Missing Tags Research Articles

Finding Needles in a Haystack: Missing Tag Detection in Large RFID Systems

Radio frequency identification technology has been widely used in missing tag detection to reduce and avoid inventory shrinkage. In this application, promptly finding out the missing event is of paramount importance. However, the existing missing tag detection protocols cannot efficiently handle the presence of a large number of unexpected tags whose IDs are not known to the reader, which shackles the time efficiency. To deal with the problem of detecting missing tags in the presence of unexpected tags, this paper introduces a two-phase Bloom filter-based missing tag detection (BMTD) protocol. The proposed BMTD exploits Bloom filter in sequence to first deactivate the unexpected tags and then test the membership of the expected tags, thus dampening the interference from the unexpected tags and considerably reducing the detection time. Moreover, the theoretical analysis of the protocol parameters is performed to minimize the detection time of the proposed BMTD and achieve the required reliability simultaneously. In addition, we derive a critical threshold on the unexpected tag size for the execution of first phase in BMTD. Extensive experiments are then conducted to evaluate the performance of the proposed BMTD. The results demonstrate that the proposed BMTD significantly outperforms the state-of-the-art solutions.

On Missing Tag Detection in Multiple-Group Multiple-Region RFID Systems

We formulate and study a missing tag detection problem arising in multiple-group, multiple-region radio frequency identification (RFID) systems, where a mobile reader needs to detect whether there is any missing event for each group of tags. The problem we tackle is to devise missing tag detection protocols with minimum execution time while guaranteeing the detection reliability requirement for each group. By leveraging the technique of Bloom filter, we develop a suite of three missing tag detection protocols, each decreasing the execution time compared to its predecessor by incorporating an improved version of the Bloom filter design and parameter tuning. By sequentially analyzing the developed protocols, we gradually iron out an optimum detection protocol that works in practice.

Fast temporal continuous scanning in RFID systems

This paper studies the problem of temporal continuous scanning for large-scale RFID systems, which is an essential operation to keep the inventory up-to-date. The existing solutions need to execute unknown and missing tag identification protocols separately, which are of low time-efficiency because the unknown tags disturb the identification of missing tags and vice versa. To this end, we design a Fast Continuous Scanning (FCS) protocol based on the proposed multiple categories filter which can detect unknown tags and skip the empty and collision slots for improving the efficiency of missing tag identification. FCS is faster than the prior methods because the proposed filter is helpful to decreasing the interference between unknown and missing tags. We also investigate the optimization of the involved parameters to minimize the execution time. Extensive simulation results demonstrate that the proposed protocol outperforms the state-of-the-art solutions by saving 39% ∼ 58.5% of the execution time.

Hybrid textual-visual relevance learning for content-based image retrieval

Learning effective relevance measures plays a crucial role in improving the performance of content-based image retrieval (CBIR) systems. Despite extensive research efforts for decades, how to discover and incorporate semantic information of images still poses a formidable challenge to real-world CBIR systems. In this paper, we propose a novel hybrid textual-visual relevance learning method, which mines textual relevance from image tags and combines textual relevance and visual relevance for CBIR. To alleviate the sparsity and unreliability of tags, we first perform tag completion to fill the missing tags as well as correct noisy tags of images. Then, we capture users’ semantic cognition to images by representing each image as a probability distribution over the permutations of tags. Finally, instead of early fusion, a ranking aggregation strategy is adopted to sew up textual relevance and visual relevance seamlessly. Extensive experiments on two benchmark datasets well verified the promise of our approach.

Weakly-Supervised Deep Nonnegative Low-Rank Model for Social Image Tag Refinement and Assignment

It has been well known that the user-provided tags of social images are imperfect, i.e., there exist noisy, irrelevant or incomplete tags. It heavily degrades the performance of many multimedia tasks. To alleviate this problem, we propose a Weakly-supervised Deep Nonnegative Low-rank model (WDNL) to improve the quality of tags by integrating the low-rank model with deep feature learning. A nonnegative low-rank model is introduced to uncover the intrinsic relationships between images and tags by simultaneously removing noisy or irrelevant tags and complementing missing tags. The deep architecture is leveraged to seamlessly connect the visual content and the semantic tag. That is, the proposed model can well handle the scalability by assigning tags to new images. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method compared with some state-of-the-art methods.

