中 華 大 學
碩 士 論 文
中文題目:
改良 改良 改良
改良樹狀搜尋 樹狀搜尋 樹狀搜尋 樹狀搜尋之 之 之 之 RFID 防碰撞 防碰撞 防碰撞 防碰撞技術 技術 技術 技術
系 別 所: 資訊工程學系碩士班 學號姓名: E09502010 黃義彬 指導教授: 許 慶 賢 博士
中華民國 九十七 年 七 月
An Enhanced Query Tree (EQT) Protocol for Memoryless Tag Anti-Collision in RFID
Networks
By
Yi-Pin Huang
Advisor: Prof. Ching-Hsien Hsu
Department of Computer Science and Information Engineering Chung Hua University
Hsinchu, 30067, Taiwan
July 2008
中文摘要
RFID(Radio Frequency IDentification)，已 漸 漸被廣泛應 用在日 常 生活 上，而RFID 是利用frequency 來辨識ID。因此漸漸也遇到一些在辨識上的問 題，同時多數標籤讀取之Tag 訊號會有碰撞問題。RFID 系統是由無線電波傳 送訊號，若是Tag一個一個傳送訊號，讀取率自然高，但是，當多數電子標籤 同時經過Reader時，處理Tag 訊號碰撞的技術就很重要，目前產業中常用的多 數 標 籤 讀 取 技 術 有 防 碰 撞 技 術 (Anti-collision) 與 二 進 位 搜 尋 技 術 (Binary search) 等，防碰撞技術基於碰撞機率，Tag會於訊號碰撞時，於下一段時間 間隔重複傳送訊號，以確保可以完全讀取，但是為全部讀取多數Tag時會延長 讀取時間。二進位搜尋技術(Binary search) 可避免訊號碰撞問題，在多數Tag 同時讀取時，可以提高正確讀取率並縮短讀取時間。有鑑於此，防止碰撞在 RFID系統中是一個很重要的課題。改善碰撞節點而提高整體系統效率的方法已 經有很多學者提出改善的方法。本篇論文提出一個改良的樹狀詢問(EQT)方法，
目的在於改善樹狀詢問技術，應用在無記憶體標籤(memoryless tag)防止碰撞的效 率。EQT主要的概念是針對RFID 網路層中標籤反應識別碼後所產生的碰撞節 點，動態調整讀取器前串字元(prefix bit)，然而EQT 也會依據標籤識別碼的長 度，用二分法的方式來調整讀取器前串字元實驗結果顯示， EQT改進很多碰撞 結點在標籤密度60%以下。我們使用這二個方法是因為我們發現到規劃好讀取器 的前串字元(prefix bit)是可以改善標籤反應識別碼的次數，而在高密度的標籤環 境下讓標籤的反應次數變少,並且讀取器可以辨識到標籤的識別碼，是可以提升系 統效率。改善系統效率也是我們提出EQT 方法的最大精神所在。
關鍵詞：RFID，樹狀收尋、EQT、防標籤碰撞
Abstract
Ratio Frequency IDentification (RFID) is considered as the key technology of the ubiquitous computing systems. It can identify the unique tag ID, or other information associated with the tag. Compare with the bar code systems, the data capabilities of an RFID tag are superior to a bar code and the identification efficiency is much efficient.
Another benefit is that with RFID, there is no need for line of sight back to a reader whereas bar codes require a scanner to pass over each item.
One of the challenges in designing modern RFID systems is that when more than one Tag exists in an RFID environment, it may occurs collisions so that the whole system becomes inefficient and increases the time for identified RFID Tags. In order to simultaneously recognize several tags within a reader interrogation zone, an anti-collision algorithm should be applied. In this thesis, we present an Enhanced Query Tree (EQT) protocol for memoryless tag anti-collision in RFID networks. The EQT is
an enhanced technique based on the query tree protocol. The main concept of EQT is dynamically adjusting prefix bit of reader’s inquiry in order to minimize collision amount. To evaluate performance of the proposed techniques, we have implemented the EQT along with other tree based protocols. The experimental results show that the EQT outperforms traditional tree based approaches in terms of the identification
overhead and collision amount. Especially, the EQT performs very well in neutral RFID systems.
