Satellite-acquired optical remote sensing imagery is extensively applied in time-critical applications like traffic surveillance and evaluation of natural disasters. However, clouds, as a common atmospheric phenomenon, frequently obscure observation. Current approaches aim to restore visibility in cloud-obscured regions, yet they typically fall short in the presence of dense cloud cover, which are exceedingly prevalent in remote sensing imagery. Alternative approaches rely on the satellite revisit cycle, frequently surpassing ten days, a duration impractical for genuine application scenarios due to target changes and bandwidth limitations. To address these issues, this paper proposes SCo-Cloud, a novel satellite constellation collaboration framework for cloud-aware onboard-computed imaging and transmission, which consists of Center-Sat and Edge-Sats. We propose onboard thin cloud removal and re-imaging region location models to locate the impact of clouds. We further design a novel multi-satellite scheduling strategy to eliminate clouds. The models above are integrated within the Center-Sat, with the nearby Edge-Sats collaborating in tandem to execute re-imaging assignments. Furthermore, to facilitate in-depth research, we have meticulously developed a cloud-covered target detection dataset. Comprehensive experiments have conclusively demonstrated that SCo-Cloud effectively surpasses the limitations inherent in current approaches, providing accurate and timely responses within the domain of Earth observation.

Model serving systems are critical for deep learning inference, managing GPU infrastructure to deliver end-to-end services. Current frameworks typically treat operators as basic compilation and scheduling units, which often fail to maximize GPU utilization due to hardware-unfriendly kernels and coarse-grained scheduling. To address these limitations, we propose a cooperative compilation and scheduling scheme that statically generates multiple kernel variants and dynamically schedules them based on runtime context. We present Infera, a high-performance model serving system that implements this approach. Experimental results demonstrate that Infera improves inference throughput by at least 1.6× compared to state-of-the-art baselines.

RFID has revolutionized item-level intelligence in IoT ecosystems, yet static localization with passive tags remains challenging due to multipath interference inherent in RF signals. We present LuxTag, the first system to enable visible light-based sensing and localization using standard, commercial RFID tags by transforming them into ambient light sensors. Our key insight leverages the discovery that photon-induced leakage currents in passive RFID ICs modulate their persistence time (i.e., the duration a tag remains operational after RF excitation ceases) proportional to ambient illuminance. LuxTag introduces two innovations: (i) a first-principles model characterizing how ambient light alters tag persistence time, enabling battery-free light sensing without hardware modifications; (ii) a differential measurement technique and zero-shot calibration method to isolate light effects and autonomously derive tag parameters, ensuring robust and accurate static localization system using COTS RFID infrastructure. Extensive experiments demonstrate that LuxTag achieves a mean light intensity error of 3.6 lux and 60.7% improvement over state-of-the-art static RFID localization. By synergizing the ubiquity of RFID with the multipath resilience of optical sensing, LuxTag opens new avenues for static RFID localization in smart warehouses, retails, and beyond.

High Frequency (HF) RFID technology enhances operations in logistics, retail, and access control, necessitating efficient tag identification. This paper addresses the challenge of tag collisions in HF RFID systems, where existing protocols such as Time Division Multiple Access (TDMA) are not fully optimized for HF requirements. We introduce a novel hybrid-mode protocol utilizing the ISO 15693 standard, which supports either 2 or 16 branches per node in its tree-based anti-collision mechanism. By dynamically switching configurations based on real-time tag density, our protocol significantly improves identification efficiency. We validate our approach through theoretical analysis and extensive simulations, along with real-world experiments, demonstrating marked improvements in operational speed and efficiency. This study is the first to tailor a time-efficient tag identification protocol for commercial HF RFID systems, offering substantial benefits in operational efficiency and user experience across various industries.

Radio Frequency Identification (RFID) technology plays a pivotal role in modern applications ranging from retail and logistics to healthcare and security. However, a fundamental challenge persists in large-scale RFID systems: maximizing the coverage capacity of readers – the ability to reliably identify and communicate with the maximum number of tags within their operational range. While previous research has explored various aspects of RFID performance, the systematic optimization of coverage capacity remains underinvestigated. This paper addresses this gap by developing a comprehensive framework that integrates theoretical analysis with practical implementation strategies. We first establish a novel coverage capacity model that incorporates critical factors such as tag spatial distribution and link loss dynamics, providing a theoretical foundation for determining the upper bounds of reader performance. Building on this, we propose a cutoff-power-based optimization approach that dynamically adapts to real-world conditions without relying on predefined system parameters. Furthermore, to extend coverage across larger areas, we investigate advanced multi-reader configurations and present strategic deployment methodologies. Our framework is designed to characterize the baseline coverage capacity of existing RFID readers, rather than to enlarge the interrogation zone through additional hardware or protocol modifications. Moreover, it can be naturally extended to advanced configurations such as MIMO or phased-array systems once their antenna parameters are specified. The effectiveness of our approach is validated through extensive experiments using commercial RFID hardware, demonstrating measurable improvements in coverage capability. By bridging theoretical principles with practical constraints, this work offers actionable insights for designing and deploying high-performance RFID systems.

With the proliferation of RFID-enabled applications, large-scale RFID systems often require multiple readers to ensure full coverage of numerous tags. In such systems, we sometimes pay more attention to a subset of tags instead of all, which are called key tags. This paper studies an under-investigated problemkey tag distribution identification, which aims to identify which key tags are beneath which readers. This is crucial for efficiently managing specific items of interest, which can quickly pinpoint key tags and help RFID readers covering these tags collaborate to improve the tag inventory efficiency. We propose a protocol called Kadept that identifies the key tag distribution by designing a sophisticated Cuckoo filter that teases out key tags as well as assigns each of them a singleton slot for response. With this design, a great number of trivial (non-key) tags will keep silent and free up bandwidth resources for key tags, and each key tag is sorted in a collision-free way and can be identified with only 1-bit response, which significantly improves the time efficiency. To enhance the scalability and efficiency of Kadept for high key tag proportions, we propose E-Kadept protocol, which accelerates the identification process by designing an incremental Cuckoo filter that reduces false positives and improves space efficiency. We theoretically analyze how to optimize protocol parameters of Kadept and E-Kadept, and conduct extensive simulations under different tag distribution scenarios. Compared with the state-of-the-art, E-Kadept can improve the time efficiency by a factor of 1.75×, when the ratio of key tags to all tags is 0.3.

Touch switches are the foundation of a smart automation system that provides us with a seamless and intuitive interaction between users and smart devices. Typical touch switches and recent gesture sensing solutions suffer from some limitations including low scalability, low robustness, hardware modifications, and long time delay. In this work, we propose an RFID tag based touch switch called Tach that provides users with a battery-free, low-latency, low-cost, and plug-n-play touch-based input primitive, with no need for any hardware modifications. In Tach, we turn a passive tag as a touch switch and use the principle of impedance mismatch to sense the touch action by users. More specifically, we observe that when the finger touches the tag, the impedance of tag antenna changes. This change gives rise to impedance mismatch between the tag antenna and the tag chip, which further causes a frequency shift and a specific signal pattern. We design a continue wavelet transform based segmentation scheme and a dynamic time warping approach to achieve accurate and reliable touch identification in real scenarios. Additionally, multi-tag switches are discussed to support more user inputs. We implement Tach with COTS RFID devices. Extensive experiments show that the sensing accuracy reaches 96.5% with a low latency of 300ms, regardless of different users and environmental changes. The promising results might open up a new avenue in the design of touch switches in the near future. Code and datasets will be made available.

SLAM is to build a map of an unknown environment while simultaneously tracking each agent's location within it, which is widely used in many applications like navigation and inventory management. Existing SLAM work cannot provide item-level semantic information for strong interaction with complex context. In this paper, we propose a brand-new solution called RF-SLAM that fuses the techniques of RFID and traditional LiDAR SLAM. A fusion algorithm is designed to pair each tag with target objects instead of locating tags directly, which addresses the problems of uncertain robot's motion trajectory, unknown antenna's position, ambiguity, and unknown tags' heights. The competitive advantage of RF-SLAM is that it can obtain the benefits of RFID and LiDAR and build an item-level semantic map of an unknown environment, with no need for any prior knowledge or system calibrations. We implement a prototype of RF-SLAM. Extensive experiments show that RF-SLAM can build an item-level semantic map with a high accuracy of 99.5 % when target objects are 7 cm apart.

Range Query (RQ) is to check whether there are any RFID tags with data beyond a given range. With about 46 billion RFID tags sold worldwide in 2023, time-efficient RQ becomes increasingly important for practical use, which can help users quickly pinpoint the target tags (if any) and give an early warning (e.g., fire alarm) to them for taking urgent actions and reducing the potential risk. However, existing work can deal with only a single range rather than multiple ranges that are very common in real-world applications. For example, foods in the refrigerator and the freezer have different temperature ranges for safe storing; treating them as one would probably give rise to query errors. In this paper, we study an under-investigated problem called multi-range query, which aims to achieve RQ in an RFID system with multiple query ranges. We propose a tailored protocol called anomalous tag identification (ATI) that quickly separates target tags from others and avoids querying all tags for saving communication overhead. In ATI, we design a fixedlength encoding vector together with standards-compliant select commands to deal with different ranges individually, without the need for any hardware modification. We implement the proposed protocols in commodity RFID systems. Experimental results show that ATI is superior to the baseline under different parameters, in terms of the time efficiency and space efficiency.

