From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based Mary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQMDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost

From M-ary Query to Bit Query: a new strategy for efficient large-scale RFID identification

The tag collision avoidance has been viewed as one of the most important research problems in RFID communications and bit tracking technology has been widely embedded in query tree (QT) based algorithms to tackle such challenge. Existing solutions show further opportunity to greatly improve the reading performance because collision queries and empty queries are not fully explored. In this paper, a bit query (BQ) strategy based Mary query tree protocol (BQMT) is presented, which can not only eliminate idle queries but also separate collided tags into many small subsets and make full use of the collided bits. To further optimize the reading performance, a modified dual prefixes matching (MDPM) mechanism is presented to allow multiple tags to respond in the same slot and thus significantly reduce the number of queries. Theoretical analysis and simulations are supplemented to validate the effectiveness of the proposed BQMT and MDPM, which outperform the existing QT-based algorithms. Also, the BQMT and MDPM can be combined to BQMDPM to improve the reading performance in system efficiency, total identification time, communication complexity and average energy cost

Fast splitting based tag identification algorithm for anti-collision in UHF RFID System

Efficient and effective objects identification using Radio Frequency Identification (RFID) is always a challenge in large scale industrial and commercial applications. Among existing solutions, the tree based splitting scheme has attracted increasing attention because of its high extendibility and feasibility. However, conventional tree splitting algorithms can only solve tag collision with counter value equals to zero and usually result in performance degradation when the number of tags is large. To overcome such drawbacks, we propose a novel tree-based method called Fast Splitting Algorithm based on Consecutive Slot Status detection (FSA-CSS), which includes a fast splitting (FS) mechanism and a shrink mechanism. Specifically, the FS mechanism is used to reduce collisions by increasing commands when the number of consecutive collision is above a threshold. Whereas the shrink mechanism is used to reduce extra idle slots introduced by FS. Simulation results supplemented by prototyping tests show that the proposed FSA-CSS achieves a system throughput of 0.41, outperforming the existing UHF RFID solutions

Towards Extended Bit Tracking for Scalable and Robust RFID Tag Identification Systems

The surge in demand for Internet of Things (IoT) systems and applications has motivated a paradigm shift in the development of viable radio frequency identification technology (RFID)-based solutions for ubiquitous real-Time monitoring and tracking. Bit tracking-based anti-collision algorithms have attracted considerable attention, recently, due to its positive impact on decreasing the identification time. We aim to extend bit tracking to work effectively over erroneous channels and scalable multi RFID readers systems. Towards this objective, we extend the bit tracking technique along two dimensions. First, we introduce and evaluate a type of bit errors that appears only in bit tracking-based anti-collision algorithms called false collided bit error in single reader RFID systems. A false collided bit error occurs when a reader perceives a bit sent by tag as an erroneous bit due to channel imperfection and not because of a physical collision. This phenomenon results in a significant increase in the identification delay. We introduce a novel, zero overhead algorithm called false collided bit error selective recovery tackling the error. There is a repetition gain in bit tracking-based anti-collision algorithms due to their nature, which can be utilized to detect and correct false collided bit errors without adding extra coding bits. Second, we extend bit tracking to 'error-free' scalable mutli-reader systems, while leaving the study of multi-readers tag identification over imperfect channels for future work. We propose the multi-reader RFID tag identification using bit tracking (MRTI-BT) algorithm which allows concurrent tag identification, by neighboring RFID readers, as opposed to time-consuming scheduling. MRTI-BT identifies tags exclusive to different RFIDs, concurrently. The concept of bit tracking and the proposed parallel identification property are leveraged to reduce the identification time compared to the state-of-The-Art. 2013 IEEE.This work was supported by the Qatar National Research Fund (a member of Qatar Foundation) through NPRP under Grant 7-684-1-127. The work of A. Fahim and T. ElBatt was supported by the Vodafone Egypt Foundation.Scopu

Identifying RFID Tags in Collisions

How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts