic to instructional AQA applications.

The dynamic nature of tasks and environments in Internet of Things (IoT) systems require deep learning models to continuously retrain on evolving data to ensure their effectiveness. Existing continual learning (CL) methods aim to mitigate catastrophic forgetting, where the model loses knowledge of previous tasks when learning new ones. However, these methods often ignore the memory resource competition caused by the parallel execution of multiple applications, which limits the realworld IoT application of CL in resource-constrained edge devices. In this article, we propose EDCL, a novel approach that enhances the training efficiency and model accuracy of CL methods while ensuring the uninterrupted operation of high-priority inference programs. Specifically, we first implement a custom batch sampler that can dynamically load batches and measure the memory usage and training time recorded via offline profiling. In the online stage, by monitoring the resource consumption of high-priority programs, EDCL can dynamically select batch policies that meet resource constraints and facilitate efficient training. Additionally, we propose an adaptive hierarchical buffer swap method to enhance the model’s ability to retain previously learned knowledge and mitigate forgetting. Extensive experiments show that EDCL effectively balances training efficiency and model accuracy while preventing high-priority inference programs from failing due to memory contention, demonstrating promising performance compared to baselines.

Wi-Fi device-free tracking enables the inference of user behaviors without physical contact, which is crucial for intelligent indoor location-based services. Nevertheless, the practical implementation of current tracking systems is constrained by several critical limitations: 1) The low-quality sensing signals in complex scenarios lead to increased tracking errors; 2) Existing methods inadequately adjust to dynamic environments, necessitating additional data collection or retraining processes. To address these challenges, this paper introduces EOC-Tracking, a device-free Wi-Fi tracking system that dynamically incorporates environmental information. Our key innovation involves leveraging obstacles to correct illogical users' trajectories and facilitate adjustment to varying environments. This significantly improves the accuracy of the follow-up in complex and changing environments. The EOC-Tracking system is built upon three fundamental design principles: 1) A lightweight dual-branch neural network architecture that effectively fuses environmental data with Wi-Fi signal characteristics; 2) An autonomous map updating mechanism that facilitates real-time adaptation to environmental layout modifications without human intervention; 3) A sophisticated data-driven, phased training paradigm that optimizes the model's ability to learn and apply obstacle constraints. We implement EOC-Tracking using commercial Wi-Fi devices and deploy it on low-power embedded systems such as the MCU. Experimental results demonstrate that EOC-Tracking can reduce tracking errors by at most 49.48% compared to datadriven methods and 62.21% compared to model-based methods in various complex scenarios.

Millimeter-wave (mmWave) 5G-V2X communications face significant challenges in real-time beam alignment within high-mobility vehicular networks. While environmentaware beam prediction methods mitigate channel estimation overhead, their efficacy is severely compromised by modality data loss stemming from lighting variations, adverse weather, or sensor failures. To address this issue, we propose a Cross-modal Losstolerant Beam Prediction model (CLBP). CLBP robustly fuses RGB camera and LiDAR data, employing a novel cross-modal attention mechanism to achieve resilient feature alignment across these heterogeneous modalities. Furthermore, a Branch Features Dynamic Fusion (BFDF) module adaptively reweights modality features, suppressing noise from degraded inputs and promoting effective information propagation to enhance resilience. To facilitate realistic evaluation, we introduce a Data-Conditioned Missingness Mechanism (DCMM), which augments the DeepSense 6G V2V dataset with meticulously simulated sensor failure scenarios. Experimental results demonstrate CLBP's superior performance, achieving 94.48% Top-5 beam prediction accuracy even under 10% modality loss, and a 29% reduction in average power loss compared to baseline methods. These findings demonstrate CLBP's significant robustness in dynamic vehicular environments and its capacity to maintain consistent, high-performance beam prediction despite challenging data imperfections.

Federated Learning (FL) is a popular distributed machine learning method that enables the development of a robust global model through decentralized computation and periodic model aggregation, without requiring direct access to clients' data. However, data heterogeneity poses a significant challenge in FL, and the global long-tail distribution exacerbates this issue. While substantial research has focused on mitigating performance degradation caused by long-tailed distributions, existing methods typically concentrate on addressing discrepancies between local and global class distributions, often overlooking the fact that these discrepancies stem from variations in the data itself. To address this, we propose a novel approach, Federated Context Optimization and Feature Information Decoupling (FedDR), which generates partition strategies for each sample to extract and leverage long-tail, global, personalized, and label-text information within its features to enhance the representational distinction of tail classes. Specifically, we first design a Feature Information Decoupling module that separates global, personalized, and long-tail information within the features and incorporates this information into the loss function to strengthen the global model's focus on personalized information in tail samples. Furthermore, to exploit the textual label information embedded in the samples, we integrate a cross-modal model, CoOp, which utilizes open-vocabulary prior knowledge, and implement dynamic knowledge distillation between the client model and CoOp to enhance the client model's feature representation capability. Extensive experimental results on multiple benchmarks demonstrate that the proposed FedDR outperforms state-of-the-art methods in the federated long-tailed learning setting.

Many existing transport designs aim to deliver ultra-low latency and high bandwidth for applications in high-speed datacenter networks. However, almost all of them intertwine the control of small and large flows using the same control entity (e.g., sender or receiver) and congestion feedback signal (e.g., ECN or credit), thus bringing significant performance impairments. By contrast, we seek to decouple the rate control of small flows from that of large ones.

End-to-end semantic communication paradigms demonstrate substantial potential in reducing network load and compressing data redundancy. However, their inherent openness introduces significant security risks, such as unauthorized access that enables attackers to camouflage themselves among legitimate users. Moreover, legitimate users may exploit input-output data pairs to conduct model stealing attacks. Existing defense strategies generally lack user access control mechanisms and fail to provide targeted countermeasures against model inversion attacks from internal users. To address this gap, we propose KGSC-SAT, a Key-Gated Semantic Communication framework enhanced by Steganography Adversarial Training for Secure Transmission. The framework employs a key-based feature modulation method to identify authorized users, while adversarial steganography training facilitates deep feature-level masking. Experimental results demonstrate that KGSC-SAT effectively mitigates both unauthorized access and insider model inversion threats, while delivering reliable communication performance.