With the advancement of Deep Learning, the performance of speech translation systems has made remarkable progress. However, similar to traditional software, speech translation systems can still suffer from software defects that can lead to incorrect translations with potentially serious consequences. These systems are also vulnerable to real-world environmental interference, making their behavior unpredictable. The black-box nature of deep neural networks renders traditional testing methods ineffective, while the lack of diverse test cases and the challenge of constructing test oracles further hinder the implementation of their testing. To address this, we introduce syntactic structure invariance, a linguistically inspired concept that captures the structural containment between a pair of derivationally related sentences and their corresponding translations. Based on this concept, we propose a novel speech translation testing method, SPlice. SPlice simulates environmental disturbances on a seed speech, disassembles it into a template and speech blocks, and then generates multiple derivational speech pairs by inserting the blocks back into the template. SPlice detects translation errors by checking whether the syntactic structure invariance relation is violated in the translation results corresponding to the speech pairs. To validate SPlice, we experiment with three industrial speech translation systems: Google Translate, Youdao Translator, and Iflytek Translator. With 600 speeches crawled from the BBC as seed tests, SPlice detects 1,640, 1,101, and 1,305 translation errors with around 90.7% precision. The experimental results show that SPlice can effectively detect errors in the speech translation results with high precision, providing valuable information for developers to improve system performance.

Approximate membership query data structures (i.e., filters) have ubiquitous applications in database and data mining. Cuckoo filters are emerging as the alternative to Bloom filters because they support deletions and usually have higher operation throughput and space efficiency. However, their designs are confined to a single-threaded execution paradigm and consequently cannot fully exploit the parallel processing capabilities of modern hardware. This paper presents PipeFilter, a faster and more space-efficient filter that harnesses pipeline parallelism for superior performance. PipeFilter re-architects the Cuckoo filter by partitioning its data structure into several sub-filters, each providing a candidate position for every item. This allows the filter operations, including insertion, lookup, and deletion, to be naturally distributed across several pipeline stages, each overseeing one of the sub-filters, which can further be implemented through multi-threaded execution or pipeline stages of programmable hardware to achieve significantly higher throughput. Meanwhile, PipeFilter excels for single-threaded execution thanks to a combination of unique design features, includingblock design,path prophet,round robin, andSIMD optimization, such that it achieves superior performance than the SOTAs even when running with a single core. PipeFilter also has a competitive advantage in space utilization because it permits each item to explore more candidate positions. We implement and optimize PipeFilter on four platforms (single-core CPU, multi-core CPU, FPGA, and P4 ASIC). Experimental results demonstrate that PipeFilter surpasses all baseline methods on four platforms. When running with a single core, it showcases a notable 15%$\sim$57% improvement in operation throughput and a high load factor exceeding 99%. When parallel processing on other platforms, PipeFilter achieves 7$\times \sim 800\times$higher throughput than single-threaded execution.

A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.

With 52.8 billion RFID tags used worldwide in 2024, a common basic functionality needed by RFID-enabled applications is cardinality estimation — to quickly estimate the number of distinct tags in an RFID system. Although many advanced solutions have been proposed over the past decade, they suffer from one major limitation in practical use: they need to either modify the existing RFID standard or obtain MAC-layer information, both of which however cannot be supported by commercial off-the-shelf (COTS) devices. In this paper, we revisit the counting problem and propose a novel counting scheme called average time duration based counter (ATD) that quickly estimates the number of distinct tags in a standards-compliant manner. Compared with existing work, the competitive advantage of ATD is that it can be directly deployed on a COTS RFID system, with no need for any hardware modifications. In ATD, we found a new and measurable indicator — the time duration between two adjacent singleton slots, which depends on the number of tags. Following this observation, we derive the theoretical relationship between the time indicator and the number of tags and then give the proof of the estimation as well as its parameter settings. Additionally, we propose a flag-flipping solution to address the overlapping problem in the multi-reader case. We implement ATD in a COTS RFID system with 1000 tags. Experimental results show that ATD is$4.2\times $faster than the baseline of tag inventory; the performance gain will be further increased in a larger RFID system.

Accurate pose estimation over SE(3) is fundamentally crucial for numerous perception tasks, including camera re-localization. While existing learning-based methods estimated from a series of RGB images have significantly improved the accuracy of pose, the majority of models still face one or two limitations. First, few representations on SE(3) are smooth and differential, making them difficult to apply in deep learning frameworks. Second, they often require high computational resources due to complex deep network designs. We in this paper propose the DL-PoseNet to address these issues. Specifically, we present a novel representation for SE(3) which follows the property of smoothness of the pose. We then design a lightweight neural network to regress the pose by developing a differential pose layer. Finally, we introduce a novel loss function and gradient descent method to better supervise the proposed lightweight pose network. Extensive experiments on the camera re-localization task on the Cambridge Landmarks and 7-Scenes datasets demonstrate the superior predictive accuracy and benefits of our method in comparison with the state-of-the-art.

A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the cost, the deployability, and the scalability of a boundary. In this paper, we propose a new RFID-based boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free, low-cost, and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna DoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary.

With the proliferation of RFID-enabled applications, multiple readers are required for the complete coverage of numerous tags in a large-scale RFID system. In this scenario, we sometimes pay more attention to a subset of tags instead of all, which are referred to as key tags. In this paper, we study an under-investigated problem key tag distribution identification, which aims to identify which key tags are beneath which readers. This is crucial for efficiently managing specific items of interest, which can quickly pinpoint key tags and help RFID readers covering these tags collaborate to improve the tag inventory efficiency. Since key tags typically make up a small part of all tags, it is time consuming to deal with all tags in the traditional way. We propose a protocol called Kadept that identifies the key tag distribution by using a sophisticatedly designed filter that teases out key tags as well as assigns each of them a singleton slot for response. With this design, a great number of trivial (non-key) tags will keep silent and free up bandwidth resources for key tags, and each key tag is sorted in a collision-free way and can be identified with only 1-bit response, which significantly improves the time efficiency. We theoretically analyze how can we optimize protocol parameters of Kadept and conduct extensive simulations under different tag distribution scenarios. Compared with the state-of-the-art, Kadept can improve the time efficiency by a factor of 3.7×, when the ratio of key tags to all tags is 0.1.

With the proliferation of RFID-enabled applications, multiple RFID readers (or reader antennas) must be used to provide full coverage to any deployment area beyond the communication range of a single reader. However, reader collision together with tag-to-tag collision seriously degrades the system performance or even blocks out some tags from being read. To address this problem, this paper proposes a time-efficient protocol stack that is tailored to the tag identification in a multi-reader RFID system, which consists of two protocols: one is for eliminating reader collision (RCE) and the other is for avoiding tag-to-tag collision (TCE). In RCE, we enable multiple readers to work in parallel for maximizing the number of tags to be read per unit of time. Where RCE shines is that it well addresses the problem of unbalanced load by each reader due to uneven tag distributions. Namely, in existing work, the readers with fewer tags covered will finish the tag identification earlier than other readers (with more tags). After that, these readers have to wait for all readers until they are done. This is a waste of time. The solution of RCE is to take the reader with the minimum number of tags as the reference and iteratively to update the set of concurrent readers. Besides, in TCE, we use bit-level tag response to resolve tag-to-tag collision and increase the number of useful slots, so does the read throughput. Theoretical analysis and simulation results show that the above two protocols can jointly improve the inventory efficiency and reduce the identification time by more than 50%, in comparison to the state-of-the-art. We validate TIMR’s performance by comparing its results on a real-world library data set with simulated data, demonstrating consistency across both settings.

In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an "early-fusion" or "late-fusion" manner. Such one-size-fits-all approaches suffer from a dilemma of failing to fully utilize the characteristic of each modality or to maximize the inter-modality complementarity. To address the problem, we propose a novel end-to-end framework, which consists of 2D and 3D branches with multiple bidirectional fusion connections between them in specific layers. Different from previous work, we apply a point-based 3D branch to extract the LiDAR features, as it preserves the geometric structure of point clouds. To fuse dense image features and sparse point features, we propose a learnable operator named bidirectional camera-LiDAR fusion module (Bi-CLFM). We instantiate two types of the bidirectional fusion pipeline, one based on the pyramidal coarse-to-fine architecture (dubbed CamLiPWC), and the other one based on the recurrent all-pairs field transforms (dubbed CamLiRAFT). On FlyingThings3D, both CamLiPWC and CamLiRAFT surpass all existing methods and achieve up to a 47.9% reduction in 3D end-point-error from the best published result. Our best-performing model, CamLiRAFT, achieves an error of 4.26% on the KITTI Scene Flow benchmark, ranking 1st among all submissions with much fewer parameters. Besides, our methods have strong generalization performance and the ability to handle non-rigid motion.

Performing accurate sensing in diverse environments is a challenging issue in wireless sensing technologies. Existing solutions usually require collecting a large number of samples to train a classifier for every environment, or further assume similar sample distribution between different environments such that a model trained in one environment can be transferred to another. In this paper, we propose RF-Siamese, an RFID-based gesture sensing approach that achieves comparable accuracy to existing solutions but requires only a few samples in each eivironment. RF-Siamese leverages Siamese networks to distinguish different gestures with only a small number of samples and is enhanced by several novel designs to achieve high accuracy in diverse environments. First, the network structure and parameters (e.g., loss function and distance metric) are carefully designed to be suitable for RFID gesture recognition. Second, a permutation-based dataset generation strategy is proposed to make full use of the collected samples to enhance the recognition accuracy. Third, a template matching method is proposed to extend the Siamese network to classify multiple gestures. Extensive experiments on commercial RFID devices demonstrate that RF-Siamese achieves a high accuracy of 0.93 with only one sample of each gesture when recognizing 18 different gestures, while state-of-the-art approaches based on transfer learning and meta learning achieve an accuracy of only 0.59 and 0.70, respectively.