Keywords: RFID, Query Tree, Enhanced Query Tree, Tag Anti-Collision
Acknowledgements
首先我最感謝許慶賢教授.我剛到中華大學就讀時,對 RFID 領域完全是不 了解,在許教授的指導下,我開始跟隨教授研究 RFID 領域。 教授不厭其煩的和 我討論並指點我正確的方向，使我在這 2 年中獲益匪淺。教授對學問的嚴謹更 是我要學習的典範。
我在許教授的指導下除了與教授一起研究 RFID 專業領域，還學習到一個 終身受益的東西就是細心。與教授討論中教授總是不斷的提醒我，凡事抱著大 膽假設，細心去證明是否合理。 這樣細心的態度在社會工作真的是受益無窮。
論文的完成我還要感謝二位耐心教我的學長逸民跟仁豪以及常常在我旁邊 提醒我的榮崇同學，有了大家不斷的教導與提醒我，使得我完成了本論文。
最後我要感謝我的家人，有了你們全力的支持，這二年給了我最大的力量。
Table of Contents
Chienese Abstract………... ...I English Abstract... II Acknowledgements...III Table of Contents ...IV List of Figures ... V
1 Introduction...1
1.1 Motivation...1
1.2 Objective ...3
1.3 Thesis Organization ...3
2 Related Work...4
3 Preliminary...7
3.1 Tree Based Tag -collision Protocol ...7
3.2 Flaw of Tree Based Search ...8
4 Enhance Query Tree ...9
5 Performance Evaluation and Results...21
5.1 Simulator and Comparison ...21
5.2 Experiment Results ...23
6 Conclusions and Future work...30
Reference ...31
List of Figures
Figure 1: An example of Binary Tree Algorithm ...8
Figure 2: The Query Tree Paradigm ...10
Figure 3: Query Tree Protocol for example 1...12
Figure 4: Hybrid Query Tree Protocol for example 1...13
Figure 5: Enhance Query Tree Protocol for example 1 ...14
Figure 6: Query Tree Protocol for example 2...15
Figure 7: Hybrid Query Tree Protocol for example 2...16
Figure 8: Enhance Query Tree Protocol for example 2 ...16
Figure 9: Query Tree Protocol for example 3...18
Figure 10: Hybrid Query Tree Protocol for example 3...19
Figure 11: Enhance Query Tree Protocol for example 3 ...19
Figrue 12: Collision Cycle Estimated by HQTsim [15]. ...21
Figure 13: A snapshot of EQTsim. ...22
Figure 14: Comparisons of IO in very-low density (3%) RFID system ...24
Figure 15: Comparisons of CA in very-low density (3%) RFID system...24
Figure 16: Comparisons of QA in very-low density (3%) RFID system……...24
Figure 17: Comparisons of IO in low density (3%-20%) RFID system...25
Figure 18: Comparisons of CA in low density (3%-20%) RFID system ...25
Figure 19: Comparisons of QA in low density (3%-20%) RFID system ……….…...26
Figure 20: Comparisons of IO in medium density (21%-40%) RFID system...26
Figure 21: Comparisons of CA in medium density (21%-40%) RFID system ... 27
Figure 22: Comparisons of QA in medium density (21%-40%) RFID system……....27
Figure 23: Comparisons of IO in high density (41%-60%) RFID system...……..28
Figure 24: Comparisons of CA in high density (41%-60%) RFID system ...…….28
Figure 25: Comparisons of QA in high density (41%-60%) RFID system……….….28
Figure 26: Improvement Ratio (IR) of the EQT to HQT on collision amount ……....29
CHAPTER 1 Introduction
1.1 Motivation
Radio Frequency Identifier (RFID) System is an automatic technology aids machines or computers to identify objects, record metadata or control individual target through radio waves. The RFID system is composed by two components, tags and readers. An RFID tag is comprised of integrated circuit with an antenna for storing information and communication, respectively. An RFID reader is capable of reading the information stored at tags located in its sensing range. The electronics in the RFID reader use an outside power resource to generate signal to drives the reader’s antenna and turn into radio wave. The radio wave will be received by RFID tag which will reflect the energy in the way of signaling its identification and other related information. In matured RFID systems, the reader’s RF can also instruct the memory to be read or written from which the tag contained. Many applications, such as supply chain automation, identification of products at check-out points, security and access control, localization, and object tracking have been developed to take the primary function of RFID systems.