Fast and Reliable Detection and Identification of Missing RFID Tags in the Wild

Radio-frequency identification (RFID) systems have been deployed to detect and identify missing products by affixing them with cheap passive RFID tags and monitoring them with RFID readers. Existing missing tag detection and identification protocols require the tag population to contain only those tags whose IDs are already known to the reader. However, in reality, tag populations often contain tags with unknown IDs, called unexpected tags. These unexpected tags cause unexpected false positives, i.e. , due to them, missing tags are detected as present. We take the first step toward addressing the problem of detecting and identifying missing tags from a population that contains unexpected tags. Our protocol, RUN, uses standardized frame slotted Aloha for communication between tags and readers. It executes multiple frames with different seeds to reduce the effects of unexpected false positives. At the same time, it minimizes the missing tag detection and identification time by first estimating the number of unexpected tags in the population and then using it along with the false-positive probability to obtain optimal frame sizes and minimum number of times Aloha frames should be executed to mitigate the effects of false positives. RUN works with multiple readers with overlapping regions. It is easy to deploy, because it is implemented on readers as a software module and does not require any modifications to tags or to the communication protocol between the tags and the readers. We implemented RUN along with four major missing tag detection and identification protocols, namely, TRP, IIP, MTI, and SFMTI, and the fastest tag ID collection protocol TH and compared them side by side. Our performance evaluation results show that RUN is the only protocol that achieves required reliability in the presence of unexpected tags, whereas the best existing protocol achieves a maximum reliability of only 67%. RUN identifies 100% of missing tags in the presence of unexpected tags, whereas the best existing protocol identifies a maximum of only 60% of missing tags.

Efficient detection of missing tags for passive RFID systems

SummaryOne of the most interesting radio‐frequency identification applications is the detection of missing tags along with the objects they are attached to. This process must be executed in a fast and efficient way. A monitoring function may need to be periodically executed in order to implement a reliable detection system, therefore raising the need to optimize its performance, in terms of both time and reliability. The contribution of this paper is twofold; on one hand, we provide a protocol taxonomy for the missing‐tag detection problem, which up to our best knowledge is the first of its class, and on the other hand, we propose two novel protocols for a fast detection of the missing tags in a passive radio‐frequency identification system. By increasing the length of the packets exchanged among the reader and the tags, our protocols are able to reduce the time needed to detect the missing tags, by decreasing the number of broadcast messages in the network. Our results show that our proposals outperform the most recent protocols in the literature, obtaining improvements on the execution time going from 25% to 75%. Copyright © 2016 John Wiley & Sons, Ltd.

Design and Analysis of Unequal Missing Protection for the Grouping of RFID Tags

In this paper, we address the issue of unequal missing protection (UMP) for the design of grouping of radio-frequency identification (RFID) tags. While relying on group generation matrices, grouping of RFID tags allows verifying the integrity of a collection of tags without the requirement for accessing external systems, and can be extended to identify missing tags. Motivated by application needs that call for UMP among the tags, we first introduce the concepts of UMP for grouping of RFID tags and its extended counterpart, with which missing tags with high missing protection levels are more easily counted or identified than those with low missing protection levels. We then present simple yet effective schemes to realize grouping of RFID tags with UMP and its extended counterpart. The proposed schemes not only can easily fulfill the requirement of UMP among the tags, but also offer flexibility in constructing UMP group generation matrices. We also characterize key objects in order to further study grouping of RFID tags with UMP and its extended counterpart, called consistent and unidentifiable sets, respectively. This characterization in turn enables theoretical analysis of the error rate from the perspective of a tag. Theoretical and simulation results are presented to demonstrate the efficiencies of the proposed schemes for the design of grouping of RFID tags with UMP.

P-MTI: Physical-Layer Missing Tag Identification via Compressive Sensing

Radio frequency identification (RFID) systems are emerging platforms that support a variety of pervasive applications. RFID tags can be used to label items and enable item-level monitoring. The problem of identifying missing tags in RFID systems has attracted wide attention due to its practical importance (e.g., anti-theft). This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme that effectively makes use of the lower-layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated tag responses instead of focusing on individual tag responses and extracts useful information from physical-layer collisions. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We prototype P-MTI using the USRP software defined radio and Intel WISP platform. We also evaluate the performance of P-MTI with extensive simulations and compare to previous approaches. The experiment results show the promising performance of P-MTI in identification accuracy, time efficiency, as well as robustness over noisy channels.