Weak-signal detection underlies a variety of ubiquitous computing applications, such as wireless sensing and machinery fault diagnosis. Stochastic resonance (SR) provides a new way for weak-signal detection by boosting undetectable signals with added white noise. However, existing work has to take a long time to search optimal parameter settings for SR, which cannot fit well some time-critical applications. In this paper, we propose an adaptive SR scheme (ASR) that can amplify the original signal at a low cost in time. The basic idea is that we find that the potential parameter is a key factor that determines the performance of SR. By treating the system as a feedback loop, we can dynamically adjust the potential parameters according to the output signals and make SR happen adaptively. ASR answered two technical questions: how can we evaluate the output signal and how can we tune the potential parameters quickly towards the optimal. In ASR, we first design a spectral-analysis based solution to examine whether SR happens using continuous wavelet transform. After that, we reduce the parameter tuning problem to a constrained non-linear optimization problem and use the sequential quadratic programming to iteratively optimize the potential parameters. We implement ASR and apply it in two ubiquitous computing applications: respiration-rate detection and machinery fault diagnosis. Extensive experiments show that ASR outperforms the state-of-the-art.

In recent years, Neural Radiation Fields (NeRF) have shown tremendous potential in encoding highly-detailed 3D geometry and environmental appearance, thus making it a promising alternative to traditional explicit maps for robot localization. However, current NeRF localization methods suffer from significant computational overheads, primarily resulting from the large number of iterations or particle samples required, as well as the additional computational demands associated with the estimation of the initial pose through multimodal sensors. To overcome these challenges, we propose a novel and time-efficient NeRF localization pipeline, named FVLoc-NeRF. This pipeline solely employs RGB monocular images as input and leverages a retrieval method to obtain the initial pose. Subsequently, the pose update is derived using the Perspective-n-Point (PnP) algorithm, thereby considerably reducing the number of iterations and accelerating the localization process. Our extensive experimental results clearly demonstrate that FVLoc-NeRF is much faster than the state-of-the-art method.

People counting tracks the number of people entering or exiting any given space in real-time, which helps make intelligent business decisions and improve quality of service (QoS) for a range of commercial applications, e.g., retail analytics, queue management, and occupancy monitoring. Existing CV-based and CSI-based solutions suffer from problems ranging from blind zones and privacy breaches to low robustness and complicated system calibration. In this paper, we propose a new solution to the counting problem with carry-on RFID tags. The basic idea behind the curtain is that when a person is under an RFID reader's coverage zone, his/her carry-on tags can be read by this reader at the same time. We can classify the tags based on the records of reading profiles. Hence, this problem can be boiled down to a clustering problem. To achieve this goal, we build a feature vector for each tag by gathering its reading profile, specify the distance between two feature vectors by combining the Hausdorff distance and the Euclidean distance, and perform a maximum density clustering algorithm to get the distributions of each tag. Afterward, we design an outlier detection algorithm to screen out the cluster centers from the non-cluster ones. The number of cluster centers is treated as the number of people. We implement the RFID-based counting model in a commodity RFID system. Extensive experimental results show that our approach is superior to existing solutions under different scenarios.

With 30 billion RFID tags sold worldwide in 2021, a common basic functionality needed by RFID-enabled applications is cardinality estimation --- to quickly estimate the number of distinct tags in an RFID system. Although many advanced solutions have been proposed over the past decade, they suffer from one major limitation in practical use: they need to either modify the existing RFID standard or obtain MAC-layer information, both of which however cannot be supported by commercial off-the-shelf (COTS) devices. In this paper, we revisit the counting problem and propose a novel counting scheme called average time duration based counter (ATD) that quickly estimates the number of distinct tags in a standards-compliant manner. Compared with existing work, the competitive advantage of ATD is that it can be directly deployed on a COTS RFID system, with no need for any hardware modifications. In ATD, we found a new and measurable indicator --- the time duration between two adjacent singleton slots, which depends on the number of tags. Following this observation, we derive the theoretical relationship between the time indicator and the number of tags and then give the proof of the estimation as well as its parameter settings. Additionally, we propose a flag-flipping solution to address the overlapping problem in the multi-reader case. We implement ATD in a COTS RFID system with 1000 tags. Experimental results show that ATD is 4.2× faster than the baseline of tag inventory; the performance gain will be further increased in a larger RFID system.

Abstract Orientation estimation is one of the core problems in several computer vision tasks. Recently deep learning techniques combined with the Bingham distribution have attracted considerable interest towards this problem when considering ambiguities and rotational symmetries of objects. However, existing works suffer from two issues. First, the computational overhead for calculating the normalisation constant of the Bingham distribution is relatively high. Second, the choice of loss functions is uncertain. In light of these problems, we present an online deep Bingham network to estimate the orientation of objects. We sharply reduce the computational overhead of the normalisation constant by directly applying a numerical integration formula. Additionally, we are the first to give theorems on the convexity and Lipschitz continuity of the Bingham distribution's negative log‐likelihood, which formally indicates that it is a proper choice of the loss function. We test our method on three public datasets, namely the UPNA, the T‐LESS and Pascal3D+, showing that our method outperforms the state‐of‐the‐art in terms of orientation accuracy and time efficiency, which can reduce the runtime by more than 6 h compared to the offline methods. The ablation experiments further demonstrate the effectiveness and robustness of our model.

Heavy-hitter detection is a fundamental task in network traffic measurement and security. Existing work faces the dilemma of suffering dynamic and imbalanced traffic characteristics or lowering the detection efficiency and flexibility. In this paper, we propose a flexible sketch called SwitchSketch that embraces dynamic and skewed traffic for efficient and accurate heavy-hitter detection. The key idea of SwitchSketch is allowing the sketch to dynamically switch among different modes and take full use of each bit of the memory. We present an encoding-based switching scheme together with a flexible bucket structure to jointly achieve this goal by using a combination of design features, including variable-length cells, shrunk counters, embedded metadata, and switchable modes. We further implement SwitchSketch on the NetFPGA-1G-CML board. Experimental results based on real Internet traces show that SwitchSketch achieves a high Fβ-Score of threshold-t detection (consistently higher than 0.938) and over 99% precision rate of top-k detection under a tight memory size (e.g., 100KB). Besides, it outperforms the state-of-the-art by reducing the ARE by 30.77%\sim99.96%. All related implementations are open-sourced.

Lip reading can help people with speech disorders to communicate with others and provide them with a new channel to interact with the world. In this paper, we design and implementHearMe, an accurate and real-time lip-reading system built on commercial RFID devices. HearMe can be used to accurately recognize different words in a pre-defined vocabulary without limitations in light conditions and can be used in multiple user scenarios by leveraging RFID's ability in identifying different users. We design an effective data collection strategy to well capture the tiny and complex signal patterns caused by mouth motion and propose a set of algorithms to extract signal profiles related to mouth motions and mitigate interference factors like multi-path. A carefully designed set of features, including time-domain statistical features and frequency-domain features, are then extracted from the signal to lift the recognition accuracy at the word level. To reduce training costs when the model is used in a new environment, a transfer-learning-based approach is adopted to enhance the robustness of the model in cross-environment scenarios. Experimental results show that HearMe detects speaking actions of the user with an accuracy higher than 0.95 and recognizes different words in a 20-words vocabulary with an average accuracy higher than 0.88. Moreover, the latency of HearMe ($\sim$150ms) is nearly two orders of magnitude less than traditional approaches, making it applicable to practical scenarios that require real-time lip reading.

In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an “early-fusion“ or “late-fusion“ manner. Such one-size-fits-all approaches suffer from a dilemma of failing to fully utilize the characteristic of each modality or to maximize the inter-modality complementarity. To address the problem, we propose a novel end-to-end framework, called CamLiFlow. It consists of 2D and 3D branches with multiple bidirectional connections between them in specific layers. Different from previous work, we apply a point-based 3D branch to better extract the geometric features and design a symmetric learnable operator to fuse dense image features and sparse point features. Experiments show that CamLiFlow achieves better performance with fewer parameters. Our method ranks 1st on the KITTI Scene Flow benchmark, outperforming the previous art with 1/7 parameters. Code is available at https://github.com/MCG-NJU/CamLiFlow.

In the context of Industrial Internet of Things (IIoT), RFID technologies have been widely applied to locate or track tagged objects for achieving item-level intelligence. However, prior localization work encounters two main issues. First, the phase measurement usually contains physical deviation. Existing localization work generally takes the physical center of an RFID antenna as its phase center, which is a key factor in improving localization accuracy but actually different from the physical center in practice. Second, the non-linear localization model is likely to be too complex to run on edge nodes with limited computing resources. In this paper, we present a LInear localizatiON solution, called LION, to perform the phase calibration for antennas with no need for the complex computation nor strong limitations. Specifically, we provide a novel lightweight model to pinpoint the actual antenna position quickly and accurately. Compared to previous localization methods, we reduce the intersection of circles or hyperbolas into radical lines, which greatly reduces the computation cost while guaranteeing the high accuracy. Further, to adapt to the complex environment with various ambient noise and multi-path effect, we leverage the weighted least square method to determine the optimal position. Moreover, we propose an adaptive parameter selection scheme to automatically choose optimal parameters for localization. In this way, LION is able to perform the accurate localization robustly. We implement LION using commercial RFID devices, and evaluate its performance extensively. Experimental results show the necessity of phase calibration as well as the high time efficiency of LION, e.g., the average accuracy improves by 6× and 2.1× for 2D and 3D localization, and the average time consuming is 0.02s and 1.8s for 2D and 3D cases.

