https://duanch.github.io/publications/PublicationsHugoBlox Kit (https://hugoblox.com)en-usSun, 01 Mar 2026 00:00:00 +0000https://duanch.github.io/media/icon_hu_da05098ef60dc2e7.pnghttps://duanch.github.io/publications/https://duanch.github.io/publications/2026-mmwave-radar-based-unsupervised-gesture-recognition-via-image-aligned-heterogeneous-domain/Sun, 01 Mar 2026 00:00:00 +0000https://duanch.github.io/publications/2026-mmwave-radar-based-unsupervised-gesture-recognition-via-image-aligned-heterogeneous-domain/<h2 id="abstract">Abstract</h2> <p>Human Gesture Recognition (HGR) using mmWave radar has become increasingly promising due to its exceptional contactless perception sensitivity. Conventional approaches predominantly rely on supervised models to learn radar signals, thus incurring substantial costs associated with annotation. To address this limitation, certain works embrace transfer learning to effectively transfer knowledge from labeled source domain to unlabeled target domain, achieving unsupervised recognition in the target domain. However, existing transfer-based methods still necessitate large-scale labeled source domain radar data, thereby constraining their practical applicability. To this end, we propose a novel unsupervised solution for mmWave-based HGR by transferring public image gestures to radar data, eliminating the need for acquiring labeled radar data in source domain. We aim to establish heterogeneous alignment between images and radar signals, facilitating cross-domain transfer. Initially, we mitigate the negative impact of data heterogeneity by employing sophisticated signal processing techniques to convert raw radar signals into gesture trajectories. Subsequently, we introduce an Adversarial-Contrastive Domain Transfer Model (ACDTM) to achieve fine-grained alignment. ACDTM not only confuses the source and target domains by adversarial learning, enabling the acquisition of domain-invariant features, but also designs a robust similarity matrix to facilitate intra-class alignment through contrastive learning. Additionally, ACDTM conducts adversarial self-training on target domain with pseudo-labeled distribution. Our experimental findings substantiate that the unsupervised accuracy achieves about 80$\sim$92% on different mmWave gesture datasets, outperforming existing unsupervised HGR schemes by large margins. Code is available at .</p> <h2 id="citation">Citation</h2> <p>Q. Feng, K. Cheng, and C. Duan, “mmWave Radar-Based Unsupervised Gesture Recognition via Image-Aligned Heterogeneous Domain Transfer,” IEEE Transactions on Mobile Computing (TMC), vol. 25, no. 3, pp. 3279-3296, 2026.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Feng Q, Cheng K, Duan C. mmWave Radar-based Unsupervised Gesture Recognition via Image-Aligned Heterogeneous Domain Transfer[J]. IEEE Transactions on Mobile Computing (TMC), 2026, 25(3): 3279-3296.</p>https://duanch.github.io/publications/2026-imbalanced-semi-supervised-learning-for-wifi-gesture-recognition-via-dynamic-threshold-bas/Thu, 01 Jan 2026 00:00:00 +0000https://duanch.github.io/publications/2026-imbalanced-semi-supervised-learning-for-wifi-gesture-recognition-via-dynamic-threshold-bas/<h2 id="abstract">Abstract</h2> <p>WiFi sensing advancements facilitate the capture of human gestures from wireless signals, ensuring both privacy preservation and robustness under low-light conditions. Deep learning-based WiFi Human Gesture Recognition (HGR) demonstrates remarkable performance in handling complex gestures. To reduce labeling efforts, recent years have seen the emergence of semi-supervised WiFi HGR, leveraging massive amounts of unlabeled data. However, existing semi-supervised schemes often assume a balanced class distribution and utilize a fixed threshold for selecting pseudo-labels of unlabeled samples, leading to low performance for minority classes and decreased model generalization on real-world imbalanced datasets. To address this issue, we propose a novel semi-supervised WiFi HGR approach with dynamic pseudo-labeling thresholds to handle imbalanced class distribution, incorporating Spatial-Temporal Attention (STA) networks. Unlike using a fixed threshold for all unlabeled samples, our design implements class-independent thresholds for different classes, dynamically adjusting them by encoding pseudo-label distribution during training. To emphasize critical features in informative areas within the WiFi signals, we incorporate both spatial self-attention and temporal attention mechanisms to dynamically learn salient features and identify pivotal frames, respectively. Moreover, we introduce adaptive WiFi data augmentations that propel the semi-supervised framework and enhance model robustness. Experimental results on the Widar3.0 dataset reveal that our approach outperforms existing semi-supervised methods by large margins in accuracy, effectively mitigating imbalanced bias and enhancing model generalization.</p> <h2 id="citation">Citation</h2> <p>Q. Feng, C. Duan, J. Xue, C. Li, F. Huang, X. Zhang, J. Weng, and P. Yu, &ldquo;Imbalanced Semi-Supervised Learning for WiFi Gesture Recognition via Dynamic Threshold-Based Spatio-Temporal Attention Networks,&rdquo; IEEE Transactions on Mobile Computing (TMC) , vol. 25, no. 1, pp. 483-499, 2026.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Feng Q, Duan C, Xue J, et al. Imbalanced Semi-Supervised Learning for WiFi Gesture Recognition via Dynamic Threshold-Based Spatio-Temporal Attention Networks[J]. IEEE Transactions on Mobile Computing (TMC), 2026, 25(1): 483-499.</p>https://duanch.github.io/publications/2025-non-intrusive-item-authentication-with-high-robustness-for-rfid-enabled-logistics/Mon, 19 May 2025 00:00:00 +0000https://duanch.github.io/publications/2025-non-intrusive-item-authentication-with-high-robustness-for-rfid-enabled-logistics/<h2 id="abstract">Abstract</h2> <p>RFID technology has found extensive applications in the realm of smart logistics, facilitating rapid package sorting and tracking. Harnessing the potential of RFID in addressing concerns related to logistics security holds significant promise and meaning. To detect the status of goods inside the packages and provide timely alerts for instances of loss, replacement, or damage, we propose RF-Express, a non-intrusive and anti-interference item authentication method. RF-Express enables seamless authentication of items during the logistics process, utilizing pervasive RFIDs with almost no additional costs. Specifically, by tactfully arranging a pair of tags on each package and extracting representative features from the backscatter signals of the tags, we manage to discern the authenticity of the item with high precision across various multipath environments. Besides, a feature matching algorithm based on the triplet network is employed to further reinforce the robustness of the system. We have implemented the RF-Express prototype on commercial devices and conducted extensive experiments. RF-Express achieves a true acceptance rate of 90.97% and a true rejection rate of 94.38% on average under common attack scenarios.</p> <h2 id="citation">Citation</h2> <p>J. Xue, C. Duan, F. Li, Q. Feng, Z. Wang, and Y. Zhu, “Non-Intrusive Item Authentication with High Robustness for RFID-Enabled Logistics,” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2025, pp. 1-10.