Multimodal Large Language Models (MLLMs), while powerful, pose significant privacy risks by memorizing and potentially exposing sensitive information linked to individuals' visual appearances. Existing machine unlearning techniques, developed primarily for text-based models, are ill-equipped to handle the deeply entangled nature of visual and semantic knowledge. To address this challenge, we introduce GAIA-UL, a novel three-stage framework that performs Surgical Unlearning of visual knowledge. Our approach first conducts a Causal Hotspot Diagnosis, using gradient-based analysis to precisely identify influential parameters within the visual-semantic pathway. Second, it performs a Targeted Adapter Intervention, surgically injecting lightweight, trainable adapters only at these hotspots while freezing the base model. Finally, it employs Semantically Orthogonal Fine-tuning, a novel objective that forces the model's internal representation of a target face to become orthogonal to embeddings of associated sensitive concepts, thereby erasing the link at a deep representational level. Extensive experiments on the MLLMU-Bench benchmark demonstrate that GAIA-UL significantly outperforms existing baselines, achieving superior visual knowledge ablation while robustly preserving general model utility and text-only knowledge.

Sign Language recognition is practically important in various scenarios such as smart home, medical rehabilitation, and intelligent industry. Compared with wireless sensing and computer vision methods, data glove-based methods have gained a plenty of attention, because they can perform well even in the environments with multi-path noise or visual occlusion. However, existing data glove-based methods usually require complex calibration and laborious dataset collection, and suffer from accumulated error. To address these challenges, we introduce a robust sign language recognition system with cross-domain generation, called SmartGlove, the first approach to achieve robust sign language recognition. To avoid complex calibration process, we propose a customized feature set that can enable user-insensitive and unintentional system calibration. To avoid the labor cost in training data collection, we propose a cross-domain data transformation technique to generate training data in target domain. To eliminate the accumulated error of sentence recognition, we utilize a context-based calibration method considering correlation among adjacent words. We implement SmartGlove with COTS devices, and extensive experiments reveal that SmartGlove achieves accuracy exceeding 97.11% for 30 sign language words, with an average recognition time of 47 milliseconds per word. Furthermore, the system recognizes 30 common sign language sentences with accuracy of 97.17%.

Automated manufacturing is the cornerstone of the Industrial Internet of Things (IIoT) ecosystem, where vibration monitoring technology is a critical tool for maintaining industrial machinery. The prevailing approach mostly employs inertial measurement units (IMUs), lasers, and cameras, each demonstrating deployment constraints. In recent years, millimeter-wave (mmWave) radar has shown high vibration measurement performance, but it faces challenges in accurately localizing vibrating objects and determining observation points. This study introduces a new system called VibCamera, which leverages the mmWave vibration measurement technology with computer vision (CV) algorithms for vibration monitoring. With the positional assistant of CV semantic segmentation, the radar can accurately determine sufficient observation points, thereby achieving precise measurement with high directionality. VibCamera includes two camera modes, RGB-only and RGB+depth, and solves two technical challenges: 1) integrating multimodal information for vibration target localization and 2) extracting high-quality vibration signals in interference environments. VibCamera provides more consistent and precise outcomes without the need for physical contact. The experimental results indicate that the RGB-only mode has amplitude and frequency errors below$27.04 \; \mu \rm m$and 0.22 Hz, respectively, with a 90% probability, and the RGB+depth mode has errors below$23.72 \; \mu \rm m$and 0.21 Hz.

The Wi-Fi sensing technique holds great promise for future smart homes, thanks to the widespread use of Wi-Fi devices. With this technique, we can deduce the behavior of the target based on the channel state information (CSI), which is obtained during Wi-Fi communication. However, existing Wi-Fi sensing technologies are not compatible with standard communication technologies. This is because Wi-Fi sensing usually relies on capturing CSI from high-frequency communication packets, whereas regular IoT communication does not consistently maintain such high communication rates. To achieve precise sensing even with a low packet rate, we introduce LowDetrack, an indoor human detection and tracking system at ultra-low packet rates with Wi-Fi. In particular, we utilize compressed sensing to supplement missing data compared to existing systems that rely on linear interpolation or neural networks. To detect and track the target, our insights are twofold: 1) We combine compressed sensing and Fresnel zone to a theoretical model for accurately obtaining the reflection path change rate, which can be converted into the actual velocity of the target; 2) We investigate the mapping relationship between the dynamic frequency composition ratios in different links, which can provide navigation for velocity direction and correct direction recognition errors. We implement LowDetrack on commercial off-the-shelf Wi-Fi and realize human detection and tracking, where the median tracking error is 0.76m at the packet rate of 25 Hz.

A federated learning framework enables multiple clients to jointly train models locally without uploading their private data, effectively protecting the clients’ data privacy. However, existing federated learning auction mechanisms have not considered heterogeneity in client training time, making it difficult for the server to aggregate client models effectively within a constrained time. Moreover, continuously selecting specific clients in each round can lead to overfitting. This article proposes an Uncertainty-aware Auction Mechanism (UAMARD) based on Age of Update (AoU), Reputation, and Data Quantity, which considers training time and provides guidance on the number of data points to participate in training for selected clients. Firstly, we model a reverse auction system that considers the uncertainty of training time to promote client participation. We introduce AoU to quantify the time interval required for the server to receive the latest updates from the client to avoid overfitting. Then, we prove that solving the problem of maximizing social welfare is NP-hard. Subsequently, we introduce a dynamic programming algorithm (VCG RA) to solve the problem of maximizing social welfare. To further reduce time complexity, we propose our UAMARD method, which achieves a near-optimal level of social welfare while ensuring minimal time complexity. Ultimately, simulation experiments confirmed the efficacy of UAMARD and VCG RA. When benchmarked against other mechanisms, UAMARD and VCG RA demonstrated superior performance with quicker convergence and higher accuracy in testing the MNIST and CIFAR-10 datasets.

Abstract Federated learning represents a decentralized approach to machine learning, enabling numerous devices to collaboratively contribute to model training while ensuring the privacy of individual data. However, the existing incentive mechanism of hierarchical federated learning (HFL) only considers the data contribution of a single round, which needs to be revised. For non‐IID data sets, the continuous selection of any end devices will cause the weights to diverge in a specific direction. Therefore, a new metric is needed to avoid continuously selecting a certain end device to ensure the overall effectiveness. We introduce a metric to describe the importance of updates: age of update (AoU), which can help select end devices not selected in the previous round to promote a faster model convergence. We put forward an incentive mechanism based on AoU, reputation, and data quantity in HFL (ARDHFL). We have derived the optimal equilibrium solution for the three‐stage Stackelberg game. Based on this solution, we can ensure maximum edge‐cloud utility while incentivizing end devices to engage actively in HFL tasks and providing superior data to train the HFL model. Finally, we conducted extensive experiments to prove that ARDHFL can effectively improve the performance. Compared with the fixed scheme, random scheme, FMore and InFEDge, the testing accuracy of ARDHFL in the MNIST dataset has been improved by 29.7%, 9.3%, 6.8% and 6.1%, respectively. In the CIFAR‐10 dataset, it has been improved by 40.2%, 33.1%, 16.4% and 14.2%, respectively, and demands fewer communication iterations to achieve the same testing accuracy.