Fast and accurate pose estimation is essential for many robotic applications such as SLAM, manipulation, and 3D point registration. Existing solutions to this problem suffer from either high computation overhead due to the nonlinear features or accuracy loss due to linear approximation. In this paper, we propose a dual quaternion feedback particle filter (DQFPF) that can capture the nonlinear factors in the observation model and use the optimal control theory to estimate the pose. To avoid particle degeneracy caused by sequential importance sampling and resampling, we present a feedback particle update formula to speed up the optimization with fewer particles being sampled. Simulation results show that in known corresponding cases our approach can converge to the correct pose more efficiently than the state-of-the-art. A similar conclusion can also be drawn in real applications of unknown corresponding cases, i.e., point cloud stitching and visual odometry estimation.

RFID technologies have been widely used for item-level object monitoring and tracking in industrial applications. In this paper, we study the problem of range detection in a commodity RFID system, which aims to quickly figure out whether there are any target tags that hold specific data between a lower and upper boundary. This is important to help users pinpoint tagged objects of interest (if any) and give an early warning for reducing the potential risk, e.g., temperature monitoring for fire safety. We propose a time-efficient protocol called encoding range query (EnRQ). The basic idea is to use a sparse vector to separate target tags from the others with a few select commands. The sparse vector is specifically designed by encoding the tag’s data based on notational systems. We implement EnRQ in commodity RFID systems with no need for any hardware modifications. Extensive experiments show that EnRQ can improve the time efficiency by more than 40% on average, compared with the state-of-the-art.

Radio frequency identification (RFID) based localization has attracted increasing attentions due to competitive advantages of RFID tags: unique identification, low-cost, and battery-free. Although many advanced phase-based localization methods are proposed, few of them take fully the unknown phase center (PC) and the phase offset (PO) into account, which however are the key factors in fine-grained localization. In this paper, we propose a novel localization algorithm called Segment Aligned Hologram (SAH) that jointly calibrates the PC and the PO. SAH first builds a phase matrix and then designs a phase alignment algorithm based on the phase matrix for reducing the multi-path effect. Afterwards, SAH constructs a hologram for calibration and localization, which greatly reduces the system errors. We implement SAH through commercial RFID devices. Extensive experiments show that SAH can achieve a fine-grained localization accuracy in both the lateral and the radial directions with only a single antenna.

With the rapid development of deep neural networks and end-to-end learning techniques, automatic speech recognition (ASR) systems have been deployed into our daily and assist in various tasks. However, despite their tremendous progress, ASR systems could also suffer from software defects and exhibit incorrect behaviors. While the nature of DNN makes conventional software testing techniques inapplicable for ASR systems, lacking diverse tests and oracle information further hinders their testing. In this paper, we propose and implement a testing approach, namely ASR, specifically for the DNN-driven ASR systems. ASRTest is built upon the theory of metamorphic testing. We first design the metamorphic relation for ASR systems and then implement three families of transformation operators that can simulate practical application scenarios to generate speeches. Furthermore, we adopt Gini impurity to guide the generation process and improve the testing efficiency. To validate the effectiveness of ASRTest, we apply ASRTest to four ASR models with four widely-used datasets. The results show that ASRTest can detect erroneous behaviors under different realistic application conditions efficiently and improve 19.1% recognition performance on average via retraining with the generated data. Also, we conduct a case study on an industrial ASR system to investigate the performance of ASRTest under the real usage scenario. The study shows that ASRTest can detect errors and improve the performance of DNN-driven ASR systems effectively.

Accurate absolute pose regression is one of the key challenges in robotics and computer vision. Existing direct regression methods suffer from two limitations. First, some noisy scenarios such as poor illumination conditions are likely to result in the uncertainty of pose estimation. Second, the output n-dimensional feature vector in the Euclidean space $\mathbb{R}^n$ cannot be well mapped to $SE(3)$ manifold. In this work, we propose a deep dual quaternion network that performs the absolute pose regression on $SE(3)$. We first develop an antipodally symmetric probability distribution over the unit dual quaternion on $SE(3)$ to model uncertainties and then propose an intermediary differential representation space to replace the final output pose, which avoids the mapping problem from $\mathbb{R}^n$ to $SE(3)$. In addition, we introduce a backpropagation method that considers the continuousness and differentiability of the proposed intermediary space. Extensive experiments on the camera re-localization task on the Cambridge Landmarks and 7-Scenes datasets demonstrate that our method greatly improves the accuracy of the pose as well as the robustness in dealing with uncertainty and ambiguity, compared to the state-of-the-art.

With the rising of demands for novel human-computer interaction approaches in the 2D plane, a number of intelligent devices come into being. For example, Microsoft Surface Dial supports simple clicks and rotations for the interaction with computer. However, these approaches are dedicated devices, and they might require batteries or have limited functions. In this paper, we propose RF-Dial to realize a light-weight, battery-free and functional 2D human-computer interaction solution via commercial off-the-shelf (COTS) passive RFID tags. What RF-Dial shines is that it can easily turn an ordinary object, e.g., a board eraser, into an intelligent interaction device. By deploying a tag array on the side face of the object together with a dipole tag on the top face, RF-Dial cannot only track the rigid motion of the object but also detect the touch gesture of a user on the surface of the object, including translation, rotation, click, press and hold, and swipe. To do the motion tracking, RF-Dial builds a phase-based model that captures the translation and the rotation of the tagged object simultaneously, by jointly exploiting the information of phase variations and the topology of the tag array. To detect the touch gesture, RF-Dial builds an RSSI-based model that uses the impact of the touching finger on the tag antenna’s impedance to estimate the touch position in real time, which is robust to environmental factors like position or orientation. We implemented a prototype of RF-Dial with commodity RFID devices. Extensive experiments show that RF-Dial achieves an accurate rigid motion tracking, with a small error of 0.6cm for the translation tracking, and a small error of 1.9 degrees for the rotation estimation. Besides, RF-Dial can also detect the touch gesture accurately, as the 90 percent of touch position errors are less than 2.09mm.

RFID technology has been widely applied in a range of applications such as inventory control, warehouse management and supply chain logistics. Many practical applications need to search a given set of tags (calledwanted tags) to determine which of them are present in the system, which is usually calledtag searching. Existing tag searching protocols suffer performance bottleneck and the time efficiency and need to be improved. The bottleneck stems from two factors. First, the existing methods validate the wanted tags in a random way due to the randomness of the hash function, which unavoidably generate many useless slots. Second, in order to achieve the predefined reliability requirement, the existing methods have to repeatedly validate target tags multiple times. In this paper, we design new tag searching techniques of compact exclusive validation that break through the existing performance bottleneck from two aspects. First, our protocols avoid slot waste. Different from random tag validation, our protocols validate tags orderly by mapping the wanted tags and the slots in a one-to-one manner, which makes the reader be able to validate tags in every slot. Second, our protocols avoid repeated tag responding. By combining two lightweight indicators, we rapidly filter out non-wanted tags so that there is no interference when validating the wanted tags. Hence, we need to validate each wanted tag only once, which avoids redundant validation and greatly improves time efficiency. Our protocols work with the assumption that the rough number of tags in the system can be obtained by using existing estimation algorithms, but they do not need to know exactly which tags are in the system. Theoretical analysis illustrates our methods achieve linear time complexity. Extensive experimental results show that, our best protocol can improve the time efficiency by up to 81 percent when compared with the-state-of-art solution.

In RFID systems, the privacy problem has attracted increasing attention as the tags have extremely limited on-chip resources and may blindly respond to unauthorized readers. One widely used solution is to deploy one or more blocker tags to collide the RF signal sent from protected tags all the time. We in this paper investigate tag identification in privacy-sensitive RFID systems when we have the authorized reader(s). The tag identification under the blocker environment becomes more challenging since the protected tags and blocker tags will always respond concurrently, leading to unreconciled collisions. Therefore, we propose three tag identification protocols IIP, SIP and SUIP, which meet three application requirements: privacy, accuracy, and efficiency. IIP iteratively deactivates blocking tags as well as labeling genuine tags, and finally identifies tags by avoiding all the unreconciled collisions. SIP further avoids the slot waste by carrying out a filter to separate genuine tags from others in advance. Furthermore, SUIP separates the incremental unknown tags from others by using a filter with improved performance and proposes a collision reconciliation technique to accelerate the identification process. Simulation results demonstrate that our protocols are able to guarantee any identification accuracy in a privacy-protected way.

RFID is becoming ubiquitously available in our daily life. After RFID tags are deployed to make attached objects identifiable, a natural next step is to communicate with the tags and collect their information for the purpose of tracking tagged objects or monitoring their surroundings in real-time. In this paper, we study an under-investigated problem range detection in a commodity RFID system, which aims to check if there are any tags with the data between an upper and lower boundary in a time-efficient way. This is important especially in a large RFID system, which can help users quickly pinpoint the target tags (if any) and give an early warning to users for taking urgent actions and reducing the potential risk in the nascent stage. We propose two tailored protocols, selective query and range query, to achieve range detection within the scope of the C1G2 standard. The novelty is that, instead of querying each tag, we exploit the capability of C1G2-compatible selection and quickly separate target tags from others by silencing most of tags. The final result is that range query is able to achieve a range detection with only one query command. We implement the proposed protocols in commodity RFID systems, with no need for any hardware modifications. Extensive experiments show that range query is able to improve the time efficiency by an order of magnitude, compared with the baseline.