Projective nonnegative matrix factorization for social image retrieval

Increasingly many social images with tags are available on photo sharing websites. Due to the subjectivity and diversity of social tagging behaviors, noisy and missing tags for images are inevitable. To tackle this problem, this paper proposes a novel factor analysis model, named ProjecTive Nonnegative Matrix Factorization (PTNMF) with ℓ2,1-norm regularization, which introduces linear transformation and ℓ2,1-norm minimization into a joint framework of NMF. For tagging data, a new interpretation is adopted to distinguish the relevant tags and irrelevant tags instead of the typically used binary scheme. In our model, the image latent representation is assumed to be projected from its original feature representation with an orthogonal transformation matrix. The projection makes convenient to embed any images including out-of-samples into the latent space. That is, the proposed method enables to handle the out-of-sample problem. The ℓ2,1-norm regularization makes the transformation matrix suitable for selecting the effective features. Local geometry preservations of image space (tag space) are explored as constraints in order to make image similarity (tag correlation) consistent in the original space and the corresponding latent space. We investigate the performance of the proposed method on image retrieval and compare it to existing work on the challenging NUS-WIDE dataset. Extensive experiments indicate the effectiveness and potentials of the proposed method in real-world applications.

Missing-Tag Detection and Energy–Time Tradeoff in Large-Scale RFID Systems With Unreliable Channels

Radio frequency identification (RFID) technologies are poised to revolutionize retail, warehouse, and supply chain management. One of their interesting applications is to automatically detect missing tags in a large storage space, which may have to be performed frequently to catch any missing event such as theft in time. Because RFID systems typically work under low-rate channels, past research has focused on reducing execution time of a detection protocol to prevent excessively long protocol execution from interfering normal inventory operations. However, when active tags are used for a large spatial coverage, energy efficiency becomes critical in prolonging the lifetime of these battery-powered tags. Furthermore, much of the existing literature assumes that the channel between a reader and tags is reliable, which is not always true in reality because of noise/interference in the environment. Given these concerns, this paper makes three contributions. First, we propose a novel protocol design that considers both energy efficiency and time efficiency. It achieves multifold reduction in both energy cost and execution time when compared to the best existing work. Second, we reveal a fundamental energy-time tradeoff in missing-tag detection, which can be flexibly controlled through a couple of system parameters in order to achieve desirable performance. Third, we extend our protocol design to consider channel error under two different models. We find that energy/time cost will be higher in unreliable channel conditions, but the energy-time tradeoff relation persists.

A regularized optimization framework for tag completion and image retrieval

Abstract With the fast expansion of social image sharing websites, the tag-based image retrieval (TBIR) becomes important and prevalent for Internet users to search the social images. However, some user-provided tags of social images are too incomplete and ambiguous to facilitate the social image retrieval. In this paper, we propose a regularized optimization framework to complete the missing tags for social images ( tag completion ). Within the regularized optimization framework, the non-negative matrix factorization (NMF) and the holistic visual diversity minimization are used jointly to make the tag-image matrix completed as the relationships of images and tags are represented to a tag-image matrix. The non-negative matrix factorization casts the tag-image matrix into a latent low-rank space and utilizes the semantic relevance of tags to partially complete the insufficient tags. To take the visual content of images into account, the other objective term representing the holistic visual diversity is appended with the NMF to leverage the content-similar images. Moreover, to ensure the proper corrections and sparseness of tag-image matrix, two regularized factors are also included into the optimization framework. Through conducting the experiments on the benchmark image set with the adequate ground truth, we verify the effectiveness of our proposed approach.

