Efficiently and Completely Identifying Missing Key Tags for Anonymous RFID Systems

Chen, Honglong; Wang, Zhibo; Xia, Feng; Li, Yanjun; Shi, Leyi

doi:10.1109/jiot.2017.2752239

Cited by 39 publications

(15 citation statements)

References 31 publications

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“…Fast identifying the missing tags is critical to avoid substantial economic losses. The solutions to missing tag identification can be classified into two categories: ID collection protocol [9], [10], [14], [18]- [20] and missing tag identification protocol [3], [11]- [13], [15], [16], [21].…”

Section: Related Workmentioning

confidence: 99%

“…R ADIO frequency identification (RFID), as a compelling technology in the Internet of Things, is widely used in various applications, such as supply chain management [1], [2], inventory control [3], [4], and localization [5], [6]. In these applications, RFID can realize automated inventory management with its significant advantages of fast identification, high reliability, and long distance.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Methodology to Identify Missing Tags in Large-Scale RFID Systems

Chu¹,

Xu²,

Li³

et al. 2022

Preprint

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Radio frequency identification (RFID) has been widely has broad applications. One such application is to use RFID to track inventory in warehouses and retail stores. In this application, timely identifying the missing items is an ongoing engineering problem. A feasible solution to this problem is to map each tag to a time slot and verify the presence of a tag by comparing the status of the predicted time slot and the actual time slot. However, existing works are time inefficient because they only verify tags one by one in singleton slots but ignore the collision slots mapped by multiple tags. To accelerate the identification process, we use bit tracking to verify tags in collision slots and design two protocols accordingly. We first propose the Sequential String based Missing Tag Identification (SSMTI) protocol, which converts all time slots to collision slots and enables tags in each slot to reply to a designed string simultaneously. By using bit tracking to decode the combined string, the reader can verify multiple tags together. To improve the performance of SSMTI when most tags are missing, we further propose the Interactive String based Missing Tag Identification (ISMTI) protocol. ISMTI improves the strategies of designing strings for each collided tag so that the reader can verify more tags using shorter strings than SSMTI. Besides, ISMTI can dynamically adjust the verification mechanism according to the proportion of missing tags to maintain time efficiency. We also provide theoretical analysis for proposed protocols to minimize execution time and evaluate their performance through extensive simulations. Compared with state-of-the-art solutions, the proposed SSMTI and ISMTI can reduce the time cost by as much as 39.74% and 68.87%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Methodology to Identify Missing Tags in Large-Scale RFID Systems

Chu¹,

Xu²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…As a proactive defense mechanism, a honeypot is becoming an indispensable tool for providing network security in wide applications such as Internet of Things (IoT) [1]- [4], wireless sensor networks (WSNs) [5], [6] and vehicular networks [7], etc. With proactivity and inveiglement, a honeypot can attract an attacker to interact with the fake system resources, which prevents valuable resources from being attacked.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Distributed Honeypot Based on Blockchain

Shi

Liu

et al. 2019

IEEE Access

Self Cite

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Honeypot technology can be applied to efficiently attract attackers and exhaust their resources. However, the traditional static honeypot is easy to be recognized by anti-honeypot technology. By contrast, most of the dynamic honeypots can simulate the real system in time, thus interacting with an intruder in disguise. In this paper, we employ the dynamic property of honeypot in four kinds of services of our system. However, this dynamic property shows up in a location and identification, indicating that genuine or fake services (honeypots) are changeable in different hosts. Thus, the dynamic property of our system differs from the dynamic honeypot aforementioned. Besides, we adopt the blockchain platform (Ethereum) to decentralize our system and store the port access data by delivering a private chain. To illustrate the effectiveness of our scheme in theory and practice, security analysis, eavesdropping attack, scanning attack, and DoS attack experiments are conducted. The results show that our scheme is valid in safeguarding against network attack. INDEX TERMS Honeypot, blockchain, security analysis, network security.

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“…With the wide applications of social networks [18], [25]- [27] and Internet of things (IoTs) [4], [5], [10], [23], [24], [29], the recommendation system is popular in people's daily lives recently, such as online shopping, article reading, and films, etc. For instance, the recommendation system can greatly help the customers by suggesting them the products or services with potential interest, based on their preferences, needs, and purchase histories.…”

Section: Introductionmentioning

confidence: 99%

Deformable Convolutional Matrix Factorization for Document Context-Aware Recommendation in Social Networks

Chen¹,

Fu²,

Zhang³

et al. 2019

IEEE Access

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The extreme sparsity of the rating data seriously affects the recommendation quality of the recommendation system. In order to alleviate the problem of data sparsity, some convolutional neural network (CNN)-based models make full use of text data to improve the recommendation accuracy. However, due to the inherent properties of the traditional convolutional network, it can only extract features in a fixed position, and rely on the primitive bounding box based feature extraction, thus ignoring the flexibility of the traditional convolution. In this paper, we adopt a flexible convolutional network called deformable convolutional network (DCN), which extends the convolution transformation model capability by adding an offset layer to the traditional convolution layer, and then propose a novel deformable convolutional network matrix factorization (DCNMF) recommendation model. Specifically, we combine the DCN with word embedding to deeply capture the contextual information of document and build a latent model, which is incorporated into the probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct extensive experiments on the real-world datasets, and the experimental results show that the DCNMF outperforms the compared benchmarks. INDEX TERMS Collaborative filtering, contextual information, neural networks, recommendation system.

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Efficiently and Completely Identifying Missing Key Tags for Anonymous RFID Systems

Cited by 39 publications

References 31 publications

An Efficient Methodology to Identify Missing Tags in Large-Scale RFID Systems

An Efficient Methodology to Identify Missing Tags in Large-Scale RFID Systems

Dynamic Distributed Honeypot Based on Blockchain

Deformable Convolutional Matrix Factorization for Document Context-Aware Recommendation in Social Networks

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