With the development of deep neural networks, machine translation has achieved significant progress and integrated with people’s daily lives to assist in various tasks. However, machine translators, which are essentially one kind of software, also suffer from software defects. Translation errors might cause misunderstanding or even lead to marketing blunders, and political crisis. Thus, almost all translation service providers have feedback channels of incorrect translations to collect training data and improve product performance. Inspired by the syntax structure analysis, we introduce the constituency invariance, which reflects the structural similarity between a simple sentence and sentences derived from it, to test machine translators. We implement it into an automated tool CIT to detect translation errors by checking the constituency invariance relation between the translation results. CIT adopts constituency parse trees to represent the syntactic structures of sentences and employs an efficient data augmentation method to derive multiple new sentences based on one sentence. To validate CIT, we experiment with three widely-used machine translators, i.e., Bing Microsoft Translator, Google Translate, and Youdao Translator. With 600 seed sentences as input, CIT detects 2212, 1910, and 1590 translation errors with around 77% precision. We have submitted detected errors to the development teams. Until we submit this paper, Google, Bing, and Youdao have fixed 15.4%, 32.0%, 14.3% of reported errors, respectively.

Temperature sensing plays a significant role in upholding quality assurance and meeting regulatory compliance in a wide variety of applications, such as fire safety and cold chain monitoring. However, existing temperature measurement devices are bulky, cost-prohibitive, or battery-powered, making item-level sensing and intelligence costly. In this paper, we present a novel tag-based thermometer called Thermotag, which uses a common passive RFID tag to sense the temperature with competitive advantages of being low-cost, battery-free, and robust to environmental conditions. The basic idea of Thermotag is that the resistance of a semiconductor diode in a tag's chip is temperature-sensitive. By measuring the discharging period through the reverse-polarized diode, we can estimate the temperature indirectly. We propose a standards-compliant measurement scheme of the discharging period by using a tag's volatile memory and build a mapping model between the discharging period and temperature for accurate and reliable temperature sensing. We implement Thermotag using a commercial off-the-shelf RFID system, with no need for any firmware or hardware modifications. Extensive experiments show that the temperature measurement has a large span ranging from 0 °C to 85 °C and a mean error of 2.7 °C.

Currently, the logistic industry has introduced 3D reconstruction to monitor the package placement in the warehouse. Previous 3D reconstruction solutions mainly utilize computer vision or sensor-based methods, which are restricted to the line-of-sight or the battery life. Therefore, we propose a passive RFID-based solution, called RF-3DScan, to perform 3D reconstruction on tagged packages, including the package orientation and the package stacking. The basic idea is that a moving antenna can obtain RF-signals from the tags attached on packages with the 1D linear mobile scanning. Through extracting phase differences to build angle profiles for each tag, RF-3DScan derives their relative positions, further determines the package orientation and the coarse-grained package stacking. By simply performing the 2D scanning, RF-3DScan can provide the fine-grained package stacking determination. We implement a prototype system of RF-3DScan and evaluate its performance in real settings. Our experiment results show that RF-3DScan can achieve about 92.5 percent identification accuracy of the bottom face, and an average error about 4.08° of thethe rotation angle. For the package stacking, 1D scanning can achieve the comparable performance in comparison with 2D scanning.

In RFID systems, tag grouping is to inform each tag in the interrogator's RF field which group it belongs to by assigning them a group ID. With this information, the reader can write the same data into a group of tags in one transmission by taking their shared group ID as the destination address, which plays a vital role in improving inventory efficiency. Existing work proposes some efficient protocols but suffers from two defects: slot waste and data redundancy. To address these problems, we in this paper propose a two-phase grouping scheme that improves the grouping efficiency via slot reuse as well as data compression. For slot waste, we try to reconcile collision slots and turn some of them into useful again by re-assigning a new slot to collision tags, which increases the number of useful slots and reduces slot waste. For data redundancy, we encode group IDs with lossless coding algorithms and reduce the data redundancy caused by repetitive transmission of group IDs. Two coding methods,Huffman codingandarithmetic coding, are adopted to shorten the length of the bit vector comprised by group IDs. Theoretical analyses and extensive simulations show that our best protocol improves the time efficiency by 51.9 percent compared with the state-of-the-art.

RFID is becoming ubiquitously available in our daily life. After RFID tags are deployed to make attached objects identifiable, a natural next step is to communicate with the tags and collect their information for the purpose of tracking tagged objects or monitoring their surroundings in real time. In this paper, we study an under-investigated problem range detection in a commodity RFID system, which aims to check if there are any tags with the data between an upper and lower boundary in a time-efficient way. This is important especially in a large RFID system, which can help users quickly pinpoint the target tags (if any) and give an early warning to users for taking urgent actions and reducing the potential risk in the nascent stage. We propose two tailored protocols, selective query and range query (RQ), to achieve range detection within the scope of the C1G2 standard. The novelty is that, instead of querying each tag, we exploit the capability of C1G2-compatible selection and quickly separate target tags from others by silencing most of tags. The final result is that our best protocol RQ is able to achieve a range detection with only one query command. We implement the proposed protocols in commodity RFID systems, without any modifications of hardware. Extensive experiments show that RQ is able to improve the time efficiency by near 30×, compared with the baseline.

Shelf scanning is one of the most important processes for inventory management in a library, which is able to help library users find the misplaced books and pinpoint where they are. Traditional solutions suffer from intensive manual labor, long scanning delay, or high-cost infrastructure renewal, which is a great barrier to commercial adoption. In this paper, we propose an HF RFID system that can perform the shelf scanning automatically by combining robotics and RFID technology, where the mobile robot is used for replacing the library staff and the RFID is for locating the tagged books on the bookshelves. The biggest challenge is that the commodity HF RFID device cannot capture RF phase, which is a fine-grained metric with high resolution for locating. To address this problem, we break down the task of localization into two stages, tier-level localization and order estimation, and use fuzzy logic to model and estimate the tagged book’s position. We implement the system and deploy it into four libraries for practical use. Long-term extensive experimental results demonstrate that our system provides a finegrained book localization with an accuracy of a few centimeters.

Determining an object’s spatial pose (including orientation and position) plays a fundamental role in a variety of applications, such as automatic assembly, indoor navigation, and robot driving. In this paper, we design a fine-grained pose sensing system called Tag-Compass that attaches a single tag to an object (whose size may be small) and identifies the tagged object’s pose by determining the spatial orientation and position of the tag. We exploit thepolarizationproperties of the RF waves used in the communications between an RFID reader and the tag on the object. Polarization mismatch between the tag and the reader’s antenna affects the received signal strength at the reader. From the measured signal strength values, we are able to deduce the tag’s pose through a series of transformations and deviation minimization. We propose a system design for Tag-Compass and implement a prototype. We evaluate the performance of Tag-Compass through extensive experiments using the prototype. The experimental results show that Tag-Compass provides accurate estimate of object orientation with a median error of just 2.5° when the tag’s position is known and a median error of 3.8° when the tag’s position is unknown. In the latter case, Tag-Compass will provide an estimate of tag position as a byproduct of orientation sensing, with an accuracy comparable to the state of the art. It is practically appealing to find both the orientation and the position of an object using a single method, instead of having to deploy two different methods.

Unknown RFID tags appear when tagged items are not scanned before being moved into a warehouse, which can even cause serious security issues. This paper studies the practically important problem of unknown tag detection. Existing solutions either require low-cost tags to perform complex operations or beget a long detection time. To this end, we propose the Collision-Seeking Detection (CSD) protocol, in which the server finds out a collision-seed to make massive known tags hash-collide in the last $N$ slots of a time frame with size $f$ . Thus, all the leading ${f-N}$ pre-empty slots become useful for detection of unknown tags. A challenging issue is that, computation cost for finding the collision-seed is very huge. Hence, we propose a supplementary protocol called Balanced Group Partition (BGP), which divides tag population into $n$ small groups. The group number $n$ is able to trade off between communication cost and computation cost. We also give theoretical analysis to investigate the parameters to ensure the required detection accuracy. The major advantages of our CSD+BGP are two-fold: (i) it only requires tags to perform lightweight operations, which are widely used in classical framed slotted Aloha algorithms. Thus, it is more suitable for low-cost tags; (ii) it is more time-efficient to detect the unknown tags. Simulation results reveal that CSD+BGP can ensure the required detection accuracy, meanwhile achieving $1.7\times $ speedup in the single-reader scenarios and $3.9\times $ speedup in the multi-reader scenarios than the state-of-the-art detection protocol.

Radio Frequency IDentification (RFID) technology has been widely used in missing tag identification to reduce the economic loss caused by theft. Although many advanced works have been proposed, they cannot work properly in an open RFID system with unexpected tags. That is because the unexpected tags may reply to the reader when it is the missing tags’ turn, such that the replies are mistakenly treated as the missing tags’, leading to failure to detect the missing tags. To solve the problem, this article proposes an order-based missing tag identification protocol (OMTI) that efficiently identifies all missing tags even with the presence of unexpected tags. The key of OMTI is that we dynamically assign each tag an exclusive slot rather than use random hashes in traditional design. Namely, OMTI off-line serializes all known tags in advance by assigning each of them a unique and continuous slot, on-line identifies missing tags by checking tags’ responses in each slot, and dynamically updates the slot assignment to ensure the high efficiency of the next execution. Besides, we enhance OMTI to make it embrace newly added tags and spread to the multi-reader case from the single one. The simulation results demonstrate that OMTI can significantly reduce the identification time compared to the state-of-the-art.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer-to-peer communications among nearby tags, the emerging networked tags make a fundamental enhancement to today's RFID systems. This new capability supports a series of system-level functions in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper makes the first attempt to design a new communication model that is specifically tailored to efficient implementation of system-level functions in networked tag systems, in terms of energy cost and execution time. Instead of exploiting complex mechanisms for collision detection and resolution, we propose a collision-resistant communication model (CCM) that embraces the collision in tag communications and utilizes it to merge the data from different sources in a benign way. Two fundamental applications: RFID estimation and missing-tag detection, are presented to illustrate how CCM assists efficient system-level operations in networked tag systems. Simulation results show that the system-level applications through CCM are able to reduce the energy cost and execution time by one order of magnitude, compared with the ID-collection based solution.

