## 1 Introduction  
The Radio Frequency Identification (RFID) technique [1; 2; 3] is broadly applied as the automated identification system for Internet of Things (IoT) environment because of its feasibility, convenience, fast and contactless object identification method, as compared with the conventional bar-code system. An RFID system consists of a reader and several tags. Each tag has a unique identification (ID), and the reader identifies objects which attached tag through radio signal. During the tag identification procedure, tags may transmit signals to the reader simultaneously and collide each other, meaning that the bandwidth resource is wasted and the duration of the identification process is prolonged. A well-designed tag anti-collision protocol is thus required for RFID tag identification.  
Many RFID anti-collision algorithms were proposed and can be classified into two categories, which are Aloha-based [4] and Tree-based [5] algorithms. The Aloha-based algorithms attempt to adjust the frame size to minimize the duration of the identification process by observing the probabilities of collision and successful transmission in current frame. On the other hand, the Tree-based algorithms repeatedly subdivide collided tags into groups until each group contains, at most, one tag.  
The Tree-based algorithms can be further classified into the binary tree protocol (BT) and query tree protocol (QT). For BT, the collided tags are separated by randomly choosing a binary number. With regards to QT, the collided tags are split by the reader's query.  
However, the channel quality changes rapidly in the wireless environment; an RFID reader cannot distinguish between a collision and a frame error. When there are frame errors, the conventional algorithms would misjudge the unreadable slots as collisions instead of bad channel condition, resulting in that the measured possibility of collision gets high. Therefore, the number of tags based on the possibility of collision would be overestimated, causing more unnecessary idle slots and lower bandwidth usage. This phenomenon is also called the channel error problem in RFID system [6].  
The error resilient estimation and adaptive binary selection algorithm (ERE-ABS) and its extension was proposed to address this channel error problem [6, 7]. ERE-ABS applies the packet error rate (PER) and number of tags to judge whether the received unreadable message is caused by collision or it is the result of channel error. However, ERE-ABS does not dynamically adjust PER and the number of tags, when the channel condition changes. This results in that the PER and the number of tags may be incorrectly estimated and therefore cannot reflect the actual condition with regards to the current RFID system, meaning that the idle slots increase, and the inferior bandwidth usage is inescapably caused.  
This study therefore develops a novel RFID anti-collision algorithm, called the Channel-quality Aware Query Tree algorithm (CAQT), to improve the identification performance under error-prone channel. The CAQT is based on query tree protocol and owns three novel features:  
1. it estimates the channel quality continuously and statistically in the rapidly changing channel quality environment to improve the tag estimation;
2. it asks the tag for retransmission or to split the collide tags based on the channel quality in order to reduce the number of idle slots;
3. the number of the groups which it splits tags is based on the estimated number of tags collide in current slot in order to accelerate the tag identification process.  
The structure of this study is as follows. Section 2 presents the RFID system overview, error-prone channel, and previous studies. Section 3 introduces the concept and pseudo codes of the proposed CAQT. Section 4 demonstrates simulation results including the comparisons with CAQT and previous anti-collision algorithms. Section 5 concludes this study.