Tag Anti-Collision Resolution for Improved Quality of · PDF fileTag Anti-Collision Resolution for Improved Quality of RFID ... Anti-Collision using Precise ... 3.2 Collision Problems

Tag Anti-Collision Resolution for Improved

Quality of RFID Data Streams

Prapassara Pupunwiwat

BIT (Hons)

School of Information and Communication Technology

Science, Environment, Engineering and Technology

Griffith University

A Thesis submitted in fulfillment

of the requirements of the degree of

Doctor of Philosophy

September 2011

Abstract

Radio Frequency Identification (RFID) is a technology that allows automatic identification

of people or objects by incorporating the use of radio frequency waves to transmit data

between networked electromagnetic readers and tags. RFID is considered an emerging

technology for advancing a wide range of applications, such as supply chain management

and distribution. However, despite the extensive development of the RFID technology in

many areas, the RFID tags collision problems remain a serious issue. Collision problems

occur due to the simultaneous presence of multiple numbers of tags within the reader zone.

To solve collision problems, different anti-collision methods have been mentioned in

literature. These methods are either insufficient or too complex, with a high overhead cost

of implementation. In this work, in order to improve the quality of RFID data collection,

we propose novel deterministic and probabilistic anti-collision approaches.

The main contributions of this study are summarised as follows:

• We propose two novel deterministic anti-collision algorithms using combinations of

Q-ary trees (Pupunwiwat and Stantic, 2009a,b, 2010c), with the intended goal to

minimise memory usage queried by the RFID reader. By reducing the size of queries,

the RFID reader can preserve memories, and the identification time can be improved.

• We propose a novel frame-size estimation technique (Pupunwiwat and Stantic,

2010a,b) to minimise the number of slots and frames queried by the RFID reader

and to maximise the system efficiency. In addition, we introduce the probabilistic

group-based anti-collision method (Pupunwiwat and Stantic, 2010d) to improve

the overall performance of the tag recognition process.

• We evaluate our proposed anti-collision techniques and perform a comparative anal-

ysis, in order to find the benefits and disadvantages of each method. Additionally, in

order to identify the best selection of anti-collision method, we propose two strate-

gies for selective anti-collision technique management, i.e. a Novel Decision Tree

Strategy and a Six Thinking Hats Strategy (Pupunwiwat et al., 2011). By correctly

identifying the most suitable anti-collision method for specific scenarios, the quality

of data collection can be improved.

Statement of Originality

This work has not previously been submitted for a degree or diploma in any university. To

the best of my knowledge and belief, the thesis contains no material previously published

or written by another person except where due reference is made in the thesis itself.

Signed:

Prapassara Pupunwiwat

September 19, 2011

Acknowledgements

First and foremost, I would like to thank my supervisor Dr Bela Stantic, for his extraor-

dinary support and encouragement. During the past four years, I learned a lot from Dr

Stantic about good research works and what it takes to accomplish them. He directed me

though this journey and I would not be where I am now without his guidance.

I would also like to thank Professor Abdul Sattar, for his valuable comments on my

research. My thank also to the School of Information and Communication Technology for

providing such a good research environment and allowing me to gain academic skills in

casual tutoring.

I would like to specifically thank Mrs Rohana Wendt for her precious feedback on the

writing aspect of my thesis. Also, I would like to thank all my colleagues and university

friends for their mental support, especially Mr Peter Darcy who helped me through difficult

times.

My thank also to my childhood friends and my brother, who never fail to cheer me

up when I least expected them. Also, my special gratitude to Mr Herman Wendt, who

consoled me and being there for me when I feel distressed, and for understanding my need

to devote most of my time to my thesis.

Finally, my special appreciation and thanks to my mum and dad, who have been a

tower of strength throughout my studies, and for their support and understanding. I

would never become who I am now without their dedication and love.

List of Publications

List of Book Chapter

P. Pupunwiwat and B. Stantic, (2012). Managing Tag Collision in RFID Data

Streams using Smart Tag Anti-Collision Techniques. Chipless and Chipped Radio

Frequency Identification: Systems for Ubiquitous Tagging, IGI Global, (in press).

P. Darcy, P. Pupunwiwat and B. Stantic, (2012). The Fusion of Pre/Post RFID

Correction Techniques to Reduce Anomalies. Intelligent Sensor Networks: Across

Sensing, Signal Processing, and Machine Learning, CRC Press, (in press).

P. Darcy, P. Pupunwiwat, and B. Stantic, (2011). The Challenges and Issues

facing the Deployment of RFID Technology. Deploying RFID Challenges, Solutions, and

Open Issues (InTech2011), Rijeka, Croatia, InTech, Pages 1-26.