Misshelving is an age-old problem in a library. If this happens, users can hardly find their wanted books, leading to a waste of book resources. In this paper, we investigate the problem of book localization through mobile RFID scanning. According to extensive experiments, we find that the phase data (that are used to do localization) beyond the reader antenna's main lobe are noisy; taking these data as the input of book localization is likely to lower the localization accuracy. However, it is not easy to figure out which data are noisy. To address this problem, we propose a fine-grained localization method called FILM, which can achieve cm-level book localization through the following three steps. First, it collects the phase profile for each tagged book as is. Second, it sophisticatedly chooses three samples from the phase profile and locates the tagged book by modeling the three samples. Since there are a great number of 3-combinations, we can get many candidates of the book localization. Third, it uses the Gaussian kernel density model to determine whether the candidates are good or not. The basic idea is that, the noisy data give rise to uncertain estimates, whereas the clean data will always get an estimate close to the ground truth. Therefore, the position with the highest density is the most likely to be the tag's position. By this means, the noisy data do not contribute to the final estimate, which improves the localization accuracy. We setup a prototype of FILM and experiment results show that FILM is superior to the state-of-the-art, with a small localization error of 1.3cm.

Sleep monitoring plays an important role in many medical applications, including SIDS prevention, care of patients with pressure ulcers, and assistance to patients with sleep apnea, where studies have shown that autonomous and continuous monitoring of sleep postures provides useful information for lowering health risk. Existing systems are designed based on electrocardiogram, cameras and pressure sensors, which are expensive to deploy, intrusive to privacy, or uncomfortable to use. This paper presents TagSheet, the first sleep monitoring system based on passive RFID tags, which provides a convenient, non-intrusive, and comfortable way of monitoring the sleeping postures. It does not require attaching any tag directly to a patient’s body. Tags are taped under a bed sheet. With a combination of hierarchical recognition, image processing and polynomial fitting, the proposed system identifies body postures based on the observed variation caused by the patient body to the backscattered signals from tags. The system does not require any personalized data training, making it plug-n-play in use. One additional advantage is that the system can also estimate the patient’s respiration rate. This is particularly helpful in assisting patients with sleep apnea. We have implemented a prototype system, and experiments show that the system performs posture identification with an accuracy up to 96.7% and in the meantime it measures the respiration rate with a small error of about 0.7 bpm (breath per minute).

High write throughput plays a vital role in improving the time efficiency of RFID-enabled applications, such as password update and over-the-air programming. In this paper, we make a fresh attempt to study the under-investigated problem of group write and discuss how to improve the write throughput in the commodity RFID system. By conducting extensive experiments, we reveal that the select operation specified by the C1G2 standard takes up the large overhead of a write cycle, which is the key factor in determining the write efficiency. Based on this finding, we propose an efficient write bundling (WB) scheme that bundles multiple writes up and executes them together in a burst mode, which greatly reduces the number of selects and thereby amplifies the write throughput. In WB, a carefully designed bit vector is assigned to each tag for connecting multiple tags into a run, such that a single select command is able to pick these tags concurrently. Besides, WB integrates multiple select commands into one by running a series of logic operations on the inventoried flags, which are used to indicate whether or not a tag is active. We implement WB in a commodity RFID system, with no need of any software modifications or hardware argument. Extensive experiment results show that WB is able to reduce the number of selects by 87.5% and produce a $2\times$ write amplification, compared with the C1G2-compatible exclusive write.

Deep Neural Network(DNN) techniques have been prevalent in software engineering. They are employed to facilitate various software engineering tasks and embedded into many software applications. However, because DNNs are built upon a rich data-driven programming paradigm that employs plenty of labeled data to train a set of neurons to construct the internal system logic, analyzing and understanding their behaviors becomes a difficult task for software engineers. In this paper, we present an instance-based visualization tool for DNN, namely NeuralVis, to support software engineers in visualizing and interpreting deep learning models. NeuralVis is designed for: 1). visualizing the structure of DNN models, i.e., neurons, layers, as well as connections; 2). visualizing the data transformation process; 3). integrating existing adversarial attack algorithms for test input generation; 4). comparing intermediate layers' outputs of different inputs. To demonstrate the effectiveness of NeuralVis, we design a task-based user study involving ten participants on two classic DNN models, i.e., LeNet and VGG-12. The result shows NeuralVis can assist engineers in identifying critical features that determine the prediction results. Video: https://youtu.be/solkJri4Z44

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to asinformation sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.

RFID tags have been widely deployed to report valuable information about tagged objects or surrounding environment. To collect such information, the key is to avoid the tag-to-tag collision in the open wireless channel. Polling, as a widely used anti-collision protocol, provides a request-response way to interrogate tags. The basic polling however needs to broadcast the tedious tag ID (96 bits) to query a tag, which is time-consuming. For example, collecting only 1-bit information (e.g., battery status) but with 96-bit overhead is a great limitation. This paper studies how to design efficient polling protocols to collect tag information quickly. The basic idea is to minimize the length of the polling vector as well as to avoid useless communication. We first propose an efficient Hash polling protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than 16 bits (the number of tags is less than 100,000). We then propose a tree-based polling protocol (TPP) that avoids redundant transmission in HPP. By constructing a binary polling tree, TPP transmits only different postfix of the neighbor polling vectors; the same prefix is reserved without any retransmission. The result is that the length of the polling vector reduces to only 3.4 bits. Finally, we propose an incremental polling protocol (IPP) that updates the polling vector based on the difference in value between the current polling vector and the previous one. By sorting the indices and dynamically updating them, IPP drops the polling vector to 1.6 bits long, 60 times less than 96-bit IDs. Extensive simulation results show that our best protocol IPP outperforms the state-of-the-art information collection 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.

Radio frequency identification (RFID) technology has rich applications in cyber-physical systems, such as warehouse management and supply chain control. Often in practice, tags are attached to objects belonging to different groups, which may be different product types/manufacturers in a warehouse or different book categories in a library. As RFID technology evolves from single-group to multiple-group systems, there arise several interesting problems. One of them is to identify the popular groups, whose numbers of tags are above a pre-defined threshold. Another is to estimate arbitrary moments of the group size distribution, such as sum, variance, and entropy for the sizes of all groups. In this paper, we consider a new problem which is to estimate all these statistical metrics simultaneously in a time-efficient manner without collecting any tag IDs. We solve this problem by a protocol named generic moment estimator (GME), which allows the tradeoff between estimation accuracy and time cost. According to the results of our theoretical analysis and simulation studies, this GME protocol is several times or even orders of magnitude more efficient than a baseline protocol that takes a random sample of tag groups to estimate each group size.

Radio-frequency identification (RFID) technology has been widely adopted in various industries and people's daily lives. This paper studies a fundamental function of spatial-temporal joint cardinality estimation in distributed RFID systems. It allows a user to make queries over multiple tag sets that are present at different locations and times in a distributed tagged system. It estimates the joint cardinalities of those tag sets with bounded error. This function has many potential applications for tracking product flows in large warehouses and distributed logistics networks. The prior art is either limited to jointly analyzing only two tag sets or is designed for a relative accuracy model, which may cause unbounded time cost. Addressing these limitations, we propose a novel design of the joint cardinality estimation function with two major components. The first component is to record snapshots of the tag sets in a system at different locations and periodically, in a time-efficient way. The second component is to develop accurate estimators that extract the joint cardinalities of chosen tag sets based on their snapshots, with a bounded error that can be set arbitrarily small. We formally analyze the bias and variance of the estimators, and we develop a method for setting their optimal system parameters. The simulation results show that, under predefined accuracy requirements, our new solution reduces time cost by multiple folds when compared with the existing work.

Nowadays, the demand for novel approaches of 2D human-computer interaction has enabled the emergence of a number of intelligent devices, such as Microsoft Surface Dial. Surface Dial realizes 2D interactions with the computer via simple clicks and rotations. In this paper, we propose RF-Dial, a battery-free solution for 2D human-computer interaction based on RFID tag arrays. We attach an array of RFID tags on the surface of an object, and continuously track the translation and rotation of the tagged object with an orthogonally deployed RFID antenna pair. In this way, we are able to transform an ordinary object like a board eraser into an intelligent HCI device. According to the RF-signals from the tag array, we build a geometric model to depict the relationship between the phase variations of the tag array and the rigid transformation of the tagged object, including the translation and rotation. By referring to the fixed topology of the tag array, we are able to accurately extract the translation and rotation of the tagged object during the moving process. Moreover, considering the variation of phase contours of the RF-signals at different positions, we divide the overall scanning area into the linear region and non-linear region in regard to the relationship between the phase variation and the tag movement, and propose tracking solutions for the two regions, respectively. We implemented a prototype system and evaluated the performance of RF-Dial in the real environment. The experiments show that RF-Dial achieved an average accuracy of 0. 6cm in the translation tracking, and an average accuracy of 1.9°in the rotation tracking.

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.