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Xue J, Duan C, Li F, et al. Non-Intrusive Item Authentication with High Robustness for RFID-Enabled Logistics[C]//Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). 2025: 1-10.</p>https://duanch.github.io/publications/2025-tagrecon-fine-grained-3d-reconstruction-of-multiple-tagged-packages-via-rfid-systems/Sat, 01 Mar 2025 00:00:00 +0000https://duanch.github.io/publications/2025-tagrecon-fine-grained-3d-reconstruction-of-multiple-tagged-packages-via-rfid-systems/<h2 id="abstract">Abstract</h2> <p>To meet the new requirements of Industry 4.0, the logistics field has introduced 3D reconstruction technology. Computer vision-based solutions face challenges like bad lighting conditions and line-of-sight constraints. Meanwhile, the widespread adoption of RFID tags in supply chains offers an opportunity to enhance current reconstruction methods. In this article, we propose TagRecon, a fine-grained multi-object 3D reconstruction scheme utilizing well-deployed RFIDs. Specifically, TagRecon transforms the task of reconstruction into a problem of estimating 3D bounding boxes for tagged packages. By placing dual anchor tags on each target package, TagRecon enables accurate inference of the package’s translation and rotation using RFID-based localization and orientation sensing. Our scheme introduces a novel method to estimate rotations and translations for tagged packages, utilizing the known geometric relationship of anchor tags. Besides, to achieve simultaneous reconstruction of multiple packages, we manage to match tags from various packages through the correlation between anchor tag pairs. As far as we know, this is the first RFID-based solution that can simultaneously realize 3D translation and rotation estimation of multiple objects to a fine granularity. Experiments validate TagRecon achieves a 28.0 cm translation error and 6.8°, 6.0°, and 7.5° rotation errors for roll, pitch, and yaw angles on average.</p> <h2 id="citation">Citation</h2> <p>Z. Wang, C. Duan, J. Xue, F. Li, Q. Feng, Y. Zhu, and Z. Zhou, “TagRecon: Fine-Grained 3D Reconstruction of Multiple Tagged Packages via RFID Systems,” ACM Transactions on Sensor Networks(TOSN), vol. 21, no. 2, pp. 1-25, 2025.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Wang Z, Duan C, Xue J, et al. TagRecon: Fine-Grained 3D Reconstruction of Multiple Tagged Packages via RFID Systems[J]. ACM Transactions on Sensor Networks(TOSN), 2025, 21(2): 1-25.</p>https://duanch.github.io/publications/2024-evit-privacy-preserving-image-retrieval-via-encrypted-vision-transformer-in-cloud-computin/Thu, 01 Aug 2024 00:00:00 +0000https://duanch.github.io/publications/2024-evit-privacy-preserving-image-retrieval-via-encrypted-vision-transformer-in-cloud-computin/<h2 id="abstract">Abstract</h2> <p>Image retrieval systems help users to browse and search among extensive images in real time. With the rise of cloud computing, retrieval tasks are usually outsourced to cloud servers. However, the cloud scenario brings a daunting challenge of privacy protection as cloud servers cannot be fully trusted. To this end, image-encryption-based privacy-preserving image retrieval (PPIR) schemes have been developed, which first extract features from cipher-images, and then build retrieval models based on these features. Yet, most existing PPIR approaches extract shallow features and design trivial unsupervised retrieval models, resulting in insufficient expressiveness for the cipher-images. In this paper, we propose a novel paradigm named Encrypted Vision Transformer (EViT), which advances the discriminative representations capability of cipher-images. First, to capture comprehensive ruled information, we extract multi-level local length sequence and global Huffman-Code frequency features from the cipher-images which are encrypted by permutation encryption, sign encryption, and stream cipher during the JPEG compression process. Second, we design the modified self-supervised Vision Transformer with Huffman-embedding and propose two robust data augmentations on cipher-images to improve representation power of the retrieval model. Moreover, our proposal can be easily adapted to unsupervised or supervised settings. Extensive experiments reveal that EViT achieves both excellent encryption and retrieval performance, outperforming current schemes in terms of retrieval accuracy by large margins while protecting image privacy effectively. Code is publicly available at .</p> <h2 id="citation">Citation</h2> <p>Q. Feng, P. Li, Z. Lu, C. Li, Z. Wang, Z. Liu, C. Duan, F. Huang, J. Weng, and P. Yu, “EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing,” IEEE Transactions on Circuits and Systems for Video Technology(TCSVT), vol. 34, no. 8, pp. 7467-7483, 2024.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Feng Q, Li P, Lu Z, et al. EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing[J]. IEEE Transactions on Circuits and Systems for Video Technology(TCSVT), 2024, 34(8): 7467-7483.</p>https://duanch.github.io/publications/2023-i-can-hear-you-without-a-microphone-live-speech-eavesdropping-from-earphone-motion-sensors/Wed, 17 May 2023 00:00:00 +0000https://duanch.github.io/publications/2023-i-can-hear-you-without-a-microphone-live-speech-eavesdropping-from-earphone-motion-sensors/<h2 id="abstract">Abstract</h2> <p>Recent literature advances motion sensors mounted on smartphones and AR/VR headsets to speech eavesdropping due to their sensitivity to subtle vibrations. The popularity of motion sensors in earphones has fueled a rise in their sampling rate, which enables various enhanced features. This paper investigates a new threat of eavesdropping via motion sensors of earphones by developing EarSpy, which builds on our observation that the earphone’s accelerometer can capture bone conduction vibrations (BCVs) and ear canal dynamic motions (ECDMs) associated with speaking; they enable EarSpy to derive unique information about the wearer’s speech. Leveraging a study on the motion sensor measurements captured from earphones, EarSpy gains abilities to disentangle the wearer’s live speech from interference caused by body motions and vibrations generated when the earphone’s speaker plays audio. To enable user-independent attacks, EarSpy involves novel efforts, including a trajectory instability reduction method to calibrate the waveform of ECDMs and a data augmentation method to enrich the diversity of BCVs. Moreover, EarSpy explores effective representations from BCVs and ECDMs, and develops a convolutional neural model with Connectionist Temporal Classification (CTC) to realize accurate speech recognition. Extensive experiments involving 14 participants demonstrate that EarSpy reaches a promising recognition for the wearer’s speech.</p> <h2 id="citation">Citation</h2> <p>Y. Cao, F. Li, H. Chen, X. Liu, C. Duan, and Y. Wang, “I Can Hear You Without a Microphone: Live Speech Eavesdropping From Earphone Motion Sensors, ” in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2023, pp. 1-10.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Cao Y, Li F, Chen H, et al. I Can Hear You Without a Microphone: Live Speech Eavesdropping From Earphone Motion Sensors[C]//Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). 2023: 1-10.