Given privacy and network load concerns, employing on-device multimodal neural networks (MNNs) for IoT data is a growing trend. However, the high computational demands of MNNs clash with limited on-device resources. MNNs involve input and model redundancies during inference, wasting resources to process redundant input components and run excess model parameters. Model Redundancy Elimination (MRE) reduces redundant parameters but cannot bypass inference for unnecessary input components. Input Redundancy Elimination (IRE) skips inference for redundant input components but cannot reduce computation for the remaining parts. MRE and IRE independently fail to meet the diverse computational needs of multimodal inference. To address these issues, we aim to combine the advantages of MRE and IRE to achieve a more efficient inference. We propose anadaptivemultilevelredundancyelimination framework (AMRE), which supports both IRE and MRE.AMREfirst establishes a collaborative inference mechanism for IRE and MRE. We then propose a multifunctional, lightweight policy model that adaptively controls the inference logic for each instance. Moreover, a three-stage training method is proposed to ensure the performance of collaborative inference inAMRE. We validateAMREin three scenarios, achieving up to 52.91% lower latency, 56.79% lower energy cost, and a slight accuracy gain compared to state-of-the-art baselines.

Non-intrusive hand tracking with mmWave radar technology is important in various Human-Computer Interaction (HCI) scenarios. However, existing mmWave-based solutions require users to be stationary and restrict a fixed hand motion area, which limits application flexibility and user experience. This paper proposes a novel mmWave-basedMobileHandTracking (MHTrack) system, which tracks user's hand gestures during walking. MHTrack focuses on tracking bothabsolutehand trajectory in the global coordinate system andrelativehand trajectory to the body. Specifically, we propose a wake-up mechanism for hand motion capture, in which hand point cloud can be recognized even under body interference and noise. We propose a hand tracking strategy named local spatial update, which overcomes the sparsity and instability of point clouds, to obtain absolute hand trajectory. Subsequently, we propose a hand anchor correction method to suppress anchor offset and remove the impact of body movement from absolute hand trajectory, thereby obtaining relative hand trajectory. As a case study, we project the relative hand trajectory onto a 2D image and feed it into a gesture recognition model to recognize the gestures. We conduct extensive experiments to evaluate the performance of MHTrack. Results demonstrate a 3D hand trajectory tracking error of$3.6cm$in an area of$3.2m\times 4.8m$and a gesture recognition accuracy of$99\%$with 30 gesture classes.

Liquid sensing in ubiquitous contexts plays an essential role in various scenarios. Recently, some wireless sensing systems have been proposed for liquid identification. However, existing works usually require specific equipment or capture the signals penetrating a target, limiting the deployability of liquid sensing. In large-scale scenarios, multiple devices are usually required to expand the coverage area due to the RFID reader antenna's reading range limitation. To enlarge the sensing range and make the liquid sensing method can be adopted in real moving scenarios, in this paper, we presentMobileLiquidIDentification (MLiquID), a liquid sensing system that can recognize the type of liquid in a mobile manner with commercial off-the-shelf (COTS) RFID devices. This mobile process leads to continuous variation in location, so the major challenge in this paper is how to extract signal features from the superimposed information of movement and material. The key insight is to regard movement as an opportunity to acquire data from different perspectives instead of a challenge to hinder feature extraction. We construct a Phase-RSS model by analyzing the influence of moving and liquid on the phase and RSS signals. First, we propose a method to calculate the distance from the tag to the reader antenna. Second, we explore an identification method to identify liquid type by extracting signal features Phase-RSS coefficient$C_{P-R}$and Maximum Response Distance (MRD). Experimental results demonstrate an average accuracy of 96.80% in identifying 10 common liquids, which shows the great potential of MLiquID for mobile liquid sensing.

As an advanced technology for non-contact sensing, mmWave radar enables fine-grained measurement of a wide variety of user behaviors. While creating intelligence and convenience, it also concerns behavioral privacy and security, as radar signals contain a wealth of behavioral information. Existing solutions are either incapable of customizable privacy protections or cannot cope with multi-person scenarios. This paper presents aMulti-user behavioral privacyFilter, MuFilter, a data masking system centered on the idea of dimensional signal interference. It determines the sensing signatures that need to be preserved or interfered with based on the sensing services that users want to enable and disable, thereby making targeted tampering on the radar signal. On this basis, we introduce the multi-person tracking technology to allow MuFilter to determine the number of users in unknown scenarios. Moreover, a subspace tampering technique is proposed to ensure that each tampering only affects the target user and not other users, thus supporting personalized privacy protection for multiple users. Experiments show that MuFilter can interfere with targeted behavioral signatures with a 100% success rate, while the degree of impact on other users’ signatures ranges from 0% to 3.85%.

Location-based services and identification hold promise for future smart home applications. Through them, we can provide customized services for specific users in current locations. Recent studies have demonstrated that Wi-Fi signals can be leveraged to achieve device-free tracking and gait recognition. Despite their good performance, these two technologies are not effectively integrated for the following reasons: First, the device-free tracking method might yield tracking results that conflict with human gait. Second, extracting gait features relies on knowing or accurately estimating the user's trajectory. Consequently, gait recognition and tracking are inherently linked, but there has been no effective approach to integrate these two techniques. In this paper, we present STAGR, a system capable ofSimultaneousTrackingAndGaitRecognition. The main contribution of our technique is that we establish a theoretical model that reveals how to transform path-dependent spectra into path-independent spectra directly. Specifically, we conduct a preliminary study to demonstrate the need for simultaneous tracking and gait recognition. Second, we propose a novel method to extract path-independent gait features, which can significantly save execution time compared with the learning-based method. Third, we design a polar-coordinate filtering method to retain the gait features while correcting the trajectory. We implement a prototype STAGR system and conduct extensive experiments to verify the proposed mechanism. The experimental results show that we can realize simultaneous tracking and gait recognition. The median tracking error is$ 0.45m$, while the recognition accuracy is 95.3% for 6 users.

Radio frequency identification (RFID) system has been extensively employed to track missing items by affixing them with RFID tags. Many practical applications require to efficiently identify missing events for a specific subset of system tags (called key tags) due to their elevated importance. Existing methods primarily aim to identify all tags, which makes it challenging to specifically identify key tags because of interference from other non-key tags (called ordinary tags). In light of this, several key tag identification methods follow a two-step scheme that filters ordinary tags first and then identifies key tags. Nevertheless, this wastes too much time on tag filtering, resulting in low time efficiency. This paper presents a novel missing key tag identification protocol with two creative designs to gain high efficiency. First, we develop a novel verification technique that can rapidly determine the presence or absence of key tags amid the scenarios with both key tags and ordinary ones. By combining the ON-OFF Keying modulation, we could verify multiple key tags in a single slot, thereby reducing the total slots required. Second, we design a new selection technique that efficiently selects the unverified key tags for further verification, while filtering out the verified key tags and irrelevant ordinary tags to avoid redundant data transmission. Additionally, we present an enhancement protocol that leverages a preselection technique to avoid collecting useless tag responses, further boosting efficiency. We carry out rigorous theoretical analysis to optimize the performance of the proposed protocols. Both simulations and practical experiments demonstrate that our method is markedly superior to state-of-the-art solutions.