In RFID systems, a cloning attack is to fabricate one or more replicas of a genuine tag, so that these replicas behave exactly the same as the genuine tag and fool the reader for getting legal authorization, leading to potential financial loss or reputation damage for the corporations. These replicas are called clone tags. Although many advanced solutions have been proposed to combat cloning attack, they need to either modify the MAC- layer protocols or increase extra hardware resources, which cannot be deployed on commercial off-the-shelf (COTS) RFID devices for practical use. In this paper, we take a fresh attempt to counterattack tag cloning based on COTS RFID devices and the universal C1G2 standard, without any software redesign or hardware augment needed. The basic idea is to use the RF signal profile to characterize each tag. Since these physical-layer data are measured by the reader and susceptible to various environmental factors, they are hard to be estimated by the attackers; let alone be cloned. Even so, we assert that it is challenging to identify clone tags as the signal data from a genuine tag and its clones are all mixed together. Besides, the tag moving has a great impact on the measured RF signals. To overcome these challenges, we propose a clustering-based scheme that detects the cloning attack in the still scene and a chain- based scheme for clone detection in the dynamic scene, respectively. Extensive experiments on COTS RFID devices demonstrate that the detection accuracy of our approaches reaches 99.8% in a still case and 99.3% in a dynamic scene.

Radio Frequency Identification (RFID) technology has been widely used in missing tag identification to reduce the economic loss caused by theft. Although many advanced works have been proposed, they cannot work properly in an open RFID system with unexpected tags. To solve the problem, this paper proposes an order-based missing tag identification protocol (OMTI) that efficiently identifies all missing tags even with the presence of unexpected tags. The key of OMTI is that we dynamically assign each tag a slot rather than use random hashes in traditional design. Namely, OMTI off-line sorts all known tags in advance by assigning each of them a unique and continuous slot, on-line identifies missing tags by checking tags' responses in each slot, and dynamically updates the slot assignment to ensure the high efficiency of the next execution. Besides, we enhance OMTI to make it works in the case of newly added tags. The simulation results demonstrate that OMTI can reduce the execution time by a factor of 12, compared with the polling-based approach.

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag's memory as required. Since such information reflects the category attributes, all tags in the same category carry the identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag's response in a category is sufficient. In this paper, we investigate the new problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose an efficient two-phase sampling protocol (TPS). By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category by broadcasting only 7.5-bit polling vector (very efficient when compared to the 96-bit tag ID). We theoretically analyze the protocol performance and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that TPS outperforms the benchmark, greatly improving the sampling performance.

RFID technologies are making their way into numerous applications, including inventory management, supply chain, product tracking, transportation, logistics, etc. One important application is to automatically detect anomalies in RFID systems, such as missing tags, unknown tags, or cloned tags due to theft, management error, or targeted attacks. Existing solutions are all designed to detect a certain type of RFID anomalies, but lack a general functionality for detecting different types of anomalies. This paper attempts to propose a general framework for anomaly detection in RFID systems, thereby reducing the complexity for readers and tags to implement different anomaly-detection protocols. We introduce a new concept of differential Bloom filter (DBF), which turns physical-layer signal data into a segmented Bloom filter that encodes the IDs of abnormal tags. As a case study, we propose a protocol that builds DBF for identifying all missing tags in an efficient way. We implement a prototype for missing-tag identification using USRP and WISP tags to verify the effectiveness our protocol, and use large-scale simulations for performance evaluation. The results show that our solution can significantly improve time efficiency, when comparing with the best existing work.

Identifying an object's spatial direction (or orientation) plays a fundamental role in a variety of applications, such as automatic assembly, indoor navigation, and robot driving. In this paper, we design a fine-grained direction finding system called Tag-Compass that attaches a single tag to an object (whose size may be small) and identifies the tagged object's orientation by determining the spatial direction of the tag. We exploit the polarization properties of the RF waves used in the communications between an RFID reader and the tag on the object. Polarization mismatch between the tag and the reader's antenna affects the received signal strength at the reader. From the measured signal strength values, we are able to deduce the tag's direction through a series of transformations and deviation minimization. We propose a system design for Tag-Compass and implement a prototype. We evaluate the performance of TagCompass through extensive experiments using the prototype. The experimental results show that Tag-Compass provides accurate direction estimates with a median error of just 2.5° when the tag's position is known and a median error of 3.8° when the tag's position is unknown.

Shelf scanning is one of the most important processes for inventory management in a library. It helps the librarians and library users discover the miss-shelved books and pinpoint where they are, improving the quality of service. By traditional means, however, manually checking each bookshelf suffers from extremely intensive labor and long scanning delay. Although some existing RFID-enabled approaches have been proposed, they suffer from either high-cost infrastructure or complicated system deployment, forming a great barrier to commercial adoption. In light of this, we in this paper propose a smart system called RF-Scanner that can perform the shelf scanning automatically by combining the robot technology and the RFID technology. The former is used for replacing the librarians and liberating them from intensively manual labor. The later is installed on the robot and moves with the robot to scan the on-the-shelf books. We formulate two important issues concerned by librarians, the book localization and the lying-down book detection, and give the sophisticated solutions to them. Besides, we implement RF-Scanner and put it into practical use in our school library. Long-term experiments and studies show that RF-Scanner provides fine-grained book localization with a mean error of just 1.3 cm and accurate detection accuracy of lying-down books with a mean error of 6%.

Nowadays, 3D reconstruction has been introduced in monitoring the package placement in logistic industry-related applications. Existing 3D econstruction methods are mainly based on computer vision or sensor-based approaches, which are limited by the line-of-sight or battery life constraint. In this paper, we propose RF-3DScan to perform 3D reconstruction on tagged packages via passive RFID, by attaching multiple reference tags onto the surface of the packages. The basic idea is that by moving the antenna along straight lines within a constrained 2-dimensional space, the antenna obtains the RF-signals of the reference tags attached on the packages. By extracting the phase differences to build the angle profile for each tag, RF-3DScan can compare the angle profiles of the different reference tags and derive their relative positions, then further determine the package orientation and stacking for 3D reconstruction. We implement RF- 3DScan and evaluate its performance in real settings. The experiment results show that the average identification accuracy of the bottom face is about 92.5%, and the average estimation error of the rotation angle is about 4.08o.

In RFID systems, the privacy problem has attracted increasing attention as the tags have extremely limited on-chip resources and may blindly respond to unauthorized readers. To protect the privacy, one widely used solution is to deploy one or more blocker tags to collide the RF signal sent from protected tags all the time. In this paper, we investigate the problem of tag identification in privacy-sensitive RFID systems when we have the authorized reader(s). Due to the presence of blocker tags, the tag identification becomes more challenging since the protected tags and blocker tags will always respond concurrently, leading to unreconciled collisions. To overcome this challenge, we in this paper propose two efficient tag identification protocols IIP and SIP, which meet three application requirements: privacy, accuracy, and efficiency. IIP iteratively deactivates blocking tags as well as labeling genuine tags, and finally identifies tags by avoiding all the unreconciled collisions. On top of IIP, SIP further avoids the slot waste by carrying out a filter to separate genuine tags from others in advance. Simulation results demonstrate that our protocols are able to guarantee any identification accuracy in a time-efficient and privacy-protected way.

Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking products. The grouping problem is to efficiently inform all tags which groups they belong to, such that tags in the same group have the same group ID. With this information, the reader can transmit data through efficient multicast rather than tedious unicast. However in the dynamic RFID system, where the group IDs change frequently, existing works suffer from low time- efficiency. In light of this, this paper proposes the binary grouping (BIG) protocol to improve the grouping performance in the dynamic RFID system. BIG follows three design principles. First, for the to-be-grouped tags, BIG separates them from the entire tag set before grouping, so that the not-to-be-grouped tags will not participate in the following grouping, reducing the running delay. Second, for the update of group ID, BIG flips only the bits of group ID which differ from the previous ones, instead of updating all bits of the group ID, saving the communication overhead. Third, BIG adopts a Huffman coding based grouping scheme, which classifies tags into different groups in an efficient way. The extensive simulations show BIG outperforms the existing promising works.

In our daily life, some sensitive cargos (e.g., refrigerators) are required to keep one right side up. If these items are turned upside down due to incorrect transportation or wrong storage, some unpredictable exception will happen, leading to potential economic loss. In this paper, we propose a lightweight system TagUP that uses RFID to detect the upside-down exception. By dynamically changing the frequency of the electromagnetic wave and comparing the phase difference between two measurements, TagUP is able to achieve the detection accuracy of 98.0% when two tags are placed 60cm apart.

In RFID systems, tags may blindly respond to any readers authorized or not, leading to privacy leakage. A feasible solution to this issue is to deploy blocker tags that emulate and behave like the genuine tags. With the blocker tags, there is always a virtual tag produced by the blocker tag that responds to the reader when the genuine tag to be protected replies, leading to irreconcilable collisions and thus preventing privacy-leakage. In this paper, we take the fresh attempt to study the problem of missing tag identification in blocker- enabled RFID systems. Unlike traditional missing tag identification, the reader in our problem will always get the responses from blocker tags even the genuine tags are lost, which makes the existing solutions unavailable. To address this problem, we propose a Group-based Missing Tag Identification (GMTI) protocol that first uses the grouping technology to divides the entire tag set into three subsets and later separately deals with each of them in different ways. The final result is that GMTI is able to identify any missing tags in a time-efficient, privacy-protected, and complete way.

Radio frequency identification (RFID) is an automatic identification technology that brings a revolutionary change to quickly identify tagged objects from the collected tag IDs. Considering the misplaced and newly added tags, fast identifying such unknown tags is of paramount importance, especially in large-scale RFID systems. Existing solutions can either identify all unknown tags with low time-efficiency, or identify most unknown tags quickly by sacrificing the identification accuracy. Unlike existing work, this paper proposes a protocol that utilizes physical layer (PHY) information to identify the intact unknown tag set with high efficiency. We exploit the physical signals in collision slots to separate unknown tags from known tags, a new technique to speed up the ID collection. Such new technique was verified in an RFID prototype system using the USRP-based reader and WISP tags. We also evaluated our protocol to show the efficiency of leveraging PHY signals to successfully get all unknown tag IDs without wasted known tag ID transmission. Simulation results show that our protocols outperform prior unknown tag identification protocols. For example, given 1000 unknown tags and 10 000 known tags, our best protocol has 56.8% less time to the state-of-the-art protocol when collecting all unknown tag IDs.