</p>https://duanch.github.io/publications/2023-hearasl-your-smartphone-can-hear-american-sign-language/Mon, 01 May 2023 00:00:00 +0000https://duanch.github.io/publications/2023-hearasl-your-smartphone-can-hear-american-sign-language/<h2 id="abstract">Abstract</h2> <p>Sign language is expressed by movements of the hands and facial expressions, which is mainly used by the deaf community. Although some gesture recognition methods are put forward, they possess different defects and are not applicable to deal with the sign language recognition (SLR) problem. In this article, we propose an end-to-end American SLR system with built-in speakers and microphones in smartphones, which enables SLR at both word level and sentence level. The high-level idea is to use the inaudible acoustic signal to estimate channel information and capture the sign language in real time. We use channel impulse response to represent each sign language gesture, which can realize finger-level recognition. We also pay attention to conversion movements between two words and treat them as an additional label when training the sentence-level classification model. We implement a prototype system and run a series of experiments that demonstrate the promising performance of our system. Experimental results show that our approach can achieve an accuracy of 97.2% at word-level recognition and word error rate of 0.9% at sentence-level recognition, respectively.</p> <h2 id="citation">Citation</h2> <p>Y. Wang, F. Li, Y. Xie, C. Duan, and Y. Wang, “HearASL: Your Smartphone Can Hear American Sign Language,” IEEE Internet of Things Journal(IOT), vol. 10, no. 10, pp. 8839-8852, 2023.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Wang Y, Li F, Xie Y, et al. HearASL: Your Smartphone Can Hear American Sign Language[J]. IEEE Internet of Things Journal(IOT), 2023, 10(10): 8839-8852.</p>https://duanch.github.io/publications/2023-rosense-refining-los-signal-phase-for-robust-rfid-sensing-via-spinning-antenna/Sat, 01 Apr 2023 00:00:00 +0000https://duanch.github.io/publications/2023-rosense-refining-los-signal-phase-for-robust-rfid-sensing-via-spinning-antenna/<h2 id="abstract">Abstract</h2> <p>RFID sensing leveraging backscatter signal features (e.g., phase shift) from tags has gained increasing popularity in numerous applications, but also suffers from negative impacts of environmental multipaths. Past works to address it rely on extra customized devices, labor-intensive offine training or frequency channel hopping, all of which are nonubiquitous or ineffective for real-life adoption. This paper presents RoSense, a universal method to alleviate multipath reflections&rsquo; impacts by spinning the reader antenna, thus enabling more robust RFID sensing. Besides, RoSense requires no RF devices or offline training, and operates in a nonintrusive manner. The key insight of RoSense is to exploit two properties of line-of-sight (LOS) signal when spinning the antenna, i.e., the linearity of phase changes and stability of received signal strength to attenuate the non-linear and non-monotonic effect of multipath signals and refine the phase shift of LOS signal. We have implemented a prototype of RoSense with COTS devices and studied two cases for evaluation: material identification and object localization. Experimental results show that RoSense can improve the material identification accuracy by 16.22% and reduce the mean localization error by 39.93%, greatly outperforming the state-of-the-art solutions.</p> <h2 id="citation">Citation</h2> <p>Y. Zhu, C. Duan, and X. Ding, “RoSense: Refining LOS Signal Phase for Robust RFID Sensing via Spinning Antenna,” ACM SIGMETRICS Performance Evaluation Review(PER), vol. 50, no.4, pp. 53-55, 2023.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X. RoSense: Refining LOS Signal Phase for Robust RFID Sensing via Spinning Antenna[J]. ACM SIGMETRICS Performance Evaluation Review(PER), 2023, 50(4): 53-55.</p>https://duanch.github.io/publications/2023-towards-location-and-orientation-independent-rfid-authentication-with-cots-devices/Mon, 13 Mar 2023 00:00:00 +0000https://duanch.github.io/publications/2023-towards-location-and-orientation-independent-rfid-authentication-with-cots-devices/<h2 id="abstract">Abstract</h2> <p>To authenticate tags against counterfeiting, RF fin-gerprinting technique is widely exploited. However, the status of tagged item is always changed by movement, rotation and other operations. When the item&rsquo;s location or orientation changes, the capability of past methods would be severely affected and their supported authentication ranges are fairly small. To overcome this challenge, we propose the first-of-its-kind method FreeAuth to achieve certain location- and orientation-independent RFID authentication without any customized devices, by attaching a tag-pair and hopping the frequency channels and transmission powers. The key insight of FreeAuth lies in an implicit fin-gerprint matching scheme where the distance-frequency-power and orientation-frequency-power relationships are leveraged to circumvent these two negative factors. We implement a prototype of FreeAuth with COTS devices and the experiments demonstrate that FreeAuth is able to achieve around 0.8m and 0.6m ranges along 2D axes, in which the authentication accuracy is over 80%. The average effective authentication range of FreeAuth can outperform the state-of-the-art method by 11.67 x.</p> <h2 id="citation">Citation</h2> <p>Y.Zhu, C. Duan, and X. Ding, “Towards Location- and Orientation-Independent RFID Authentication with COTS Devices,” in Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events(PerCom Workshops), 2023, pp. 363-366.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X. Towards Location- and Orientation-Independent RFID Authentication with COTS Devices[C]//Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events(PerCom Workshops). 2023: 363-366.</p>https://duanch.github.io/publications/2023-tagfocus-towards-fine-grained-multi-object-identification-in-rfid-based-systems-with-visua/Wed, 01 Feb 2023 00:00:00 +0000https://duanch.github.io/publications/2023-tagfocus-towards-fine-grained-multi-object-identification-in-rfid-based-systems-with-visua/<h2 id="abstract">Abstract</h2> <p>Obtaining fine-grained spatial information is of practical importance in Radio Frequency Identification (RFID)-based systems for enabling multi-object identification. However, as high-precision positioning remains impractical in commercial-off-the-shelf (COTS)-RFID systems, researchers propose to combine computer vision (CV) with RFID and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in harsh conditions like small tag intervals and low reading rates. To address the limitation, we propose TagFocus to achieve fine-grained multi-object identification with visual aids in RFID systems. The key observation is that traces generated through different methods shall be compatible if they are of one identical object. Accordingly, a Transformer-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 91% in harsh conditions, outperforming state-of-the-art schemes by 27%.</p> <h2 id="citation">Citation</h2> <p>J. Yin, Z. Yang, S. Liao, C. Duan, X. Ding, and L. Zhang, “TagFocus: Towards Fine-Grained Multi-Object Identification in RFID-based Systems with Visual Aids,” ACM Transactions on Sensor Networks(TOSN), vol. 19, no. 1, pp. 1-22, 2023.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Yin J, Yang Z, Liao S, et al. TagFocus: Towards Fine-Grained Multi-Object Identification in RFID-based Systems with Visual Aids[J]. ACM Transactions on Sensor Networks(TOSN), 2023, 19(1): 1-22.