Advantages of RFID technologies, such as price efficiency, fast deployment, reusable and accuracy of stock management also broaden the scope of applications of RFID systems. Advanced characteristics of recent RFID readers, like size miniaturization and capabilities of Wi-Fi or cellular also motivate the development of large scale RFID systems. In recent RFID technologies, it is motivated that an RFID system can be integrated with wireless sensor network by interfacing RFID tags with external sensing capabilities, such as light, temperature or shock sensors; forming a hybrid infrastructure that combines advantages of both techniques, such as accurate identification,
monitoring of objects and efficient deployment. Similar to wireless sensor network, RFID tags can be deployed in an ad-hoc fashion instead of pre-installed statically. As a result, RFID has gradually been applied to our daily lives so that some identification problems happen. When readers try to identify multiple tags, signals collision will appear because the RFID system is through radio waves to carry signals. If one tag follows another one, the read rate is naturally high, but if multiple tags simultaneously pass through readers, the collision may happen. Therefore, efficient methods for identifying multiple tags simultaneously are of great importance for the development of large-scale wireless RFID systems. At present, there are anti-collision technologies and tree-based technologies for identifying multiple tags. Anti-collision technologies are based on collision rates, when signals collide, tags will repeatedly send signals in next interval of time to make sure completed read. For the complete read of multiple tags, the read time would be extended. The problem of signal collision can be avoided by the binary search technology to raise correct read rates and to shorten the read time when multi-tags are read at the same time. Regarding that the query tree technology can raise correct read rates, that may be true, but there still have challenges in long identification latency.
1.2 Objective
Collision problem happened while RFID is with multiple readers and tags. In this thesis we present an Enhanced Query Tree (EQT) method to improve the collision problems caused by multi-tags respond the signal to reader at the same time. For examining this technology, this thesis uses the simulator to prove that we can improve the tag collision problem. Performance analysis and simulation comparisons are conducted under different density of RFID networks. Both theoretical analysis and experimental results show that the EQT technique can alleviate tag collisions under different circumstances.
1.3 Thesis Organization
The rest of this thesis is organized as follows: In Chapter 2, a brief survey of related work will be presented. Chapter 3 introduces the tree search collision problem.
The Enhanced Query Tree (EQT) protocol for memoryless tag anti-collision will be presented in Chapter 4. Performance analysis and simulation comparisons will be given in Chapter 5. Finally, in Chapter 6, some concluding remarks are made.
CHAPTER 2 Related Work
Tree-based protocols are introduced as anti-collision protocols. The major advantages of tree-based protocols are their implementation simplicity and low power consumption [1]. Many research results have been proposed in literature. Security and privacy related literatures [19] focused on methods of preserving and protecting privacy of RFID tags; the RFID reader collision avoidance and hidden terminal problems were first addressed in [5] aiming to enhance accuracy of RFID systems; the energy saving and coverage problem [13, 21] was extensively studied in order to improve lifetime of wireless RFID networks.
Research efforts for collision avoidance have been presented in literature.
Frequency Division Multiple Access (FDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) [6] are four basic access methods to categorize MAC-layer protocols. Standard collision avoidance protocols like RTS-CTS [17] cannot be directly applied in RFID systems due to the reason, in traditional wireless networks, the CTS are sent back to the sender. Similar situation in RFID system, when a reader broadcasts an RTS, all tags in the read range need to send back CTS to the reader. It then requires another collision avoidance mechanism for CTS, and it will make the protocol more complicated.
Techniques for resolving RFID reader collision problems are usually proposed as reader anti-collision techniques or tag anti-collision solutions. The Colorwave [20] is a scheduling-based approach prevents RFID readers from simultaneously transmitting signal to an RFID tag. The Colorwave is used as a distributed anti-collision system
based on TDMA in RFID network. Pulse protocol [1] is referred as beacon broadcast and CSMA mechanism. Readers periodically in separated control channels send a
“beacon” during communication with tags. The contend_back-off and the delay_before_beaconing in the protocol are similar in wireless networks. If the reader
receives a beacon, the residual back-off timer will be stored and kept till the next coming chance. This process is expected to achieve the fairness among all readers. A coverage-based RFID reader anti-collision mechanism was proposed in [7]. Kim et al.
[7] presented a localized clustering coverage protocol for solving reader collision problems occurring among homogeneous RFID readers. In [3], Cha et al. proposed two ALOHA-based algorithms with a Tag Estimation Method (TEM) for speedup object identification in RFID systems.
The existing tag identification approaches can be classified into two main categories, the Aloha-based [8, 9, 14, 18, 22] anti-collision scheme and the tree-based scheme [2, 12, 15, 24]. RFID readers in the former scheme create a frame with a certain number of time slots, and then add the frame length into the inquiry message sending tags in its vicinity. Tags response the interrogation based on a random time slot. Because collisions may happen at the time slot when two or more tag response simultaneously, making those tags could not be recognized. Therefore, the readers have to send inquiries contiguously until all tags are identified. As a result, Aloha-based scheme might have long processing latency in identifying large-scale RFID systems [9]. In [18], Vogt et al. investigated how to recognize multiple RFID tags withinthe reader's interrogation ranges without knowing the numberof tags in advance by using framed Aloha. A similar research is also presented in [22] by Zhen et al. In [8], Klair et al. also presented a detailed analytical methodology and an in-depth qualitative energy consumption analysis of pure and slotted Aloha anti-collision protocols. Another anti-collision algorithm called enhanced dynamic
framed slotted aloha (EDFSA) is proposed in [10]. EDFSA estimates the number of unread tags first and adjusts the number of responding tags or the frame size to give the optimal system efficiency.