Radio Frequency Identification (RFID) brings a revolutionary change in a range of applications by automatically monitoring and tracking products. With the proliferation of RFID-enabled applications, multiple readers are needed for ensuring the full coverage of numerous RFID tags. In this paper, we focus on the tag distribution problem in multi-reader RFID systems. The problem is to fast identify the tag set beneath each reader, which is a fundamental premise of efficient product inventory and management. Only with such tag set information can we localize specific tags in a reader and expedite the tag query information collection. As an RFID system usually contains a large number of tags and multiple readers, the traditional solution to identify tags by individual readers is highly time inefficient. We propose an Inference-Based protocol (IB) that identifies the tag distribution based on information inference rules and the aggregated physical signals to improve operational efficiency. In our protocol, three kinds of inference rules based on internal information reported by a single reader, external information shared by multiple readers, and history information retained by the system are fully exploited to infer tag distribution. With these rules, all readers can cooperatively work together and quickly obtain the tag distribution in the system. We also build a prototype RFID system using the USRP-based reader and WISP programmable tags, and then implement the IB protocol. The experimental results and extended simulations show that IB outperforms the state-of-the-art protocols.

Radio-frequency identification (RFID) technologies have been widely used in many applications, including inventory management, supply chain, product tracking, transportation, logistics, etc. Tag estimation, which is to estimate the cardinality of a single tag set, is an important research topic. This paper expands the estimation research as follows: It performs joint estimation between two tag sets (which exist at different locations or at the same location but different times). More importantly the estimation is fine-grained in an effort to accommodate common practical scenarios, where each tag set consists of tags belonging to different categories. For any two given tag sets, we want to know the detailed information about the joint property of each category, instead of just the aggregate information of the whole sets. Furthermore, due to the open nature of RFID communications, it is often desirable that tag estimation can be performed in an anonymous way without revealing the tags' ID information. To support these requirements, we develop a new technique called mask bitmap that can encode a tag set without requiring the tags to report their IDs or category IDs. Any two mask bitmaps of different tag sets can be combined to perform category-level joint estimation. Through formal analysis, we determine how to set system parameters to meet a given accuracy requirement that can be arbitrarily set. Extensive simulation results confirm that the proposed solution can yield accurate category-level estimates in an efficient way, and preserve tags' anonymity as well.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer-to-peer communications among nearby tags, the emerging networked tags make a fundamental enhancement to today's RFID systems. This new capability supports a series of system-level functions in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper makes the first attempt to design a new communication model that is specifically tailored to efficient implementation of system-level functions in networked tag systems. Instead of exploiting complex mechanisms for collision detection and resolution, we propose a collision-resistent communication model (CCM) that embraces the collision in tag communications and utilizes it to merge the data from different sources in a benign way. Two fundamental applications: RFID estimation and missing-tag detection, are presented to illustrate how CCM assists efficient system-level operations in networked tag systems. Simulation results show that system-level applications through CCM outperform those through ID collection.

Polling is a widely used anti-collision protocol that interrogates RFID tags in a request-response way. In conventional polling, the reader needs to broadcast 96-bit tag IDs to separate each tag from others, leading to long interrogation delay. This paper takes the first step to design fast polling protocols by shortening the polling vector. We first propose an efficient Hash Polling Protocol (HPP) that uses hash indices rather than tag IDs as the polling vector to query each tag. The length of the polling vector is dropped from 96 bits to no more than log(n) bits (n is the number of tags). We then enhance HPP (EHPP) to make it not only more efficient but also more steady with respect to the number of tags. To avoid redundant transmissions in both HPP and EHPP, we finally propose a Tree-based Polling Protocol (TPP) that reserves the invariant portion of the polling vector while updates only the discrepancy by constructing and broadcasting a polling tree. Theoretical analysis shows that the average length of the polling vector in TPP levels off at only 3.44, 28 times less than 96-bit tag IDs. We also apply our protocols to collect tag information and simulation results demonstrate that our best protocol TPP outperforms the state-of-the-art information collection protocol.

Radio-frequency identification (RFID) technologies have been widely used in many applications. Tag estimation, which is to estimate the cardinality of a single tag set, is an important research topic. This paper expands the estimation research as follows: It performs joint estimation between two tag sets, and more importantly the estimation is fine-grained in an effort to accommodate common practical scenarios, where each tag set consists of tags belonging to different categories. For any two given tag sets, we want to know the detailed information about the joint property of each category, instead of just the aggregate information of the whole sets. Furthermore, due to the open nature of RFID communications, it is often desirable that category-level joint estimation can be performed in an anonymous way without revealing the tags' IDs. To support these requirements, we develop a new technique called mask bitmap that can encode a tag set without requiring the tags to report their IDs or category IDs. Any two mask bitmaps that encode different tag sets can be combined to perform category-level joint estimation. Simulation results confirm that the proposed solution can yield accurate category-level estimates and preserve tags' anonymity.

Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking products. For many RFID-enabled applications, fast searching a particular group of products is practically important in a large-scale RFID system. Different from previous searching protocols, we propose a physical layer tag searching (PLAT) protocol, which makes three fundamental improvements. First, PLAT can exactly pinpoint the search result without false positives at a small delay expense. Second, based on the physical layer signals, PLAT can interpret the accurate number of tags replying in a collision slot, speeding up the execution of tag searching. Third, PLAT can take the global view of the accurate replying information from slots to extract each tag identifier, further improving the protocol performance. We also implement a prototype system based on the USRP and WISP platform. Experimental results validate the feasibility of our protocol. The extensive simulations show PLAT produces the performance gain by a factor of above 2 compared with the state-of-the-art works.

The grouping problem in RFID systems is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of$m$tags simultaneously in multicast, which improves the efficiency by$m$times when comparing with unicast. This paper studies this practically important but not yet thoroughly investigated grouping problem in large RFID system. We start with a straightforward solution called the Enhanced Polling Grouping EPG protocol. We then propose a time-efficient Filter Grouping FIG protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping CCG protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. We further enhance CCG to make it perform better with very large groups. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.

In RFID systems, the grouping problem is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on the unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of m tags simultaneously in multicast, which improves the efficiency by m times when comparing with unicast. We study fast grouping protocols in large RFID systems. To the best of our knowledge, it is the first attempt to tackle this practically important yet uninvestigated problem. We start with a straightforward solution called the Enhanced Polling Grouping (EPG) protocol. We then propose a time-efficient FIltering Grouping (FIG) protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping (CCG) protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.

Radio Frequency Identification (RFID) technology brings a revolutionary change in warehouse management by automatically monitoring and tracking. Considering the misplaced and newly added tags, fast identifying such unknown tags is of paramount importance, especially in large-scale RFID systems. Unlike existing work, this paper proposes a fast Physical-layer Unknown Tag Identification (PUTI) protocol that gathers intact unknown tag set with high identification efficiency. PUTI utilizes physical layer aggregated signals to separate all unknown tags from known tags and then collects IDs from only the entire unknown tag set. We also optimize the parameter setting and analyze protocol performance in theory. Simulation results show that PUTI outperforms prior unknown tag identification protocols. For example, given a RFID system with 1,000 unknown tags and 10,000 known tags, PUTI represents reduction of 37.7% when compared with the state-of-the-art protocol.

Radio-Frequency Identification (RFID) technology has fostered many object monitoring systems. Along with this trend, tagged objects' value and privacy become a primary concern. A corresponding important problem is to verify the intactness of a set of tagged objects without leaking tag identifiers (IDs). However, existing solutions necessitate the knowledge of tag IDs. Without tag IDs as a priori, this paper studies intactness verification in anonymous RFID systems. We identify three critical solution requirements, that is, deterministic verification, anonymity preservation, and scalability. We propose Cardiff and Divar, two crypto-free, lightweight protocols that isolate tag IDs from intactness verification and satisfy solution requirements. Cardiff explores tag cardinality as intactness proof while Divar leverages Direct-Sequence Spread Spectrum (DSSS) enabled RFID. Both analytical and simulation results demonstrate that Cardiff and Divar can satisfy the requirements of accuracy, privacy, and scalability.

Radio Frequency Identification (RFID) has dramatically streamlined supply chain management by automatically monitoring and tracking commodities. Considering the proliferation of RFID data volume, distributed storage is more applicable and scalable than centralized storage for distributed query processing. Traditional distributed RFID data storage requires each distribution center to locally store raw RFID data, leading to data redundancy, storage and query inefficiency. In this paper, we design an efficient distributed storage model by leveraging Bloom filters to save storage space and improve query efficiency. Meanwhile, we establish corresponding query processing schemes to locally support existence queries and path queries, which are two kinds of most popular queries in the supply chain management. A local query can be completed with constant time complexity regardless of data volume. Experiments demonstrate that our storage model outperforms the traditional one in terms of both space and time efficiency.

One primary issue which hinders large-scale Radio Frequency Identification (RFID) applications is the imperfect read rate. According to Friis equation and experiments, we find that the dipole tag's orientation plays a significant role in read performance both in theory and practice. In this paper, we consider how to deploy multiple reader antennas to alleviate the negative effect caused by uncontrollable tag orientations in item-level applications. Different from previous work, we expand the candidate antenna positions from the portal to real three dimensional space and establish two estimation functions based on sphere coverage models under different polarized environment. Meanwhile, we provide an improved enumeration scheme with leaping and layering search strategies based on sphere quadtree (SQT) model to find optimized deployment.