</p>https://duanch.github.io/publications/2022-rosense-refining-los-signal-phase-for-robust-rfid-sensing-via-spinning-antenna/Thu, 01 Dec 2022 00:00:00 +0000https://duanch.github.io/publications/2022-rosense-refining-los-signal-phase-for-robust-rfid-sensing-via-spinning-antenna/<h2 id="abstract">Abstract</h2> <p>RFID sensing leveraging backscatter signal features (e.g., phase shift) from tags has gained increasing popularity in numerous applications but also suffers from negative impacts of environmental multipaths. Past works to address it rely on extra customized devices, labor-intensive offline training, or frequency channel hopping, all of which are non-ubiquitous or ineffective for real-life adoption. This article presents RoSense, a universal method to alleviate multipath reflections’ impacts by spinning the reader antenna, thus enabling more robust RFID sensing. Besides, RoSense requires no RF devices or offline training and operates in a nonintrusive manner. The key insight of RoSense is to exploit two properties of line-of-sight (LOS) signal when spinning the antenna, i.e., the linearity of phase changes and stability of received signal strength to attenuate the nonlinear and nonmonotonic effect of multipath signals and refine the phase shift of LOS signal. We have implemented a prototype of RoSense with COTS devices and studied two cases for evaluation: 1) material identification and 2) object localization. Experimental results show that RoSense can improve the material identification accuracy by up to 16.22% and reduce the mean localization error by up to 39.93%, greatly outperforming the state-of-the-art solutions.</p> <h2 id="citation">Citation</h2> <p>Y. Zhu, C. Duan, X. Ding, and Z. Yang, “RoSense: Refining LOS Signal Phase for Robust RFID Sensing via Spinning Antenna,” IEEE International Conference on Sensing, Communication, and Networking(IOT), vol. 9, no. 23, pp. 24135-24147, 2022.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X, et al. RoSense: Refining LOS Signal Phase for Robust RFID Sensing via Spinning Antenna[J]. IEEE International Conference on Sensing, Communication, and Networking(IOT), 2022, 9(23): 24135-24147.</p>https://duanch.github.io/publications/2022-readerprint-a-universal-method-for-rfid-readers-authentication-based-on-impedance-mismatch/Tue, 20 Sep 2022 00:00:00 +0000https://duanch.github.io/publications/2022-readerprint-a-universal-method-for-rfid-readers-authentication-based-on-impedance-mismatch/<h2 id="abstract">Abstract</h2> <p>Unauthorized access attack has always been a critical problem in RFID systems since any illegitimate reader can conduct access commands on tags without authorization and leave no trace. Past solutions for reader authentication require either modifications on EPC-global Gen2 protocol, which are inapplicable to existing infrastructures, or numerous extra customized devices as communication monitors, which incur high overhead. In this paper, we present a universal, low-cost and effective system to authenticate RFID readers, namely ReaderPrint, which only requires an extra passive tag array and is fully compatible with Gen2 protocol. The key insight behind ReaderPrint is that the impedance mismatch degrees (IMD) of different reader antennas across channels are distinguishable. We verify this mechanism through empirical studies using vector network analyzer and further propose two brand-new forms of hardware fingerprints, i.e., IMD-induced transmission power attenuation (ITPA) and phase shifts (IPS) across channels to quantify the IMD. Besides, to address the negative impacts of environmental changes, well-refined fingerprint matching algorithms are designed accordingly. We implement a prototype of ReaderPrint and evaluate it on 96 different readers in three indoor scenarios. Experimental results show that ReaderPrint can achieve fairly high authentication accuracy of up to 97.2%, regardless of environmental or device conditions.</p> <h2 id="citation">Citation</h2> <p>Y. Zhu, C. Duan, X. Ding, and Z. Yang, “ReaderPrint: A Universal Method for RFID Readers Authentication Based on Impedance Mismatch,” in Proceedings of the IEEE International Conference on Sensing, Communication, and Networking(SECON), 2022, pp. 352-360.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X, et al. ReaderPrint: A Universal Method for RFID Readers Authentication Based on Impedance Mismatch[C]//Proceedings of the IEEE International Conference on Sensing, Communication, and Networking(SECON). 2022: 352-360.</p>https://duanch.github.io/publications/2021-b-aut-a-universal-architecture-for-batch-rfid-tags-authentication/Tue, 14 Dec 2021 00:00:00 +0000https://duanch.github.io/publications/2021-b-aut-a-universal-architecture-for-batch-rfid-tags-authentication/<h2 id="abstract">Abstract</h2> <p>RFID tags authentication is always a critical but challenging problem because only checking the EPC is vulnerable to counterfeiting attacks. Past works explore the unique backscat-ter signal features induced by tags&rsquo; manufacturing imperfection as fingerprints, but fail to support simultaneous authentication for a batch of tags in practice, which is vital for large-scale RFID applications (e.g., warehouse inventory). In this paper, we present a universal architecture, namely B-AUT, to simultaneously authenticate multiple tags even with the same EPC and pinpoint them, which is fully compatible with Gen2 standard and applicable to almost all tags&rsquo; hardware fingerprints proposed in existing works. The workflow of B-AUT is threefold based on our novel algorithms. First, the extracted fuzzy fingerprint and EPC are jointly exploited to cluster raw data. Second, we extract the tags&rsquo; fine-grained fingerprints for genuineness validation and obtain the invalid clusters. Third, we harness localization methods to match the invalid cluster to dubious tags and further conduct small-scale re-validation to pinpoint the counterfeit tags. We have implemented a prototype of B-AUT and evaluated it in extreme cases. Experiment results demonstrate that B-AUT can maintain nearly the same authentication accuracy as that of separate authentication and reduce the time overhead by 43.3%. Moreover, the pinpointing accuracy can reach as high as 92.8%, regardless of tags&rsquo; total quantities or tag models.</p> <h2 id="citation">Citation</h2> <p>Y. Zhu, C. Duan, X. Ding, and Z. Yang, “B-AUT: A Universal Architecture for Batch RFID Tags Authentication,” in Proceedings of the IEEE International Conference on Parallel and Distributed Systems(ICPADS), 2021, pp. 755-762.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X, et al. B-AUT: A Universal Architecture for Batch RFID Tags Authentication[C]//Proceedings of the IEEE International Conference on Parallel and Distributed Systems(ICPADS). 2021:755-762.