In tree-based scheme, such as ABS [12], IBBT [4] and IQT [16], RFID readers split the set of tags into two subsets and labeled them by binary numbers. The reader repeats such process until each subset has only one tag. Thus the reader is able to identify all tags. The adaptive memoryless tag anti-collision protocol proposed by Myung et al. [11] is an extended technique based on the query tree protocol. Choi et al. also proposed the IBBT (Improved Bit-by-bit Binary-Tree) algorithm [4] in
Ubiquitous ID system and evaluate the performance along three other old schemes.
The IQT protocol [16] is a similar work approach by exploiting specific prefix patterns in the tags to make the entire identification process. Recently, Zhou et al. [23]
consider the problem of slotted scheduled access of RFID tags in a multiple reader environment. They developed centralized algorithms in a slotted time model to read all the tags. With the fact of NP-hard, they also designed approximation algorithms for the single channel and heuristic algorithms for the multiple channel cases.
Although tree based schemes have advantage of implementation simplicity and better response time compare with the Aloha based ones, they still have challenges in decreasing the identification latency.
In this thesis, we present an enhanced tree based tag identification technique aims to coordinate simultaneous communications in large-scale RFID systems, to speedup tag identification time.
CHAPTER 3 Preliminary
3.1 Tree Based Tag-Collision Protocol
Radio Frequency IDentification (RFID) systems proceed with the identification function through radio frequency, but usually, not only one tag is applied to the identification, that means RFID reader needs to identify multiple tags at the same time.
However, collision problems may happen in the process of identifying multiple tags.
At present, the Binary Tree (BT) is commonly applied in solving the tag collision problems; nevertheless, when tag density is higher, search duplication may occur, and that wastes the time and frequency of identification. Figure 1 shows an example of identifying four RFID tags, using tree scan scheme. In the tree scan scheme, tags and reader are communicated through RF. An RFID reader will send a prefix bit 1 or 0 to all Tags in its vicinity. Tags have the same prefix code will reflect its ID to the corresponding reader. There are three sorts of node in tree scan scheme, the identified node, the collision node and the idle node. The identified node means that only one Tag replied reader’s prefix query and transmit its ID code to the reader successfully.
The collision node represents that more than one tag match the same prefix code and replies their ID to the corresponding RFID reader. The Idle node shows that no tags with the same prefix code. Tree search protocol will cause a serious problem that is tag transmit ID code to reader repeatedly and increase the difficulty of identification so that the all system efficiency gradually decrease.
We can observe that if the depth of a query tree becomes deeper, almost each layer has collisions. Once collision happens, it’s a waste of identification time. As a result,
the standard binary tree protocol [10] and the query tree protocol [9] have such drawbacks of long identification time.
0 1
00 01 10 11
Tag A Tag B Tag C Tag D
Figure 1: An example of Binary Tree Algorithm
3.2 Flaw of Tree Based Search Scheme
If the density of an RFID system is relative high, the collisions might happen at all levels of a query tree. Once the collision happens frequently, it’s a waste of identification time. In other words, the exhaust tree search scheme is not a good method in minimizing number of collision nodes.
To alleviate the collision problem, a reader may skip some unnecessary prefix match if it is aware that system density is high. By adjusting the length of reader’s prefix bit, the collision node could be minimized.
CHAPTER 4
Enhance Query Tree (EQT)
This chapter presents the proposed Tag Anti-collision technique, named Enhanced Query Tree (EQT). Because the EQT is based on the tree scan scheme, we first explain some notations and terminologies used in this kind of approach as the preliminary for understanding our proposed algorithms.
Figure 2 shows the paradigm of tree structure usually used in Tag Anti-collision in query tree scheme. A reader scans the full binary tree start from step-1. If the two nodes in the step-1 are collision nodes, the reader goes to next level of the query tree, i.e., step-2, and so on, until all RFID tags are identified. The main reason that the collision node happened is that multiple tags respond its ID code to reader simultaneously. In this thesis, we propose an algorithm to change reader’s prefix bit dynamically, the reader’s prefix bit is counted by using the length of tag ID code. Intuitionally, if the scan of tree steps can be reduced, a reader will decrease the overall identification time.
