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.

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.

With the proliferation of RFID-enabled applications, large-scale RFID systems containing numerous tags are becoming increasingly common. Efficient management of these systems requires the ability to quickly collect information from specific subsets of tags, known as target tags. However, existing works often rely on hardware modifications, limiting their applicability to commercial RFID systems. To address this, this paper proposes the Target Tag Information Collection (TTIC) protocol and its enhanced version E-TTIC, both designed for efficient target tag collection using off-the-shelf RFID devices. TTIC leverages the Cuckoo filter to eliminate non-target tags and accurately collect target tags. It first inserts all target tags into the Cuckoo filter and then designs select commands compatible with commercial RFID readers to identify target tags while silencing non-target tags. On top of TTIC, E-TTIC further reduces the collection time by designing a novel Cuckoo filter that requires fewer select commands. We implement our protocols in a commodity RFID system. Extensive experiments show that E-TTIC can improve time efficiency by up to 80 % compared to the baseline.

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.

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.

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.

Airlines commonly need to take into consideration maximizing their profit while designing the hub-and-spoke network to obtain more market share and promote healthy development of aviation industry. Hence, in this article, we study the problem of multiple-allocation HUb and spoke network design for ROuting flight flows to maximize airline profit utility (HURO). That is, given a set of airport nodes, a set of flight flows with known origin positions and destination positions, finding a limited number of hub edges to transfer flows and determining routing allocation mode considering customer preference such that the overall transportation profit utility is maximized. To address HURO problem, we first consider a relaxed version of HURO (HURO-R for short). We prove that HURO-R falls into the realm of maximizing a submodular set function subject to a cardinality constraint, and propose an algorithm with a constant approximation ratio. Next, we design a two-level algorithm framework with a constant approximation ratio to address HURO. Besides, we consider variants of HURO, HURO-C and HURO-RU, and design approximation algorithms to address them. We conduct simulation experiments on standard dataset and field experiments to verify our theoretical findings. The results shows that our proposed algorithm can outperform other comparison algorithms by 75.28 percent.

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.

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.

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.

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.

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).

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.

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%.

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 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.