Wi-Fi contact-free sensing systems have attracted widespread attention due to their ubiquity and convenience. The integrated sensing and communication (ISAC) technology utilizes off-the-shelf Wi-Fi communication signals for sensing, which further promotes the deployment of intelligent sensing applications. However, current Wi-Fi sensing systems often require prolonged and unnecessary communication between transceivers, and brief communication interruptions will lead to significant performance degradation. This paper proposes Baton, the first system capable of accurately tracking targets even under severe Wi-Fi feature deficiencies. To be specific, we explore the relevance of the Wi-Fi feature matrix from both horizontal and vertical dimensions. The horizontal dimension reveals feature correlation across different Wi-Fi links, while the vertical dimension reveals feature correlation among different time slots. Based on the above principle, we propose the Simultaneous Tracking And Predicting (STAP) algorithm, which enables the seamless transfer of Wi-Fi features over time and across different links, akin to passing a baton. We implement the system on commercial devices, and the experimental results show that our system outperforms existing solutions with a median tracking error of 0.46m, even when the communication duty cycle is as low as 20.00%. Compared with the state-of-the-art, our system reduces the tracking error by 79.19% in scenarios with severe Wi-Fi feature deficiencies.

With the wide application of multi-modal fusion sensing in scenarios such as autonomous driving and human-computer interaction, the privacy security and communication burden caused by massive data uploading need to be solved urgently. Federated Learning (FL) has received significant attention as a privacy-preserving distributed machine learning paradigm. Recent Multi-Modal Federated Learning (MMFL) focuses on addressing modal heterogeneity to enhance accuracy and speed up convergence. However, it overlooks the huge communication overhead in updating complex multi-modal network models, especially in edge environments with limited bandwidth. At the same time, the state-of-the-art communication-efficient FL methods are not customized to the MMFL characteristics. In this paper, we propose the Adaptive Quantization framework for Multi-modal Federated Learning (AQMFL). AQMFL implements decision-level multi-modal fusion locally by using parallel training and model ensemble, supporting its adaptation to modal heterogeneity and flexible deployment. AQMFL can adaptively allocate the number of quantization levels of gradient according to the modal contribution and the heterogeneous communication ability of nodes, which speeds up the system convergence and achieves a better balance between accuracy and communication efficiency. Compared with the classical baselines, AQMFL can reduce the total communication overhead by up to 50.47% and the total training time by up to 52.11% while maintaining the accuracy.

The ubiquity of acoustic devices and the fine-grained sensing of acoustic signals have made acoustic device tracking a popular option. We propose to expand the use of commercial devices with microphones as an extension of the audio system to support intelligent applications, such as VR/AR. This paper introduces a novel 6D acoustic pose estimation system. To realize device-based pose estimation, most existing systems deploy multiple speakers. However, due to limited inaudible bandwidth, concurrent transmissions with multiple speakers pose challenges in balancing resolution and frame rate. To address this problem, we design 2×Track, a band multiplexing signal model that doubles the availability of limited bandwidth by utilizing a unique encoding strategy for concurrent transmissions. We also propose solutions to enhance signal feature estimation and implement a 6DoF pose tracking scheme tailored for distributed systems. The prototype is deployed on a typical circular microphone array, and experimental results show that 2×Track achieves a median position and orientation error of 7.6mm and 4.1°, respectively, in a 4-speaker setup. Our extended applications on commercial devices also showcase the versatility of our system, particularly in face orientation detection, air mouse and drone tracking.

Sleep posture recognition is practically important in various scenarios such as sleep healthcare, bedridden patient care, and chronic disease diagnosis. With concerns of user privacy preserving, we prefer the wireless sensing methods to computer vision methods when dealing with sleep posture recognition. However, the existing wireless sensing methods suffer from at least one of the following major limitations: (i) difficult to deploy in practice; (ii) few posture categories; (iii) insufficient accuracy; (iv) poor generalization ability. In this paper, we use commercial-off-the-shelf (COTS) mmWave radar to implement a sleep posture recognition system called PosMonitor. When designing the PosMonitor system, we need to address the following challenging issues. First, we propose an angle purification method based on multi-frame joint analysis to alleviate the sparsity and instability of the point cloud. Then, we endow the point cloud with respiratory features to enhance its representation of the sleep posture. Further, to make the system applicable to different users, we extract relative respiratory features by normalization to overcome individual differences. Extensive experimental results show that our PosMonitor system can achieve 98% accuracy on average in recognizing 6 typical sleep postures and has good reliability across different conditions.

Wireless signal analytics in IoT systems can enable various promising wireless sensing applications such as localization, anomaly detection, and human activity recognition. As a matter of fact, there are significant correlations in terms of dimension, spatial and temporal aspects among wireless signals from multiple sensors. However, none of the wireless sensing research currently in use directly incorporates or exploits the signal correlations. Therefore, there is still substantial scope for improvement in regards to accuracy and robustness. We are introducing a novel framework called Signal Correlation Learning (SCL). This framework utilizes a directed graph to explicitly represent the signal correlation across various wireless sensors. We use signal embedding to depict the correlation features of a multi-dimensional sensor that arise from a multi-sensor system. Then, we perform Kullback-Leibler (KL) divergence on embedding vectors of any pair of sensors in the system to construct a subgraph at a given time point, which can measure the spatial signal correlation of sensors. Subsequently, several subgraphs spanning a specific time frame are fused into a coherent universal graph based on the small-world theory. This universal graph represents the three types of signal correlation simultaneously. A signal correlation aggregation structure is utilized to extract the features from the universal graph. These features can be used to address target sensing problems. We implement SCL in real RFID, Bluetooth, WIFI, and Zigbee systems, and evaluate its performance in three common wireless sensing problems including localization, anomaly detection, and human activity recognition. Extensive experiments demonstrate that our SCL framework significantly outperforms state-of-the-art wireless sensing algorithms by increasing$80\%\sim 190\%$in terms of accuracy, and by increasing$160\%\sim 220\%$in terms of robustness.