0 1
00 01 10 11
. . . . . .
. . . . .
. .
. . . . .
. . . . . .
Tag 1 Tag 2 Tag 3 . . . . . . . . . Tag N-2 Tag N-1 Tag N
Level 0 Level 1 Level K
Figure 2: The Query Tree Paradigm
Given a full binary tree with height = (log N ) +1, representing a binary query tree, In EQT scheme, a reader starts the identification process by sending an inquiry with prefix
bit (RPB),
RPB= ………. (1) where N is the length of Tag ID. If a collision is found on the level of step 1, it will increase the prefix bit by N/4,
RPB= ………..… (2) Similarly, if the reader finds collision node in step2, it increases the prefix bit by N/8.
RPB= ……….…(3) According to (1), (2) and (3), the reader prefix bit (RPB) can be formulated as follows,
2 N
+
4 2
N N
+
+
8 4 2
N N N
RPB=
∑
K S S
N
2 ……….………..…(4)
Where N is the length of tag ID, S is the tree step, K is the log N.
If collisions happened at level k-1, it is clear that two tags are exists with the same parent node at level k-1. Therefore, a reader can simply identify both of the two tags without sending a prefix inquiry with length N.
We use an example to demonstrate the identification process of the three algorithms, EQT, HQT and QT.
Example1: Assume an RFID environment consists of six tags, with ID = {Tag A= 0000, Tag B= 0001, Tag C= 0011, Tag D=1000, Tag E= 1010, Tag F=1011}
Figure 3 detailed the identification of the Query Tree protocol. The reader first send an inquiry with prefix code 0, Tag A, Tag B and Tag C response its ID code to the reader simultaneously and resulting a collision. Similarly, when the reader sends prefix code 1, Tag D, Tag E and Tag F all response its ID code to the reader simultaneously, collisions happened again. The reader extends the prefix code by one bit, sending 00, again, Tag A, Tag B, and Tag C match the prefix code and all response its ID code to the reader simultaneously, the collision happened again. On the right sub-tree of the internal node “0”, the reader sends prefix code 01 as an inquiry in level 1, it receives no tag’s response and marks the node “01” as an idle node. Contiguously, the reader uses prefix code 10 as inquiry, Tag D, Tag E and Tag F all response its ID code to reader simultaneously, the collision also happened. For prefix code 11, there is no tag matches the prefix code and results the node “11” as an idle node. For prefix code 000, Tag A and Tag B will response its ID code to the reader simultaneously, the collision still happened. For prefix code 001, only Tag C responses ID code to the reader,
therefore, Tag C is identified. Table 1 summarizes the complete identification process of the Query Tree protocol. It spends 14 steps to identify all RFID Tags.
X X
init
X
X
X
X
0 1
00 01
000 001
0000 0001
10 11
100 101
1010 1011
0011
0000 0001 1010 1011
1000
Figure 3: Query Tree Protocol for example 1
Figure 4 detailed the identification of the Hybrid Query Tree protocol. The reader prefixes code by two bit, on the query tree level 0. The reader first send with prefix code 00, Tag A, Tag B and Tag C response its ID code to the reader simultaneously and result a collision. Contiguously, the reader send prefix code 01, it receives no tag’s response and marks the node “01” as an idle node. Similarly, the reader uses prefix code 10, Tag D, Tag E and Tag F all response its ID code to reader simultaneously, the collision also happened. Contiguously, the reader send prefix code 11, it receives no tag’s response and marks the node “11” as an idle node. The query tree level 0, right sub-tree of the internal node “00” will be collision node, the reader inquiry with prefix code on level 1. For prefix code 0000, only Tag A responses ID code to the reader, therefore, Tag A is identified. Table 1 summarizes the complete identification process of the Hybrid Query Tree protocol. It spends 12 steps to identify all RFID Tags.
Figure 4: Hybrid Query Tree Protocol for example 1
Figure 5 detailed the identification of the Enhance Query Tree protocol. The reader prefixes code by two bit, on the query tree level 0. The reader first send with prefix code 00, Tag A, Tag B and Tag C response its ID code to the reader simultaneously and result a collision. Contiguously, the reader send prefix code 01, it receives no tag’s response and marks the node “01” as an idle node. Similarly, the reader uses prefix code 10, Tag D, Tag E and Tag F all response its ID code to reader simultaneously, the collision also happened. Contiguously, the reader send prefix code 11, it receives no tag’s response and marks the node “11” as an idle node. The query tree level 0, right sub-tree of the internal node “00” will be collision node, the reader inquiry with prefix code on level 1. For prefix code 000, Tag A , Tag B response ID code to the reader, therefore, Tag A ,Tag B are identified. Table 1 summarizes the complete identification process of the Enhance Query Tree protocol. It spends 8 steps to identify all RFID Tags.
