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Author: Zelalem Legese Hailemariam
Zelalem Legese Hailemariam
Thesis Title: 針對RFID抗碰撞協定之 知識基礎詢問樹
Knowledge-based Query Trees for RFID Anti-collision Protocols
Advisor: 賴源正
Yuan-Cheng Lai
Committee: 賴源正
Yuan-Cheng Lai
林伯慎
Bor-Shen Lin
楊傳凱
Chuan-Kai Yang
陳彥宏
Yen-Hung Chen
林志宗
Chih-Chung Lin
Degree: 博士
Doctor
Department: 管理學院 - 資訊管理系
Department of Information Management
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 99
Keywords (in Chinese): 無線射頻辨識詢問樹位元追蹤區分位元抗碰撞協定
Keywords (in other languages): RFID, query tree, bit-tracking, distinguished-bit, anti-collision protocol
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無線射頻辨識(Radio Frequency Identification, RFID)能以無線方式與最低限度的互動實現無所不在的追蹤與監控物件,其在許多應用中扮演了重要的角色,像是資產追蹤、無接觸支付、存取控制、運輸和物流以及其他工業應用。在RFID系統中,讀取器透過共享無線通道上的通訊來識別標籤。然而,當多個標籤同時傳輸其 ID 時訊號會發生碰撞,進而導致識別延遲(Identification Delay)之增加。許多抗碰撞(Anti-collision)的協定已經提出來應對這些挑戰,包括基於樹(Tree-based)和基於 ALOHA (ALOHA-based)的方法,前者主要包括詢問樹(Query Tree, QT)和二元樹(Binary Tree, BT)演算法。
在某些情況下,讀取器知道可能出現的標籤ID,此在本文中稱為「知識」。在本論文中,我們透過已知的知識建造一個詢問樹。我們提出了三種抗碰撞協定:一種基於知識且具有捷徑與配對解決的新型詢問樹(Query Tree with Shortcutting and Couple Resolution, QTSC)、基於知識的位元追蹤詢問樹 (Bit-tracking Knowledge-based Query Tree, BKQT)和基於知識的區分位元詢問樹(Distinguished-bit Searching Knowledge-based Query Tree, DKQT)協定。這些協定的主要概念是QTSC利用知識,而BKQT和DKQT不僅利用知識,還採用位元追蹤技術,允許讀取器感知碰撞槽中碰撞位元的位置。另一方面,BKQT 和 DKQT 的差異在於後者降低了前者建造詢問樹的時間複雜度。
首先,在QTSC中,建造一個基於知識的詢問樹(k-tree)來儲存識別資料庫中所有可能的標籤所需的查詢。然後使用捷徑來遊歷k-tree以識別出現的標籤,同時使用能在同一時槽內同時傳輸兩個ID前綴的配對解決技術來略過k-tree中的重複查詢。接下來,我們提出BKQT,它結合了兩種技術:儲存所有可能出現的標籤 ID之知識; 及允許讀取器檢測碰撞槽中發生碰撞位元位置之位元追蹤,BKQT亦會使用知識建造一個k-tree,同時它使用位元追蹤去建造位元碰撞案例和該k-tree中每個節點的對應操作。在識別過程中,BKQT遊歷此k-tree,因此能夠根據發生位元碰撞案例後採取行動以更快地識別碰撞標籤。最後,提出的DKQT協定建造一棵區分位元知識樹。在每個樹的節點中,該演算法可以從所有可能的標籤中尋找具有區分位元的標籤,並將該標籤儲存在資料庫中。因此,區分位元知識樹是一個n元樹,而不是二元樹。在識別標籤的過程中,DKQT演算法遊歷這個區分位元知識樹,如果一個標籤在所有出現的標籤中有一個區分位元,則讀取器便能直接在對應的查詢中識別該標籤。
模擬結果顯示,與現有的基於知識的協定:知識詢問樹(Knowledge Query Tree, KQT)和啟發式詢問樹(Heuristic Query Tree, H-QT)協定相比,QTSC分別將識別時間改進了60.5%和39.0%。與 KQT、H-QT 和 QTSC 相比,BKQT 分別將識別時間改進了 44.3%、46.4% 和 25.1%。而與QTSC和BKQT相比,DKQT 將識別時間改進了 41.1% 和 21.1%。