The ubiquitous Internet-of-Things (IoT) devices generate vast amounts of multimodal data, and the deep multimodal fusion network (DMFN) is a promising technology for processing multimodal data. Deploying DMFNs locally on embedded IoT devices is a profitable way to provide privacy-preserving and robust sensing services. However, the current compression methods suffer from the following limitations: First, they are designed based on unimodal networks or specific model structures. Hence, it is hard to extend these methods to diverse DMFNs; Second, existing works never relate their efforts to disparate computational demands of multimodal data and modalities. Easy samples and redundant modalities consume the same computational resources as powerful modalities and complex samples. We propose anAdaptiveCompressionFramework (ACF) for DMFNs to address those challenges. It enables input-dependent runtime compression locally on resource-constrained embedded devices. Specifically, we propose an offline model transformation module to upgrade the static network with two kinds of dynamic components to support online structural adjustment. Then we design a lightweight policy network to generate multi-granularity and data-dependent compression strategies for different model parts. Finally, we evaluate ACF on four DMFNs across three embedded platforms. Compared with the best results of the existing schemes, ACF obtains up to 2.61× latency reduction and 2.30× energy consumption reduction, with up to 3.57% accuracy improvement.

Fine-grained recognition of human manipulation activities on objects is crucial in the era of human-computer-object integration. However, there is a lack of solutions for simultaneous recognition of human identity, manipulation activities (including drawing and rotation), and manipulated objects. Therefore, we propose an RF-Camera system that combines RFID and computer vision techniques to address this challenge in multi-person and multi-object scenarios. In RF-Camera, we employ a skeleton-assisted method to extract facial images of target individuals, enabling precise recognition of their identities. To identify manipulation activities, we analyze the 3D hand trajectory and fingertip vector angle, differentiating drawing and rotation manipulation activities. Additionally, we model target person?s hand movements to predict phase data of the target tag, enabling the determination of person-object relationships. Implementing RF-Camera using COTS RFID and Kinect devices involves overcoming challenges such as extracting effective data from noisy streams, predicting virtual phase data considering hand-tag offset, and ensuring high tag reading rates in tag-dense scenarios. We conducted experiments involving six participants performing object manipulation activities, including drawing letters/symbols and rotating movements. Extensive experimental results show that RF-Camera achieves over 90% accuracy in recognizing person identity, manipulation activities, and person-object matching in most conditions.

Federated Learning (FL) has emerged as a privacy-preserving paradigm enabling collaborative model training among distributed clients. However, current FL methods operate under the closed-world assumption, i.e., all local training data originates from a global labeled dataset balanced across classes, which is often invalid for practical scenarios. In contrast, in many open-world settings, data have been observed to exhibit heavy-tailed distributions, particularly in the realm of mobile computing and Internet of Things (IoT). Heavy-tailed data can have a significant negative impact on the performance of learning algorithms due to amplifying the heterogeneity in the FL environment. To this end, we introduce a novel framework to counter biased training caused by diverse and imbalanced classes. This framework includes a balance-aware reward aggregation mechanism addressing local majority and global minority class disparities. Rewards are assigned based on client class prevalence for fair aggregation. A calibration module supplements global aggregation to manage conflicts from inconsistent data distribution among clients. Using reward aggregation and calibration, we effectively mitigate heavy-tailed distribution effects, enhancing FL model performance. This framework seamlessly integrates with leading FL methods, demonstrated through extensive experiments on benchmark and real-world datasets.

As a diagnostic method of equipment operational status, vibration monitoring plays a significant role in industrial systems. It is necessary to monitor multiple equipment components simultaneously, due to their different vibration modes. Previous solutions either work in an invasive manner or face challenges in object localization and results correspondence. Therefore, we propose VibCamera, a vibration monitoring system that combines mmWave radar and computer vision technology. We propose an expand-shrink method to optimize object detection results of computer vision and combine camera localization results to extract mmWave signals. Additionally, we employ mmWave data recombination and respective fitting methods to calculate the vibration characteristics for each point accurately. The experiment shows that after fusing visual information, the target detection accuracy is improved to 94.8%, and the cluster point efficiency is improved by 23.3%. Furthermore, amplitude and frequency measurement errors are reduced to 29.1μm and 0.08Hz, respectively.

RFID has been widely deployed in large-scale supply-chain and warehouse scenarios to achieve effective inventory tracking and theft prevention. In such a scenario, there is a practical need for searching for a particular subset with a large number of tags, which can help the retailer to determine whether the current inventory matches the needs of a specific order. The current solution usually searches for the target tags in the entire inventory through single-set matching, which generally takes too much time and is hard to meet given the strict delay requirements of users. To address this problem, this article proposes a technique called multiset tag matching, which applies a hierarchical space-efficient bloom filter variation to aggregate multiple search queries and searches them by category. We show that communication overhead can be optimized further by exploiting the distribution among different categories for grouping similar categories. Thus, each category can be assigned a personalized length of filter to tradeoff between accuracy and cost. Moreover, our results also show that our approach provides a fair degree of accuracy across different categories. We further propose a combination technique for further aggregating category groups with similar sizes to reduce the search and switching costs.

Major manufacturers and retailers are increasingly using RFID systems in supply-chain scenarios, where theft of goods during transport typically causes significant economic losses for the consumer. This paper studies how to achieve time-efficient and secure integrity authentication problems in RFID systems. We start with a straightforward solution called SecAuth, which uses a secure identity stored on reserved memory to authenticate tags in a secure way. We then propose a time efficient KTAuth protocol, which design a verification chain mechanism to efficiently verify a small set of key tags using limited on-tag memory. We point out that the limitation of KTAuth is that it takes too much overhead to write a large block of data to tag memory, which leads to the proposed group selection mechanism. The KTAuth with group selection (KTAuth-GS) enables you to select key tags with a single select command, which helps to quickly check the existence of key tags and reduces the data writes on the tags. Experiments and simulation results demonstrate that the proposed KTAuth-GS can defend against counterfeiting attacks by providing more reliable results and reducing the execution time by as much as a factor of 5 when compared with a baseline tag identification protocol.

Recent studies have shown that the training samples can be recovered from gradients, which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of extensive surveys covering recent advances and thorough analysis of this issue. In this paper, we present a comprehensive survey on GradInv, aiming to summarize the cutting-edge research and broaden the horizons for different domains. Firstly, we propose a taxonomy of GradInv attacks by characterizing existing attacks into two paradigms: iteration- and recursion-based attacks. In particular, we dig out some critical ingredients from the iteration-based attacks, including data initialization, model training and gradient matching. Second, we summarize emerging defense strategies against GradInv attacks. We find these approaches focus on three perspectives covering data obscuration, model improvement and gradient protection. Finally, we discuss some promising directions and open problems for further research.