X X init
00 10
000 001 100 101
0011 1000
000 101
01 11
Figure 5: Enhance Query Tree Protocol for example 1
Table1: Performance comparison for example 1
According to the examples shown in Figure 3, 4 and 5, Table 1 summarizes the detailed identification process of the three algorithms. For the Query Tree protocol, it takes 14 steps to completely identify all tags; for the HQT, it takes 12 steps to completely identify all tags; for the EQT, it takes 8 steps to completely identify all tags. The efficiency of EQT system is the best, following is HQT, then QT.
One thing worthy to mention is that if all tags are with different bits in the half of high
significant bits, it forms the best case for EQT. The following example explains this fact.
Example 2: Assume an RFID environment consists of sixteen tags, with ID =
{Tag A= 00001010, Tag B= 00011101, Tag C= 00101010, Tag D= 00110111, Tag E=
01001111, Tag F= 01011101, Tag G= 01101100, Tag H= 01111010, Tag I= 10000001,
Tag J= 10010011, Tag K= 10100110, Tag L= 10110110, Tag M= 11001111, Tag N=
11011101, Tag O= 11100011, Tag P= 11111110}
Figure 6, identification of the Query Tree protocol. The reader must go through query tree level 0 to level 3 to identify all tags. We got the query tree level 0 to level 2 should be collision node happened. Table 2 summarizes the complete identification process of the Query Tree protocol. It spends 30 steps to identify all RFID Tags.
Init
X X
X
X X
00001010
X
X X
00011101 00101010 00110111 01001111 01011101 01101100 01111010 10000001 10010011 10100110 10110110 11001111 11011101 11100011 11111110
X
X X
X
X X
00 01 10 11
0 1
000 001 010 011 100 101 110 111
0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
Figure 6: Query Tree Protocol for example 2
Figure 7, identification of the Hybrid Query Tree protocol. The reader must go through query tree level 0 to level 1 to identify all tags. We got the query tree level 0 should be collision node happened. Table 2 summarizes the complete identification process of the Hybrid Query Tree protocol. It spends 20 steps to identify all RFID Tags.
Init
00001010 00011101 00101010 00110111 01001111 01011101 01101100 01111010 10000001 10010011 10100110 10110110 11001111 11011101 11100011 11111110
X X X X
00 01 10 11
0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
Figure 7: Hybrid Query Tree Protocol for example 2
Figure 8, identification of the Enhance Query Tree protocol. The reader only the query tree level 0 to identify all tags. Therefore, different bits in the half of high significant bits, it forms the best case for EQT. Table 2 summarizes the complete identification process of the Enhance Query Tree protocol. It spends 16 steps to identify all RFID Tags.
Init
00001010 00011101 00101010 00110111 01001111 01011101 01101100 01111010 10000001 10010011 10100110 10110110 11001111 11011101 11100011 11111110
0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
Figure 8: Enhance Query Tree Protocol for example 2
Table2: Performance comparison for example 2
According to the examples shown in Figure 6, 7 and 8, Table 2 summarizes the detailed identification process of the three algorithms. For the Query Tree protocol, it takes 30 steps to completely identify all tags; for the HQT, it takes 20 steps to completely identify all tags; for the EQT, it takes16 steps to completely identify all tags. The efficiency of EQT system is the best, following is HQT, then QT.
The last example reveals a shortcoming of the EQT scheme. When the RFID system is with low density of tags, the EQT scheme performs not as ideal as previous example.
Example 3: Assume an RFID environment consists of four tags, with ID = {Tag A=
00000000, Tag B= 10010100, Tag C= 01000111, Tag D= 11100110}
Figure 9, identification of the Query Tree protocol. The reader must go through query tree level 0 to level 1 to identify all tags. There were no idle node happened by low density environment. Table 3 summarizes the complete identification process of the Query Tree protocol. It spends 6 steps to identify all RFID Tags.
Init
0 1
X
00000000
X
01000111 10010100 11100110
00 01 10 11
Figure 9: Query Tree Protocol for example 3
Figure 10, identification of the Hybrid Query Tree protocol. The reader only the query tree level 0 to identify all tags, there were no idle and collision node happened.