</p>https://duanch.github.io/publications/2021-robust-rfid-based-multi-object-identification-and-tracking-with-visual-aids/Tue, 06 Jul 2021 00:00:00 +0000https://duanch.github.io/publications/2021-robust-rfid-based-multi-object-identification-and-tracking-with-visual-aids/<h2 id="abstract">Abstract</h2> <p>Obtaining fine-grained spatial information is of practical importance in RFID-based applications. However, high-precision positioning remains a challenging task in commercial-off-the-shelf (COTS) RFID systems. Inspired by progress in the computer vision (CV) field, researchers propose to combine CV with RFID systems and turn the positioning problem into a matching problem. Promising though it seems, current methods fuse CV and RFID through converting traces of tagged objects extracted from videos by CV into phase sequences for matching, which is a dimension-reduced procedure causing loss of spatial resolution. Consequently, they fail in more harsh conditions such as small tag intervals and low reading rates of tags. To address the limitation, we propose TagFocus, a more robust RFID-enabled system for fine-grained multi-object identification and tracking with visual aids. The key observation of TagFocus is that traces generated by different methods shall be compatible if they are acquired from one identical object. Leveraging this observation, an attention-based sequence-to-sequence (seq2seq) model is trained to generate a simulated trace for each candidate tag-object pair. And the trace of the right pair shall best match the observed trace directly extracted by CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 89% in harsh conditions, outperforming state-of-the-art schemes by 25%.</p> <h2 id="citation">Citation</h2> <p>J. Yin, S. Liao, C. Duan, X. Ding, Z. Yang, and Z.Yin, “Robust RFID-Based Multi-Object Identification and Tracking with Visual Aids,” in Proceedings of the IEEE International Conference on Sensing, Communication, and Networking(SECON), 2021, pp. 1-9.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Yin J, Liao S, Duan C, et al. Robust RFID-Based Multi-Object Identification and Tracking with Visual Aids[C]//Proceedings of the IEEE International Conference on Sensing, Communication, and Networking(SECON). 2021: 1-9.</p>https://duanch.github.io/publications/2021-accurate-and-fast-detection-of-tag-antenna-damage-for-rfid-sensing-poster-abstract/Tue, 18 May 2021 00:00:00 +0000https://duanch.github.io/publications/2021-accurate-and-fast-detection-of-tag-antenna-damage-for-rfid-sensing-poster-abstract/<h2 id="abstract">Abstract</h2> <p>Tag antenna damage is a common phenomenon in real-world RFID systems, which incurs severe changes of antenna impedance and antenna gain. Existing RFID sensing systems leveraging signal features (e.g., phase readings) related to antenna impedance or gain are vulnerable to such tag antenna damage jeopardizing sensing accuracy and reliability.</p> <h2 id="citation">Citation</h2> <p>Y. Zhu, C. Duan, and X. Ding, “Accurate and Fast Detection of Tag Antenna Damage for RFID Sensing: Poster Abstract,” in Proceedings of the ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI), 2021, pp. 269-270.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Zhu Y, Duan C, Ding X. Accurate and Fast Detection of Tag Antenna Damage for RFID Sensing: Poster Abstract[C]//Proceedings of the ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI). 2021: 269-270.</p>https://duanch.github.io/publications/2021-privacy-preserving-outlier-detection-with-high-efficiency-over-distributed-datasets/Mon, 10 May 2021 00:00:00 +0000https://duanch.github.io/publications/2021-privacy-preserving-outlier-detection-with-high-efficiency-over-distributed-datasets/<h2 id="abstract">Abstract</h2> <p>The ability to detect outliers is crucial in data mining, with widespread usage in many fields, including fraud detection, malicious behavior monitoring, health diagnosis, etc. With the tremendous volume of data becoming more distributed than ever, global outlier detection for a group of distributed datasets is particularly desirable. In this work, we propose PIF (Privacy-preserving Isolation Forest), which can detect outliers for multiple distributed data providers with high efficiency and accuracy while giving certain security guarantees. To achieve the goal, PIF makes an innovative improvement to the traditional iForest algorithm, enabling it in distributed environments. With a series of carefully-designed algorithms, each participating party collaborates to build an ensemble of isolation trees efficiently without disclosing sensitive information of data. Besides, to deal with complicated real-world scenarios where different kinds of partitioned data are involved, we propose a comprehensive schema that can work for both horizontally and vertically partitioned data models. We have implemented our method and evaluated it with extensive experiments. It is demonstrated that PIF can achieve comparable AUC to existing iForest on average and maintains a linear time complexity without privacy violation.</p> <h2 id="citation">Citation</h2> <p>G. Lu, C. Duan, G. Zhou, X. Ding, and Y. Liu, “Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets,” in Proceedings of the IEEE International Conference on Computer Communications(INFOCOM), 2021, pp. 1-10.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Lu G, Duan C, Zhou G, et al. Privacy-Preserving Outlier Detection with High Efficiency over Distributed Datasets[C]//Proceedings of the IEEE International Conference on Computer Communications(INFOCOM). 2021:1-10.</p>https://duanch.github.io/publications/2021-pedestrian-trajectory-based-calibration-for-multi-radar-network/Sun, 09 May 2021 00:00:00 +0000https://duanch.github.io/publications/2021-pedestrian-trajectory-based-calibration-for-multi-radar-network/<h2 id="abstract">Abstract</h2> <p>In recent years, using radio frequency (RF) signal for pedestrian localization and tracking has aroused great interest of researchers due to its property of privacy protection. With the high spatial resolution, millimeter wave (mmWave) becomes one of the most promising RF technologies in human sensing tasks. Existing mmWave-based localization and tracking approaches can achieve decimeter-level accuracy. However, it&rsquo;s still extremely challenging to locate and track multiple pedestrians in a complex indoor environment due to target occlusion and multipath effect. To overcome these challenges, it is an opportunity to leverage multiple mmWave radars to form a multi-radar network that monitors pedestrians from different perspectives. In this poster, we address one of the fundamental challenges of building one multi-radar network: How to calibrate the perspectives of different mmWave radars before fusing their data. To reduce the overhead and improve the efficiency, we propose a multi-radar calibration method that determines the position relationship of different rad…789 tokens truncated…y CV. A prototype of TagFocus is implemented and extensively assessed in lab environments. Experimental results show that our system maintains a matching accuracy of over 89% in harsh conditions, outperforming state-of-the-art schemes by 25%.</p> <h2 id="citation">Citation</h2> <p>S. Li, J. Guo, R. Xi, C. Duan, Z. Zhai, and Y. He, “Pedestrian Trajectory based Calibration for Multi-Radar Network,” in Proceedings of the IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS), 2021, pp. 1-2.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Li S, Guo J, Xi R, et al. Pedestrian Trajectory based Calibration for Multi-Radar Network[C]//Proceedings of the IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS). 2021: 1-2.