Federated Learning (FL) is susceptible to gradient leakage attacks, as recent studies show the feasibility of obtaining private training data on clients from publicly shared gradients. Existing work solves this problem by incorporating a series of privacy protection mechanisms, such as homomorphic encryption and local differential privacy to prevent data leakage. However, these solutions either incur significant communication and computation costs, or significant training accuracy loss. In this paper, we show that the sensitivity of gradient changes w.r.t. training data is an essential measure of information leakage risk. Based on this observation, we present a novel defense, whose intuition is perturbing gradients to match information leakage risk such that the defense overhead is lightweight while privacy protection is adequate. Our another key observation is that global correlations of gradients could compensate for this perturbation. Based on such compensation, training can achieve guaranteed accuracy. We conduct experiments on MNIST, Fashion-MNIST and CIFAR-10 for defending against two gradient leakage attacks. Without sacrificing accuracy, the results demonstrate that our lightweight defense can decrease the PSNR and SSIM between the reconstructed images and raw images by up to more than 60% for both two attacks, compared with baseline defensive methods.

This paper studies the problem of 6D pose estimation, which is practically important in various application scenarios such as robotic-based object grasping, obstacle avoidance in autonomous driving scene, and object integration in mixed reality. However, existing methods suffer from at least one of the five major limitations: dependence on object identification, complex deployment, difficulty in data collection, low accuracy, and incomplete estimation. To overcome the above limitations, this paper proposes an RC6D system, which is the first to estimate 6D poses by fusing RFID and Computer Vision (CV) data with multi-modal deep learning techniques. In RC6D, we first detect 2D keypoints through a deep learning approach. We then propose a novel RFID-CV fusion neural network to predict the depth of the scene, and use the estimated depth information to expand the 2D keypoints to 3D keypoints. Finally, we model the coordinate correspondences between the detected 2D-3D keypoints, which is applied to estimate the 6D pose of the target object. When implementing RC6D, we mainly address the following three technical challenges. (i) To predict 6D poses without using the CAD model, we propose a network architecture for monocular depth estimation. (ii) To train the neural network for 6D pose estimation without time-consuming 6D labeling, we use an unsupervised learning algorithm based on 2D-3D point pair matching. (iii) To detect the subject of the object without identification, we leverage optical flow to restrict the object and RFID to directly obtain its information. The experimental results show that the localization error of RC6D is less than 10 cm with a probability higher than 90.64% and its orientation estimation error is less than 10° with a probability higher than 79.63%. Hence, the proposed RC6D system performs much better than the state-of-the-art related solutions.

With the wide deployment of RFID in various scenarios such as warehouse management, freight transportation, and manufacturing, tag authentication is increasingly important due to the threat of counterfeit tags. Recent physical-layer authentication approaches have demonstrated that the subtle differences in the hardware features offer a unique fingerprint to authenticate a tag. Although the state-of-the-art approaches are effective in laboratory environments, they are difficult for practical deployment because they either require complex analysis of the raw signal propagation or restrict the geometrical positions of the tags. In this paper, we propose an RFID tag authentication based on frequency- and orientation-related phase fingerprints, called FopPrint, which does not require raw signal analysis or complex geometric relationship. FopPrint uses the phase values of tags at different frequencies and orientations to construct feature matrices as physical-layer fingerprints and uses a pair of adjacent tags as identifiers of each object. FopPrint can effectively eliminate the influence of environmental factors by using the feature matrix constructed by the phase difference. We implement a prototype of FopPrint using Commercial-Off-The-Shelf (COTS) RFID devices. Extensive experimental results show that FopPrint achieves high authentication accuracy of 94% in various experimental settings.

We explore the problem of selectively forgetting categories from trained CNN classification models in federated learning (FL). Given that the data used for training cannot be accessed globally in FL, our insights probe deep into the internal influence of each channel. Through the visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification.

Tag identification is a critical operation in large-scale RFID applications. Typically, in the RFID-enabled warehouse, the reader needs to execute tag identifications to obtain the inventory information of numerous tagged items. The existing schemes usually divide the time frame into multiple slots and map each tag to one of them for replying its identity. This imposes serious tag collisions because two or more tags may be mapped to the same slot and their responses corrupt with each other. When tag collision happens, all the collided tags cannot be identified by the reader, which significantly increases the identification delay. To overcome the collision problem in the identification process, this paper proposes a Group Tag Identification (GTI) framework to identify grouped tags in both singleton and collision slots. The key novelty of GTI is in leveraging tag-correlation to identify grouped tags in the collision slots without any extra transmission overhead. The main challenge of this work is to overcome the communication and architectural limitations of RFID systems in the context of building ID and slot-correlation between tags. Extensive simulations show that GTI significantly reduces the identification delay by up to 40 percent when compared with the state-of-the-art dynamical frame slotted aloha schemes.

Major manufacturers and retailers are increasingly using RFID systems in supply-chain scenarios, where theft of goods during transport typically causes significant economic losses for the consumer. Recent sample-based authentication methods attempt to use a small set of random sample tags to authenticate the integrity of the entire tag population, which significantly reduces the authentication time at the expense of slightly reduced reliability. The problem is that it still incurs extensive initialization overhead when writing the authentication information to all of the tags. This paper presents KTAuth, a lightweight integrity authentication approach to efficiently and reliably detect missing tags and counterfeit tags caused by stolen attacks. The competitive advantage of KTAuth is that it only requires writing the authentication information to a small set of deterministic key tags, offering a significant reduction in initialization costs. In addition, KTAuth strictly follows the C1G2 specifications and thus can be deployed on Commercial-Off-The-Shelf RFID systems. Furthermore, KTAuth proposes a novel authentication chain mechanism to verify the integrity of tags exclusively based on data stored on them. To evaluate the feasibility and deployability of KTAuth, we implemented a small-scale prototype system using mainstream RFID devices. Using the parameters achieved from the real experiments, we also conducted extensive simulations to evaluate the performance of KTAuth in large-scale RFID systems.