Radio Frequency Identification (RFID) has allowed the realization of ubiquitous tracking and monitoring of physical objects wirelessly with minimum human interactions. It plays a crucial role in many applications, including asset tracking, contactless payment, access control, transportation, and logistics. In Radio Frequency Identification (RFID) systems, the reader identifies tags through communications over a shared wireless channel. However, when multiple tags transmit their IDs simultaneously, their signals collide, prolonging the identification delay. Numerous anti-collision protocols have been proposed to address these challenges, including the tree-based and ALOHA-based approaches. The former mainly include Query tree (QT) and the Binary Tree (BT) algorithms.
In some scenarios, the reader knows the IDs of the possible tags which might appear; called “knowledge” here. In this dissertation, we consider the knowledge query tree, which is a query tree by using known knowledge. We present three anti-collision protocols; A novel Knowledge-based Query Tree with Shortcutting and Couple Resolution (QTSC), A Bit-tracking Knowledge-based Query Tree (BKQT), and A Distinguished-bit Searching Knowledge-based Query Tree (DKQT) protocol respectively. The main concepts of these protocols are that QTSC utilizes knowledge while BKQT and DKQT not only utilize knowledge but also adopt the bit-tracking technique, which allows the reader to perceive the locations of collided bits in a collision slot. On the other hand, the difference between BKQT and DKQT is the latter reduces the time complexity of the former in constructing the query tree.
First, in QTSC, a knowledge-based query tree (k-tree) is constructed to store the queries required to identify all of the possible tags in the database. Then, traversing this k-tree with a shortcut to identify the appearing tags. Couple-resolution techniques, which can transmit two ID prefixes simultaneously within the same slot, are employed to skip redundant queries in the k-tree. Next, we propose BKQT, which combines the two techniques: knowledge, which stores all tag IDs that possibly occur, and bit-tracking, which allows the reader to detect the locations of collided bits in a collision slot. BKQT also constructs a k-tree using knowledge while it constructs bit-collision cases and the corresponding actions for each node in this k-tree using bit-tracking. In the identification process, BKQT traverses this constructed k-tree and thus identifies the colliding tags faster by taking the actions according to the happening bit-collision cases. Finally, the proposed DKQT protocol constructs a distinguished-bit knowledge tree. In each tree’s node, the algorithm searches for a tag that has a distinguished-bit from all possible tags and stores the tag in the database. Thus, the distinguished-bit k-tree is an n-ary tree, rather than a binary tree. During the tag identification process, the DKQT algorithm traverses this constructed distinguished-bit k-tree. If a tag has a distinguished-bit from all appearing tags, the reader directly identifies the tag in the corresponding query.
The simulation results show that compared to the existing knowledge-based protocols, Knowledge Query Tree (KQT) and Heuristic Query Tree (H-QT) protocols, QTSC improves the identification time by 60.5% and 39.0%, respectively. BKQT improves the identification time by 44.3%, 46.4%, and 25.1%, compared with KQT, H-QT, and QTSC. DKQT improves the identification time by 41.1% and 21.1% compared to QTSC and BKQT.

Table of Contents 摘要 III Abstract V Acknowledgments VII Table of Contents IX List of Figures XIII List of Tables XV Chapter 1 Introduction 1 1.1 RFID Tag Anti-collision Protocols 1 1.2 Motivations and Contributions 4 1.3 Organization 5 Chapter 2 Literature Review 6 2.1 Knowledge-based protocols 6 2.1.1 Query Tree (QT) 6 2.1.2 Knowledge-based Query Tree (KQT) 7 2.1.3 Heuristic Query Tree (H-QT) 7 2.1.4 Query Tree with Knowledge Splitting (QTKS) 8 2.2 Bit-tracking protocols 8 Chapter 3 A Knowledge-based Query Tree with Shortcutting and Couple Resolution 12 3.1 K-tree Construction Procedure 13 3.2 Identification Procedure 16 3.2.1 Shortcutting 17 3.2.2 Couple Resolution 18 3.2.3 Pseudo Code 18 3.3 Illustrative Example of Identification Operation 21 3.4 Performance Evaluation 23 3.4.1 Number of Appearing Tags 23 3.4.2 Number of Possible Tags 26 3.4.3 Tag ID Length 27 3.4.4 Tag Similarity 29 3.5 Summary 30 Chapter 4 A Bit-tracking Knowledge-based Query Tree 32 4.1 Notations 32 4.1 K-Tree Construction 34 4.2 Bit-Collision Cases 37 4.3 Action Construction 38 4.4 Identification Operation 40 4.5 An Example of BKQT 42 4.1 Performance Evaluation 45 4.1.1 Appearing Tags 46 4.1.2 Possible Tags 48 4.1.3 Tag ID Length 49 4.1.4 Tag Similarity 50 4.2 Summary 51 Chapter 5 A Distinguished-bit Searching Knowledge-based Query Tree 52 5.1 A Distinguished-bit Knowledge Tree Construction 52 5.2 DKQT Identification Operation 58 5.3 An Example of a DKQT Operation 59 5.4 Time Complexity Analysis 63 5.5 Performance Evaluation 63 5.5.1 Appearing Tags 64 5.5.2 Possible Tags 66 5.5.3 Tag ID Length 67 5.5.4 Tag ID Similarity 68 5.6 Summary 69 Chapter 6 Conclusions and Future Works 70 6.1 Conclusions 70 6.2 Future Works 72 References 74 Glossary 79 Publication List 80 Vita 81

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