Identifying the Missing Tags in Categorized RFID Systems

RFID is a practical technology that supports extensive applications such as warehouse management, asset tracking, and transportation and logistics. Identifying the missing tags in categorized RFID systems is of practical importance for a variety of applications but is not yet thoroughly investigated. Traditional missing tag identification approaches can not solve the problem efficiently because they do not distinguish categories, leading to long scanning time and high energy consumption. To achieve time and energy efficiency, this paper proposes three protocols. First, we formally formulate the problem of missing tag identification in categorized RFID systems. And then a compact structure is proposed to active tags in protocols which carries a large volume aggregated tag ID information. To further improve the utilization rate of a frame, we design a rehash scheme to change empty slots and collision slots into singleton slots. We evaluate the performance of our protocols with extensive simulations. Compared with the physical-layer missing tag identification protocol (P-MTI), which is the state-of-the-art missing tag identification technology, our best protocol reduces the execution time up to 99.8%.

Multi-label multi-instance learning with missing object tags

In this paper, a novel framework is developed for leveraging large-scale loosely tagged images for object classifier training by addressing three key issues jointly: (a) spam tags e.g., some tags are more related to popular query terms rather than the image semantics; (b) loose object tags, e.g., multiple object tags are loosely given at the image level without identifying the object locations in the images; (c) missing object tags, e.g., some object tags are missed and thus negative bags may contain positive instances. To address these three issues jointly, our framework consists of the following key components for leveraging large-scale loosely tagged images for object classifier training: (1) distributed image clustering and inter-cluster visual correlation analysis for handling the issue of spam tags by filtering out large amounts of junk images automatically, (2) multiple instance learning with missing tag prediction for dealing with the issues of loose object tags and missing object tags jointly; (3) structural learning for leveraging the inter-object visual correlations to train large numbers of inter-related object classifiers jointly. Our experiments on large-scale loosely tagged images have provided very positive results.

Theory and Performance Evaluation of Group Coding of RFID Tags

Radio frequency identification (RFID) is an automatic identification technology which identifies physical objects individually according to their unique identifiers (ID) recorded in each RFID tag. Many business processes require the integrity verification of a group of objects in addition to individual object identification. This paper proposes “group coding” of RFID tags with which we can verify the integrity of groups of objects by writing parity check data to the memory of RFID tags. It was revealed by simulations and experiments that we could determine the number of missing RFID tags up to 10 with accuracy over 99% when we write 96 bits of the checksum data to 20 RFID tags. The whole duration of group decoding measured in the experiment was approximately 2 to 3 s. The time to compute group encoding and decoding was in the order of several milliseconds and thus negligible. The RFID inventory accounts for the majority of the duration. Note to Practitioners-Current RFID features fast identification of many physical objects. However, the integrity check of a group of objects is usually done by looking up a packaging list or a shipment list in EDI, which requires a network connection. Our proposed “group coding” of RFID tags can perform the group integrity check without a network connection. In addition, when the integrity of the group is infringed, the group coding can determine the number of RFID tags missing from the group. These features of group coding can reduce the cost of looking up shipment lists and locate missing RFID tags. The accuracy of the determination is controlled by adjusting the size of data written in each RFID tag. Adopters of group coding can select the optimal performance of group coding from the requirements of the accuracy and constraints like memory consumption of RFID tags.

Efficient protocol design for dynamic tag population monitoring in large‐scale radio frequency identification systems

SUMMARYAs radio frequency identification (RFID) tags become more ubiquitously available, they will stay in dynamic environments where tags can freely enter or leave RFID readers' interrogation range. With such a dynamic tag population, there arises a problem of population monitoring, whose purpose is to identify the missing tags that have departed from the reading range and the new tags that have newly entered. This problem is a new problem which cannot be well solved by the conventional tag identification protocols. In this paper, we first show that this traditional approach is inefficient, because it collects all the tag IDs in each scan and ignores the ready‐for‐use knowledge of the tag population in a previous scan. To be more efficient, we present three protocols: (i) a baseline protocol that improves the traditional tag identification protocol by optimizing its length of random number used for collision detection; (ii) a novel one‐phase protocol with easy labor to identify exactly the new tags and the missing tags by fully utilizing the knowledge of previous tag population; and (iii) a hybrid protocol that smartly combines the baseline protocol and the one‐phase protocol. Its purpose is to deal with the situation that the knowledge of previous tag population is highly inconsistent with the current tag population. This hybrid protocol, as shown by our analysis, can improve the tag monitoring accuracy by 25%, and improve the time efficiency by 55.3%, as compared with a recent work (called two‐phase protocol), which also identifies the population changes. Copyright © 2012 John Wiley & Sons, Ltd.