This paper studies the practically important problem of data collection for sensor-augmented RFID systems. However, existing RFID data collection protocols suffer from two common limitations: execution time is naturally in proportion to the number of tags, thus they cannot satisfy time-stringent application scenarios; none of them is complaint with the C1G2 standard, thus they cannot be implemented using Commercial-Off-The-Shelf (COTS) RFID tags. To overcome these two limitations, this paper proposes the Geographical correlation-based RF-data Collection (GRC) protocol. GRC is fast because it is able to approximately capture the sensing data of all tags by only actually gathering data from a small set of sampled tags. This is based on the observation from the real-world data set that sensing data has a strong geographical correlation, i.e., data gathered from nearby RFID tags has similar values. In GRC, we use a greedy approach to find the minimum sampling tag set to cover the whole monitoring region such that each un-sampled tag has at least one sampled tag nearby. Then, RFID reader runs the Framed Slotted Aloha (FSA) protocol specified in C1G2 standard to collect sensing data from the sampled tags. For each un-sampled tag, we approximate its sensing data by calculating weight-average of the data collected from its nearby sampled tags, where a faraway sampled tag should be given a small weight, and vice versa. Compared with existing RFID data collection schemes, the advantages of GRC are two-fold: (1) Extensive simulation results demonstrate that the time cost of our GRC scheme is only 1/28~1/3 of the state-of-the-art data collection scheme; (2) GRC is totally complaint with C1G2 standard, thus it can be easily deployed on the COTS RFID tags.

Tag inventory is one of the most fundamental tasks for RFID systems. However, the Framed Slotted Aloha (FSA) protocol specified in the C1G2 standard is of low time-efficiency, because it needs to collect all tags in the system. To improve time-efficiency, research communities proposed a batch of sampling-based approaches, in which the reader only needs to collect a small set of sampled tags instead of all. Although time-efficiency has been improved, existing sampling-based approaches still have two common limitations. First, all tags in the system are assumed to have the same sampling probability. It is unfair that tags attached to differential items (e.g., different values) have the same chance to be sampled and collected. Second, all existing sampling-based approaches stay in theory level and cannot be deployed on Commercial Off-The-Shelf (COTS) RFID devices, because the C1G2 standard does not support the sampling function at all. To deal with the above two limitations, this paper studies the new problem of differential tag sampling-letting each RFID tag be identified with a given sampling probability. In this paper, we use the COTS RFID devices including Impinj Speedway R420 reader and Monza 4QT tags to implement the Differential Tag Sampling (DTS) operation. Then, we apply probabilistic analytics on the collected tag data to address some practically important problems such as Multi-category Tag Cardinality Estimation (MTCE), and Value-based Missing Tag Detection (VMTD). Although the analytics results are not 100 percent accurate, the deviation in the results can be controlled below a small threshold and DTS can significantly improve the time-efficiency. DTS can be easily deployed on the COTS RFID systems, because it is totally compliant with the C1G2 standard. Extensive experiments demonstrate that DTS is able to let each tag take the given sampling probability to be sampled and identified. Moreover, the proposed DTS protocol can significantly reduce the execution time of MTCE and VMTD by nearly 70 percent than the FSA protocol.

This paper studies the practically important problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the ranges specified by the user. The existing RFID protocols that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag range information from singleton and even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM or minimizing its energy cost. We can trade off between time cost and energy cost by adjusting the related parameters. The prominent advantages of the RQ+PM protocol over previous protocols are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in previous protocols. Thus, frame utilization can be significantly improved; (ii) it is immune to the interference from unexpected tags, and does not suffer information corruption issue. We use USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, and reduce the time cost as much as 40% when comparing with the state-of-the-art protocols.

This paper takes the first step in studying the problem of range query for sensor-augmented RFID systems, which is to classify the target tags according to the range of tag information. The related schemes that seem to address this problem suffer from either low time-efficiency or the information corruption issue. To overcome their limitations, we first propose a basic classification protocol called Range Query (RQ), in which each tag pseudo-randomly chooses a slot from the time frame and uses the ON-OFF Keying modulation to reply its range identifier. Then, RQ employs a collaborative decoding method to extract the tag information range from even collision slots. The numerical results reveal that the number of queried ranges significantly affects the performance of RQ. To optimize the number of queried ranges, we further propose the Partition&Mergence (PM) approach that consists of two steps, i.e., top-down partitioning and bottom-up merging. Sufficient theoretical analyses are proposed to optimize the involved parameters, thereby minimizing the time cost of RQ+PM. The prominent advantages of RQ+PM over previous schemes are two-fold: (i) it is able to make use of the collision slots, which are treated as useless in the previous schemes; (ii) it is immune to the interference from unexpected tags. We use the USRP and WISP tags to conduct a set of experiments, which demonstrate the feasibility of RQ+PM. Moreover, extensive simulation results reveal that RQ+PM can ensure 100% query accuracy, meanwhile reducing the time cost as much as 40% comparing with the existing schemes.

The widely used RFID systems are vulnerable to the denial-of-service (DoS) attacks launched by malicious blocker tags. This paper studies how to quickly and completely identify the valid RFID tags that are blocked. The existing work that can seemingly address this problem suffers from either low time-efficiency or serious false positives. This paper proposes a hybrid approach that consists of two complementary component protocols, namelyAloha Filtering(AF) andPoll&Listen(PL).AFis fast but inaccurate, whilePLis accurate but slow. Taking the merit of each protocol, our hybrid approach is to first repeat the fastAFfor multiple rounds to quickly filter out the target tags that are definitely not blocked. Then, on the size-reduced remaining set that just contains a small number of suspicious tags, we invoke the accuratePLto verify the intactness of each suspicious tag with 100 percent confidence. We optimize the round count ofAFthat trades off between the time costs ofAFandPLto minimize the total time ofAF+PL. As required in the optimization process, we need to know the size of the blocked tag set and that of the unknown tag set, which, however, are not known in advance. To estimate these two set sizes, we propose a supplementary protocol calledSimultaneous Estimation of the Blocked tag size and the Unknown tag size(SEBU). The key advantages of our approach over the prior art are four-fold. First, unlike the detection protocol that just discovers the existence of blocking attacks, our approach exactly identifies all the blocked target tags. Second, our approach is compliant with the C1G2 standard, and does not require any modifications to be made to the commercial RFID tags. It only needs to be installed on readers as a software module. Third, our approach does not involve any false positives. Finally, our approach significantly reduces the execution time when compared with the state-of-the-art schemes that can completely identify the blocked tags.

For large-scale RFID systems, this paper studies the practically important problem of target tag information collection, which aims at collecting information from a specific set of target tags instead of all. However, the existing solutions are of low time-efficiency because of two reasons. First, the serious collisions among tags due to hashing randomness seriously reduce the frame utilization, whose upper bound is just 36.8 percent. Second, they cannot efficiently distinguish the target tags from the non-target tags and thus inevitably collect a lot of irrelevant information on non-target tags, which further deteriorates the effective utilization of the time frame. To overcome the above two drawbacks, this paper proposes the minimal Perfect hashing-based Information Collection (PIC) protocol, which first leverages lightweight indicator vectors to establish a one-to-one mapping between target tags and slots, thereby improving the frame utilization to nearly 100 percent; and then uses the novel data structure called Minimal Perfect Hashing based Filter (MPHF) to filter out the non-target tags, thereby preventing them from interfering with the process of collecting information from target tags. Sufficient theoretical analyses are also presented in this paper to minimize the execution time of the proposed PIC protocol. Extensive simulations are conducted to compare the proposed PIC protocol with prior works side-by-side. The simulation results demonstrate that PIC significantly outperforms the state-of-the-art protocols in terms of time-efficiency.