</p>https://duanch.github.io/publications/2021-full-dimension-relative-positioning-for-rfid-enabled-self-checkout-services/Mon, 01 Mar 2021 00:00:00 +0000https://duanch.github.io/publications/2021-full-dimension-relative-positioning-for-rfid-enabled-self-checkout-services/<h2 id="abstract">Abstract</h2> <p>Self-checkout services in today&rsquo;s retail stores are well received as they set free the labor force of cashiers and shorten conventional checkout lines. However, existing self-checkout options either require customers to scan items one by one, which is troublesome and inefficient, or rely on deployments of massive sensors and cameras together with complex tracking algorithms. On the other hand, RFID-based item-level tagging in retail offers an extraordinary opportunity to enhance current checkout experiences. In this work, we propose Taggo, a lightweight and efficient self-checkout schema utilizing well-deployed RFIDs. Taggo attaches a few anchor tags on the four upper edges of each shopping cart, so as to figure out which cart each item belongs to, through relative positioning among the tagged items and anchor tags without knowing their absolute positions. Specifically, a full-dimension ordering technique is devised to accurately determine the order of tags in each dimension, as well as to address the negative impacts from imperfect measurements in indoor surroundings. Besides, we design a holistic classifying solution based on probabilistic modeling to map each item to the correct cart that carries it. We have implemented Taggo with commercial RFID devices and evaluated it extensively in our lab environment. On average, Taggo achieves 90% ordering accuracy in real-time, eventually producing 95% classifying accuracy.</p> <h2 id="citation">Citation</h2> <p>C. Duan, J. Liu, X. Ding, Z. Li, and Y. Liu, “Full-Dimension Relative Positioning for RFID-Enabled Self-Checkout Services,” ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies(IMWUT), vol. 5, no. 1, pp. 1-23, 2021.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Liu J, Ding X, et al. Full-Dimension Relative Positioning for RFID-Enabled Self-Checkout Services[J]. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies(IMWUT), 2021, 5(1):1-23.</p>https://duanch.github.io/publications/2020-tagmic-listening-through-rfid-signals/Sun, 29 Nov 2020 00:00:00 +0000https://duanch.github.io/publications/2020-tagmic-listening-through-rfid-signals/<h2 id="abstract">Abstract</h2> <p>RFID is an increasingly ubiquitous technology widely adopted in both the industry and our daily life nowadays. But when it comes to eavesdropping, people usually pay attention to devices like cameras and mobile phones, instead of small-volume and battery-free RFID tags. This work shows the possibility of using prevalence RFIDs to capture and recognize the acoustic signals. To be specific, we attach an RFID tag on an object, which is located in the vicinity of the sound source. Our key innovation lies in the translation between the vibrations induced when the sound wave hits the object surface and the fluctuations in the tag&rsquo;s RF signals. Although the inherent sampling rate of commercial RFID devices is deficient, and the vibrations are very subtle, we still extract characteristic features from imperfect measurements by taking advantage of state-of-the-art machine learning and signal processing algorithms. We have implemented our system with commercial RFID and loudspeaker equipment and evaluated it intensively in our lab environment. Experimental results show that the average success rate in detecting single tone sounds can reach as high as 93.10%. We believe our work would raise the attention of RFID in the concern of surveillance and security.</p> <h2 id="citation">Citation</h2> <p>Y. Li, C. Duan, X. Ding, and C. Liu, “TagMic: Listening Through RFID Signals,” in Proceedings of the IEEE International Conference on Distributed Computing Systems(ICDCS), 2020, pp. 1187-1188.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Li Y, Duan C, Ding X, et al. TagMic: Listening Through RFID Signals[C]//Proceedings of the IEEE International Conference on Distributed Computing Systems(ICDCS). 2020:1187-1188.</p>https://duanch.github.io/publications/2020-enabling-rfid-based-tracking-for-multi-objects-with-visual-aids-a-calibration-free-solutio/Mon, 06 Jul 2020 00:00:00 +0000https://duanch.github.io/publications/2020-enabling-rfid-based-tracking-for-multi-objects-with-visual-aids-a-calibration-free-solutio/<h2 id="abstract">Abstract</h2> <p>Identification and tracking of multiple objects are essential in many applications. As a key enabler of automatic ID technology, RFID has got widespread adoption with item-level tagging in everyday life. However, restricted to the computation capability of passive RFID systems, locating or tracking tags has always been a challenging task. Meanwhile, as a fundamental problem in the field of computer vision, object tracking in images has progressed to a remarkable state especially with the rapid development of deep learning in the past few years. To enable lightweight tracking of a specific target, researchers try to complement computer vision to existing RFID architecture and achieves fine granularity. However, such solution requires calibration of the cameras extrinsic parameters at each new setup, which is not convenient for usage. In this work, we propose Tagview, a pervasive identifying and tracking system that can work in various settings without repetitive calibration efforts. It addresses the challenge by skillfully deploying the RFID antenna and video camera at the identical position and devising a multi-target recognition schema with only the image-level trajectory information. We have implemented Tagview with commercial RFID and camera devices and evaluated it extensively. Experimental results show that our method can archive high accuracy and robustness.</p> <h2 id="citation">Citation</h2> <p>C. Duan, W. Shi, F. Dang, and X. Ding, “Enabling RFID-Based Tracking for Multi-Objects with Visual Aids: A Calibration-Free Solution,” in Proceedings of the IEEE International Conference on Computer Communications(INFOCOM), 2020, pp. 1281-1290.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Shi W, Dang F, et al. Enabling RFID-Based Tracking for Multi-Objects with Visual Aids: A Calibration-Free Solution[C]//Proceedings of the IEEE International Conference on Computer Communications(INFOCOM). 2020:1281-1290.</p>https://duanch.github.io/publications/2019-robust-spinning-sensing-with-dual-rfid-tags-in-noisy-settings/Fri, 01 Nov 2019 00:00:00 +0000https://duanch.github.io/publications/2019-robust-spinning-sensing-with-dual-rfid-tags-in-noisy-settings/<h2 id="abstract">Abstract</h2> <p>Conventional spinning inspection systems, equipped with separated sensors (e.g., accelerometer, laser, etc.) and communication modules, are either very expensive and/or suffering from occlusion and narrow field of view. The recently proposed RFID-based sensing solution draws much attention due to its intriguing features, such as being cost-effective, applicable to occluded objects, auto-identification, etc. However, this solution only works in quiet settings where both the reader and spinning object remain absolutely stationary, as their shaking would ruin the periodicity and sparsity of the spinning signal, making it impossible to be recovered. To overcome such limitation, this work introduces Tagtwins, a robust spinning sensing system that can work in noisy settings. It addresses the challenge by attaching dual RFID tags on the spinning surface and developing a new formulation of spinning signal that is shaking-resilient, even if the shaking involves unknown trajectories. Our main contribution lies in two newly developed techniques. First, we propose relative spinning signal using dual tags&rsquo; readings and analytically demonstrate its feasibility in various settings. Second, we introduce dual compressive reading to inspect high-frequency spinning with relatively low reading rate of RFIDs. We have implemented Tagtwins with commercial RFID devices and evaluated it extensively. Experimental results show that Tagtwins can inspect the rotation frequency with high accuracy and robustness.