Therefore, different bits in the half of high significant bits and low density, it forms the best case for HQT. Table 3 summarizes the complete identification process of the Hybrid Query Tree protocol. It spends 4 steps to identify all RFID Tags.
Init
00000000 01000111 10010100 11100110
00 01 10 00
Figure 10: Hybrid Query Tree Protocol for example 3
Figure 11, identification of the Enhance Query Tree protocol. The reader only the query tree level 0 to identify all tags, but increase a lot of the idle node by EQT. Table 3 summarizes the complete identification process of the Enhance Query Tree protocol. It spends 16 steps to identify all RFID Tags.
Init
00000000 01000111 10010100 11100110
0000 0001 0010 0011 0100 0101 0110 0111 1000 1001 1010 1011 1100 1101 1110 1111
Figure 11: Enhance Query Tree Protocol for example 3
Table3: Performance comparison for example 3
According to the examples shown in Figure 9, 10 and 11, Table 3 summarizes the detailed identification process of the three algorithms. For the Query Tree protocol, it takes 6 steps to completely identify all tags; for the HQT, it takes 4 steps to completely identify all tags; for the EQT, it takes16 steps to completely identify all tags. The efficiency of HQT system is the best, following is QT, then EQT.
The last Example shows that in lower density environment, the EQT will increase a lot idle nodes. The EQT could not avoid these idle nodes because if tag was in moveable environment the reader could not know when the new tag transmits ID code. Thus, if we remove these idle nodes, the reader will not identify new tags, the identification is getting worse. In this consideration we keep idle nodes.
CHAPTER 5
Performance Evaluation and Results
5.1 Simulator and Comparison Metrics
In this section, we first introduce the simulator we used in the simulation (HQTsim).
HQTsim can randomly generate a set of Tag IDs. To verify the correctness of the simulator used in this thesis, we compare the collision cycle estimated by HQTsim [15]
and EQTsim as shown in Figure 12 and Figure 13, respectively. Show the results we could see the simulative environment we built is same as HQT. So we make sure our comparison data.
Figure 12: Collision Cycle Estimated by HQTsim [15]
Figure 13: A snapshot of EQTsim
To validate the performance of our proposed techniques, we have implemented the HQT and EQT algorithms. Identified Overhead (IO), Collisions Amount (CA) and Query Amount (QA) are used for comparing the effectiveness of both algorithms.
To validate the performance of our proposed techniques, we have implemented the HQT and EQT algorithms. Identified Overhead (IO), Collisions Amount (CA) and Query Amount (QA) are used for comparing the effectiveness of both algorithms.
Identified Overhead (IO), is the time for reader to identify all tags in its vicinity;
Collisions Amount (CA), is the number of collisions in an identified process; Query Amount (QA), is the number of prefix query sent by a reader to identify all tags. All of
the three criteria represent overhead in identifying RFID tags, therefore, lee is better.
5.2 Experiment Results
The simulative environment we built in the thesis is tag ID length=16bit, we try to simulate system identified time, the amount of collision nodes and the frequency of the reader query by discriminating the density of Tag.
The first experiment is conducted within 3% tag density. Figure 14 shows the comparison of EQT and HQT in terms of identification overhead (IO). The HQT performs better than the EQT scheme. This is because we increased the frequency of the query amount (QA), so the HQT has less identification time than the EQT method in very low density environment. Figure 15 gives EQT has less collision amount (CA) than the HQT method. Figure 16 show HQT has less query amount (QA) than the EQT method.
0 0.5 1 1.5 2 2.5 3
0.01 0.1 0.3 0.5
# of Tags﹙×103﹚
Time (S)
HQT EQT
Figure 14: Comparisons of IO in very-low density (3%) RFID system
﹙×103﹚
0 0.1 0.2 0.3 0.4 0.5
0.01 0.1 0.3 0.5
# of Tags﹙×103﹚
Number
HQT EQT
Figure15: Comparisons of CA in very-low density (3%) RFID system
﹙×103﹚
0 0.8 1.6 2.4 3.2 4
0.01 0.1 0.3 0.5
# of Tags﹙×103﹚
Number
HQT EQT
Figure 16: Comparisons of QA in very-low density (3%) RFID system
The second experiment is conducted with 3%~20% tag density. Figure 17 shows the comparison of EQT and HQT in terms of identification overhead (IO). The EQT performs better than the HQT scheme. This is because we lower the collision amount (CA) and the query amount (QA), so the EQT has less identification time than the HQT method in low density environment. Figure 18 gives EQT has less collision amount (CA) than the HQT method. Figure 19 show EQT has less query amount (QA) than the HQT method.