With the increasing popularization of radio frequency identification (RFID) technology in the retail and logistics industry, RFID privacy concern has attracted much attention, because a tag responds to queries from readers no matter they are authorized or not. An effective solution is to use a commercially available blocker tag that behaves as if a set of tags with known blocking IDs are present. However, the use of blocker tags makes the classical RFID estimation problem much more challenging, as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose RFID estimation scheme with blocker tags (REB), the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the EPC C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. REB conducts statistical inference from the two sets of responses and estimates the number of genuine tags. Rigorous theoretical analysis of parameter settings is proposed to guarantee the required estimation accuracy, meanwhile minimizing the time cost and energy cost of REB. We also reveal a fundamental tradeoff between the time cost and energy cost of REB, which can be flexibly adjusted by the users according to the practical requirements. Extensive experimental results reveal that REB significantly outperforms the state-of-the-art identification protocols in terms of both time efficiency and energy efficiency.

In large-scale radio frequency identification (RFID)-enabled applications, we sometimes only pay attention to a small set of key tags, instead of all. This paper studies the problem of key tag population tracking, which aims at estimating how many key tags in a given set exist in the current RFID system and how many of them are absent. Previous work is slow to solve this problem due to the serious interference replies from a large number of ordinary (i.e., non-key) tags. However, time-efficiency is a crucial metric to the studied key tag tracking problem. In this paper, we propose a singleton slot-based estimator, which is time-efficient, because the RFID reader only needs to observe the status change of expected singleton slots corresponding to key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small, because key members are usually rare. As a result, even when the whole time frame is long, the number of expected singleton slots is limited and the running of our protocol is very fast. To obtain good scalability in large-scale RFID systems, we exploit the sampling idea in the estimation process. A rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. Extensive simulation results demonstrate that our scheme outperforms the prior protocols by significantly reducing the time cost.

This paper studies the practically important problem of top-k queries, which is to find the top k largest categories and their corresponding sizes. In this paper, we propose a Top-k Query (TKQ) protocol and a technique that we call Segmented Perfect Hashing (SPH) for optimizing TKQ. Specifically, TKQ is based on the framed slotted Aloha protocol. Each tag responds to the reader with a Single-One Geometric (SOG) string using the ON-OFF Keying modulation. TKQ leverages the length of continuous leading 1s in the combined signal to estimate the corresponding category size. TKQ can quickly eliminate the sufficiently small categories, and only needs to focus on a limited number of large-size categories that require more accurate estimation. We conduct rigorous analysis to guarantee the predefined accuracy constraints. To further improve time-efficiency, we propose the SPH scheme, which improves the average frame utilization of TKQ from 36.8% to nearly 100% by establishing a bijective mapping between tag categories and slots. To minimize the overall time cost, we optimize the key parameter that trades off between communication cost and computation cost. Experimental results show that our TKQ+SPH protocol not only achieves the required accuracy constraints, but also achieves a 2.6~7x faster speed than the existing protocols.

This paper studies the problem of data collection in sensor-augmented RFID networks: how to quickly obtain the error-bounded data from sensor-augmented RFID tags. Existing data collection protocols require each tag to transmit the sensor data to the reader through a low-rate channel. However, in large-scale RFID system, they take too long time and block other time-sensitive operations. By exploring the correlation of sensor data, our Sampling-based Information Collection (SIC) protocol significantly reduces the number of responding tags. Specifically, SIC obtains an error bound based on the estimation model by using some randomly-sampled data. The error bound is expected to maximize the number of data within it. These data can be seen as a cluster and be approximated by one value within the error bound. Then, SIC only needs to collect the data of out this cluster, thereby significantly reducing the data transmission. It minimizes the execution time by optimizing the sample size and estimating the number of tags out of the error bound. We conduct extensive simulations to evaluate the performance of SIC and compare it with three major related work. The results demonstrate that SIC is 1 to 10 times faster than the state-of-the-art solution.

The widely used RFID tags impose serious privacy concerns as a tag responds to queries from readers no matter they are authorized or not. The common solution is to use a commercially available blocker tag which behaves as if a set of tags with known blocking IDs are present. The use of blocker tags makes RFID estimation much more challenging as some genuine tag IDs are covered by the blocker tag and some are not. In this paper, we propose REB, the first RFID estimation scheme with the presence of blocker tags. REB uses the framed slotted Aloha protocol specified in the C1G2 standard. For each round of the Aloha protocol, REB first executes the protocol on the genuine tags and the blocker tag, and then virtually executes the protocol on the known blocking IDs using the same Aloha protocol parameters. The basic idea of REB is to conduct statistically inference from the two sets of responses and estimate the number of genuine tags. We conduct extensive simulations to evaluate the performance of REB, in terms of time-efficiency and estimation reliability. The experimental results reveal that our REB scheme runs tens of times faster than the fastest identification protocol with the same accuracy requirement.

RFID is an emerging technology that provides timely and high-value information to inventory management and object tracking, in which areas that identifying unknown tags completely is crucial. From prior researches in this area, one of the pending problem involves processing redundant time frames due to unknown tag collisions. In this paper, we propose a time-efficient unknown tag identification protocol based on coded filtering vector technique. This vector is able to efficiently separate unknown tags from known tags. It reduces the unknown-known tag collisions as well as the required time frame length. The proposed protocol can achieve the minimal execution time theoretically. And further simulations demonstrate that it performs much better than existing work by decreasing 30% of the total execution time on average.

In RFID-enabled applications, we may pay more attention to key tags instead of all tags. This paper studies the problem of key tag counting, which aims at estimating how many key tags in a given set exist in the current RFID system. Previous work is slow to solve this new problem because of the serious interference replies from the large number of ordinary (i.e., Nonkey) tags. However, time-efficiency is an important metric for the fast tag cardinality estimation in a large-scale RFID system. In this paper, we propose a singleton slot-based estimator, which is time-efficient because the RFID reader only needs to observe the status change of expected singleton slots of key tags instead of the whole time frame. In practice, the ratio of key tags to all current tags is small for "key" members should be rare. As a result, even when the whole time frame is long, the expected singleton slot number is limited and the running of our protocol is fast to achieve estimation accuracy. Rigorous theoretical analysis shows that the proposed protocol can provide guaranteed estimation accuracy to end users. We conduct simulations and implement a prototype of our protocol to verify its efficiency and deployability.