</p> <h2 id="citation">Citation</h2> <p>C. Duan, L. Yang, Q. Lin, Y. Liu, and L. Xie, “Robust Spinning Sensing with Dual-RFID-Tags in Noisy Settings,” IEEE Transactions on Mobile Computing(TMC), vol. 18, no. 11, pp. 2647-2659, 2019.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Yang L, Lin Q, et al. Robust Spinning Sensing with Dual-RFID-Tags in Noisy Settings[J]. IEEE Transactions on Mobile Computing(TMC), 2019, 18(11): 2647-2659.</p>https://duanch.github.io/publications/2019-tash-toward-selective-reading-as-hash-primitives-for-gen2-rfids/Mon, 01 Apr 2019 00:00:00 +0000https://duanch.github.io/publications/2019-tash-toward-selective-reading-as-hash-primitives-for-gen2-rfids/<h2 id="abstract">Abstract</h2> <p>Deployment of billions of commercial off-the-shelf (COTS) radio frequency identification (RFID) tags has drawn much of the attention of the research community because of the performance gaps of current systems. In particular, hash-enabled protocol (HEP) is one of the most thoroughly studied topics in the past decade. HEPs are designed for a wide spectrum of notable applications (e.g., missing detection) without need to collect all tags. HEPs assume that each tag contains a hash function, such that a tag can select a random but predictable time slot to reply with a one-bit presence signal that shows its existence. However, the hash function has never been implemented in COTS tags in reality, which makes HEPs a ten-year untouchable mirage. This paper designs and implements a group of analog on-tag hash primitives (called Tash) for COTS Gen2-compatible RFID systems, which moves prior HEPs forward from theory to practice. In particular, we design three types of hash primitives, namely, tash function, tash table function, and tash operator. All of these hash primitives are implemented through the selective reading, which is a fundamental and mandatory functionality specified in Gen2 protocol, without any hardware modification and fabrication-a feature allowing zero-cost fast deployment on billions of Gen2 tags. We further apply our hash primitives in one typical HEP application (i.e., missing detection) to show the feasibility and effectiveness of Tash. Results from our prototype, which is composed of one ImpinJ reader and 3000 Alien tags, demonstrate that the new design lowers 70% of the communication overhead in the air. The tash operator can additionally introduce an overhead drop of 29.7%.</p> <h2 id="citation">Citation</h2> <p>Q. Lin, L. Yang, C. Duan, and Z. An, “Tash: Toward Selective Reading as Hash Primitives for Gen2 RFIDs,” IEEE Transactions on Networking(TON), vol. 27, no. 2, pp. 819-834, 2019.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Lin Q, Yang L, Duan C, et al. Tash: Toward Selective Reading as Hash Primitives for Gen2 RFIDs[J]. IEEE Transactions on Networking(TON), 2019, 27(2): 819-834.</p>https://duanch.github.io/publications/2018-tagspin-high-accuracy-spatial-calibration-of-rfid-antennas-via-spinning-tags/Mon, 01 Oct 2018 00:00:00 +0000https://duanch.github.io/publications/2018-tagspin-high-accuracy-spatial-calibration-of-rfid-antennas-via-spinning-tags/<h2 id="abstract">Abstract</h2> <p>Recent years have witnessed the advance of RFID-based localization techniques that demonstrate high precision. Many efforts have been made locating RFID tags accurately with a mandatory assumption that the RFID reader&rsquo;s position is known in advance. Unfortunately, calibrating reader&rsquo;s location manually is always time-consuming and laborious in practice. In this paper, we present Tagspin, an approach using COTS tags to pinpoint the reader (antenna) quickly and easily with high accuracy. Tagspin enables each tag to emulate a circular antenna array by uniformly spinning on the edge of a rotating disk. We design an SAR-based method for estimating the angle spectrum of the target reader. Compared to previous AoA-based techniques, we employ an enhanced power profile modeling the relative signal power received from the reader along different spatial directions, which is more accurate and immune to ambient noise as well as measurement errors caused by hardware characteristics. Besides, we find that tag&rsquo;s phase measurements in practice are related to its orientation. To the best of our knowledge, we are the first to point out this fact and quantify the relationship between them. By calibrating the phase shifts caused by orientation, the positioning accuracy can be improved by 3:7×. We have implemented Tagspin with COTS RFID devices and evaluated it extensively. Experimental results show that Tagspin achieves mean accuracy of 7:3 cm with standard deviation of 1:8 cm in 3D space.</p> <h2 id="citation">Citation</h2> <p>C. Duan, L. Yang, Q. Lin, and Y. Liu, “Tagspin: High Accuracy Spatial Calibration of RFID Antennas via Spinning Tags,” IEEE Transactions on Mobile Computing(TMC), vol. 17, no. 10, pp. 2438-2451, 2018.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Yang L, Lin Q, et al. Tagspin: High Accuracy Spatial Calibration of RFID Antennas via Spinning Tags[J]. IEEE Transactions on Mobile Computing(TMC), 2018, 17(10): 2438-2451.</p>https://duanch.github.io/publications/2017-revisiting-reading-rate-with-mobility-rate-adaptive-reading-of-cots-rfid-systems/Tue, 12 Dec 2017 00:00:00 +0000https://duanch.github.io/publications/2017-revisiting-reading-rate-with-mobility-rate-adaptive-reading-of-cots-rfid-systems/<h2 id="abstract">Abstract</h2> <p>Radio-frequency identification (RFID) systems, as major enablers of automatic identification, are currently supplemented with various interesting sensing functions, e.g., motion tracking. All these sensing applications forcedly require much higher reading rate (i.e., sampling rate) such that any fast movement of tagged objects can be accurately captured in a timely manner through tag readings. However, COTS RFID systems suffer from an extremely low individual reading rate when multiple tags are present, due to their intense channel contention in the link layer. In this work, we present a holistic system, called Tagwatch, a rate-adaptive reading system for COTS RFID devices. This work revisits the reading rate from a distinctive perspective: mobility. We observe that the reading demands of mobile tags are considerably more urgent than those of stationary tags because the states of the latter nearly remain unchanged; meanwhile, only a few tags (e.g., &lt; 20%) are actually in motion despite the existence of a massive amount of tags in practice. Thus, Tagwatch adaptively improves the reading rates for mobile tags by cutting down the readings of stationary tags. Our main contribution is a two-phase reading design, wherein the mobile tags are discriminated in the Phase I and exclusively read in the Phase II. We built a prototype of Tagwatch with COTS RFID readers and tags. Results from our microbenchmark analysis demonstrate that the new design outperforms the reading rate by 3.2x when 5% of tags are moving.