0 8 16 24 32 40
2 4 6 8 10 12 13
# of Tags﹙×103﹚
Time (S)
HQT EQT
Figure 17: Comparisons of IO in low density (3%-20%) RFID system
﹙×103﹚
0 2 4 6 8 10
2 4 6 8 10 12 13
# of Tags﹙×103﹚
Number
HQT EQT
Figure18: Comparisons of CA in low density (3%-20%) RFID system
﹙×103﹚
5 11 17 23 29 35
2 4 6 8 10 12 13
# of Tags﹙×103﹚
Number
HQT EQT
Figure 19: Comparisons of QA in low density (3%-20%) RFID system
The third experiment is conducted with 21%~40% tag density. Figure 20 shows the comparison of EQT and HQT in terms of identification overhead (IO). The EQT performs better than the HQT scheme. This is because we lower the collision amount (CA) and the query amount (QA), so the EQT has less identification time than the HQT method in medium density environment. Figure 21 gives EQT has less collision amount (CA) than the HQT method. Figure 22 show EQT has less query amount (QA) than the HQT method.
34 42 50 58 66 74
14 16 18 20 22 24 26
# of Tags﹙×103﹚
Time (S)
HQT EQT
Figure20: Comparisons of IO in medium density (21%-40%) RFID system
﹙×103﹚
6 8 10 12 14 16
14 16 18 20 22 24 26
# of Tags﹙×103﹚
Number
HQT EQT
Figure 21: Comparisons of CA in medium density (21%-40%) RFID system
﹙×103﹚
30 36 42 48 54 60
14 16 18 20 22 24 26
# of Tags﹙×103﹚
Number
HQT EQT
Figure 22: Comparisons of QA in medium density (21%-40%) RFID system
The fourth experiment is conducted with 41%~60% tag density. Figure 23 shows the comparison of EQT and HQT in terms of identification overhead (IO). The EQT performs better than the HQT scheme. This is because we lower the collision amount (CA) and the query amount (QA), so the EQT has less identification time than the HQT method in high density environment. Figure 24 gives EQT has less collision amount (CA) than the HQT method. Figure 25 show EQT has less query amount (QA) than the HQT method.
60 70 80 90 100
27 29 31 33 35 37 39
# of Tags﹙×103﹚
Time (S)
HQT EQT
Figure 23: Comparisons of IO in high density (41%-60%) RFID system
﹙×103﹚
12 15 18 21 24
27 29 31 33 35 37 39
# of Tags﹙×103﹚
Number
HQT EQT
ar
Figure 24: Comparisons of CA in high density (41%-60%) RFID system
﹙×103﹚
55 60 65 70 75 80
27 29 31 33 35 37 39
# of Tags﹙×103﹚
Number
HQT EQT
Figure 25: Comparisons of QA in high density (41%-60%) RFID system
In Figure 26, EQT improved HQT collision amount (CA) under these for density environment. EQT has lower efficiency to improve HQT under very low tag density (under 3%) environment. EQT has the best efficiency to improve HQT collision amount (CA) in low tag density (3%~20%) environment. In medium tag density (21%~40%) environment and high tag density (41%~60%) environment, EQT improve HQT collision amount (CA) constantly and stably and keep the best condition.
﹙×103﹚
0 0.3 0.6 0.9 1.2
0.01 0.3 2 10 16 24 31 39
# of Tags﹙×103﹚
Number
Figure 26: Improvement Ratio (IR) of the EQT to HQT on collision amount Improvement Rate (IR)
CHAPTER 6
Conclusions and Future work
One of the challenges in designing modern RFID systems is that when more than one Tag exists in an RFID environment, it may occurs collisions so that the whole system becomes inefficient and increases the time for identified RFID Tags. In order to simultaneously recognize several tags within a reader interrogation zone, an anti-collision algorithm should be applied. In this thesis, we have presented an Enhanced Query Tree (EQT) protocol for memoryless tag anti-collision in RFID networks. The major concept of EQT is dynamical adjust prefix bit of the reader according to Query Tree level and tag ID code length. The result of our experiment is EQT improve collision amount (CA) greatly under tag density 60% environment and enhance the system efficiency.
In this thesis, we simulated the identified efficiency to different algorithms, but we did not consider the question of the application in reality. For example, the problem of the reader signals interrupt to each other will influence the efficiency of the Tag identification and affect the judgment of the Tag data. Therefore, how to avoid the reader signals interrupt to each other is a necessary research in the future so that we could represent the researched method in the real RFID environment.
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