</p> <h2 id="citation">Citation</h2> <p>Q. Lin, L. Yang, H. Jia, C. Duan, and Y. Liu, “Revisiting Reading Rate with Mobility: Rate-Adaptive Reading in COTS RFID Systems,” in Proceedings of the ACM International Conference on emerging Networking EXperiments and Technologies(CoNEXT), 2017, pp. 199-211.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Lin Q, Yang L, Jia H, et al. Revisiting Reading Rate with Mobility: Rate-Adaptive Reading in COTS RFID Systems[C]//Proceedings of the ACM International Conference on emerging Networking EXperiments and Technologies(CoNEXT). 2017: 199-211.</p>https://duanch.github.io/publications/2017-analog-on-tag-hashing-towards-selective-reading-as-hash-primitives-in-gen2-rfid-systems/Mon, 16 Oct 2017 00:00:00 +0000https://duanch.github.io/publications/2017-analog-on-tag-hashing-towards-selective-reading-as-hash-primitives-in-gen2-rfid-systems/<h2 id="abstract">Abstract</h2> <p>Deployment of billions of Commercial Off-The-Shelf (COTS) RFID tags has drawn much of the attention of the research community because of the performance gaps of current systems. In particular, hash-enabled protocol (HEP) is one of the most thoroughly studied topics in the past decade. HEPs are designed for a wide spectrum of notable applications (e.g., missing detection) without need to collect all tags. HEPs assume that each tag contains a hash function, such that a tag can select a random but predicable time slot to reply with a one-bit presence signal that shows its existence. However, the hash function has never been implemented in COTS tags in reality, which makes HEPs a 10-year untouchable mirage. This work designs and implements a group of analog on-tag hash primitives (called Tash) for COTS Gen2-compatible RFID systems, which moves prior HEPs forward from theory to practice. In particular, we design three types of hash primitives, namely, tash function, tash table function and tash operator. All of these hash primitives are implemented through selective reading, which is a fundamental and mandatory functionality specified in Gen2 protocol, without any hardware modification and fabrication. We further apply our hash primitives in two typical HEP applications (i.e., cardinality estimation and missing detection) to show the feasibility and effectiveness of Tash. Results from our prototype, which is composed of one ImpinJ reader and 3,000 Alien tags, demonstrate that the new design lowers 60% of the communication overhead in the air. The tash operator can additionally introduce an overhead drop of 29.7%.</p> <h2 id="citation">Citation</h2> <p>L.Yang, Q. Lin, C. Duan, and Z. An, “Analog On-Tag Hashing: Towards Selective Reading as Hash Primitives in Gen2 RFID Systems,” in Proceedings of the ACM International Conference on Mobile Computing and Networking(MobiCom), 2017, pp. 301-314.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Yang L, Lin Q, Duan C, et al. Analog On-Tag Hashing: Towards Selective Reading as Hash Primitives in Gen2 RFID Systems[C]//Proceedings of the ACM International Conference on Mobile Computing and Networking(MobiCom). 2017: 301-314.</p>https://duanch.github.io/publications/2017-fusing-rfid-and-computer-vision-for-fine-grained-object-tracking/Mon, 01 May 2017 00:00:00 +0000https://duanch.github.io/publications/2017-fusing-rfid-and-computer-vision-for-fine-grained-object-tracking/<h2 id="abstract">Abstract</h2> <p>In recent years, both the RFID and computer vision technologies have been widely employed in indoor scenarios aimed at different goals while faced with respective limitations. For example, the RFID-based EAS system is useful in quickly identifying tagged objects but the accompanying false alarm problem is troublesome and hard to tackle with except that the accurate trajectory of the target tag can be easily acquired. On the other side, the CV system performs fairly well in tracking multiple moving objects precisely while finding it difficult to screen out the specific target among them. To overcome the above limitations, we present TagVision, a hybrid RFID and computer vision system for fine-grained localization and tracking of tagged objects. A fusion algorithm is proposed to organically combine the position information given by the CV subsystem, and phase data output by the RFID subsystem. In addition, we employ the probabilistic model to eliminate the measurement error caused by thermal noise and device diversity. We have implemented TagVision with COTS camera and RFID devices and evaluated it extensively in our lab environment. Experimental results show that TagVision can achieve 98% blob matching accuracy and 10.33mm location tracking precision.</p> <h2 id="citation">Citation</h2> <p>C. Duan, X. Yang, L. Yang, and Y. Liu, “Fusing RFID and Computer Vision for Fine-Grained Object Tracking,” in Proceedings of the IEEE International Conference on Computer Communications(INFOCOM), 2017, pp. 1-9.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Rao X, Yang L, et al. Fusing RFID and Computer Vision for Fine-Grained Object Tracking[C]//Proceedings of the IEEE International Conference on Computer Communications(INFOCOM). 2017: 1-9.</p>https://duanch.github.io/publications/2016-accurate-spatial-calibration-of-rfid-antennas-via-spinning-tags/Mon, 27 Jun 2016 00:00:00 +0000https://duanch.github.io/publications/2016-accurate-spatial-calibration-of-rfid-antennas-via-spinning-tags/<h2 id="abstract">Abstract</h2> <p>Recent years have witnessed the advance of RFID-based localization techniques that demonstrate high precision. Many efforts have been made locating RFID tags with a mandatory assumption that the RFID reader&rsquo;s position is known in advance. Unfortunately, calibrating reader&rsquo;s location manually is always time-consuming and laborious in practice. In this paper, we present Tagspin, an approach using COTS tags to pinpoint the reader (antenna) quickly and easily with high accuracy. Tagspin enables each tag to emulate a circular antenna array by uniformly spinning on the edge of a rotating disk. We design an SAR-based method for estimating the angle spectrum of the target reader. Compared to previous AoA-based techniques, we employ an enhanced power profile modeling the signal power received from the reader along different spatial directions, which is more accurate and immune to ambient noise as well as measurement errors caused by hardware characteristics. Besides, we find that tag&rsquo;s phase measurements in practice are related to its orientation. To the best of our knowledge, we are the first to point out this fact and quantify the relationship between them. By calibrating the phase shifts caused by orientation, the positioning accuracy can be improved by 3.7×. We have implemented Tagspin withCOTS RFID devices and evaluated it extensively. Experimentalresults show that Tagspin achieves mean accuracy of 7.3cm with standard deviation of 1.8cm in 3D space.</p> <h2 id="citation">Citation</h2> <p>C. Duan, L. Yang, and Y. Liu, “Accurate Spatial Calibration of RFID Antennas via Spinning Tags,” in Proceedings of the IEEE International Conference on Distributed Computing Systems(ICDCS), 2016, pp. 519-528.</p> <h2 id="中文引用gbt-7714">中文引用（GB/T 7714）</h2> <p>Duan C, Yang L, Liu Y. Accurate Spatial Calibration of RFID Antennas via Spinning Tags[C]//Proceedings of the IEEE International Conference on Distributed Computing Systems(ICDCS). 2016: 519-528.</p>