List of Journal

P. Pupunwiwat, P. Darcy, and B. Stantic, (2011). Conceptual Selective RFID

Anti-Collision Technique Management. Procedia Computer Science, volume 5, Ontario,

Canada, ELSEVIER, Pages 827-834.

P. Pupunwiwat and B. Stantic, (2007). Location Filtering and Duplication

Elimination for RFID Data Streams. The International Journal of Principles and

Applications in Information Science and Technology (PAIST), volume 1, Auckland,

Albany, New Zealand, PAIST Press, Pages 29-43.

List of Conferences

P. Pupunwiwat and B. Stantic, (2010). Joined Q-ary Tree Anti-Collision for

Massive Tag Movement Distribution. Thirty-Third Australasian Computer Science

Conference (ACSC 2010), Brisbane, Australia, Pages 99-108.

P. Pupunwiwat and B. Stantic, (2010). Dynamic Framed-Slot ALOHA

Anti-Collision using Precise Tag Estimation Scheme. Twenty-First Australasian

Database Conference (ADC 2010), Brisbane, Australia, Pages 19-28.

P. Pupunwiwat and B. Stantic, (2010). A RFID Explicit Tag Estimation Scheme

for Dynamic Framed-Slot ALOHA Anti-Collision. Sixth Wireless Communications,

Networking and Mobile Computing (WiCOM 2010), Chengdu, China, Pages 1-4.

P. Pupunwiwat and B. Stantic, (2010). Resolving RFID Data Stream Collisions

using Set-Based Approach, Sixth International Conference on Intelligent Sensors, Sensor

Networks and Information Processing (ISSNIP 2010), Brisbane, Australia, Pages 61-66.

P. Pupunwiwat and B. Stantic, (2009). Unified Q-ary Tree for RFID Tag

Anti-Collision Resolution. Twentieth Australasian Database Conference (ADC 2009),

Wellington, New Zealand, Pages 47-56.

P. Pupunwiwat and B. Stantic, (2009). Performance Analysis of Enhanced Q-ary

Tree Anti-Collision Protocols. First Malaysian Joint Conference on Artificial

Intelligence (MJCAI 2009), Kuala Lumpur, Malaysia, Pages 229-238.

Contents

1 Introduction 1

2 RFID Background 5

2.1 History of RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 RFID Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.1 Elements of Radio Frequency Communication . . . . . . . . . . . . . 7

2.2.2 Radio Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.3 Interference and Multipath of RF waves . . . . . . . . . . . . . . . . 7

2.3 RFID Technology Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 RFID System Mechanism . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3.2 Characteristic of RFID data . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.3 Main RFID Commercial Applications . . . . . . . . . . . . . . . . . 11

2.3.4 RFID Technology in Supply Chain . . . . . . . . . . . . . . . . . . . 12

2.4 RFID Core Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.1 RFID Antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.4.2 RFID Reader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.4.3 RFID Tag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.5 RFID Data Management Issues . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.5.1 Data Capturing Process . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.5.2 Data Processing and Event Management . . . . . . . . . . . . . . . . 23

2.5.3 Data Warehousing and Data Mining . . . . . . . . . . . . . . . . . . 23

2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3 RFID Data Streams Management Techniques 25

3.1 Filtering of RFID Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1.1 Unreliable Reads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1.2 Noises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1.3 Duplications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.1.4 Missed Reads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Collision Handling in RFID Data Streams . . . . . . . . . . . . . . . . . . . 31

3.2.1 RFID Collision Types . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.2 Division Classification for Multi-Access . . . . . . . . . . . . . . . . 33

3.2.3 Taxonomy of RFID Tag Anti-Collision Protocols . . . . . . . . . . . 34

3.3 Deterministic Anti-Collision Protocols . . . . . . . . . . . . . . . . . . . . . 35

3.3.1 Binary Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3.2 Bit Arbitration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3.3 Tree Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.3.4 Query Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.4 Probabilistic Anti-Collision Protocols . . . . . . . . . . . . . . . . . . . . . . 42

3.4.1 BFSA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.4.2 DFSA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.4.3 EDFSA Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.4.4 Other ALOHA-Based Methods . . . . . . . . . . . . . . . . . . . . . 45

3.4.5 Backlog Estimation Techniques . . . . . . . . . . . . . . . . . . . . . 46

3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

3.5.1 Limitations of Existing Methods . . . . . . . . . . . . . . . . . . . . 48

3.5.2 Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4 Deterministic Anti-Collision Approaches 53

4.1 EPC Encoding Schemes Analysis . . . . . . . . . . . . . . . . . . . . . . . . 53

4.1.1 General Identifier 96 Bits . . . . . . . . . . . . . . . . . . . . . . . . 54

4.1.2 Serialised Global Trade Item Number 96 Bits . . . . . . . . . . . . . 54

4.1.3 Global Individual Asset Identifier 96 Bits . . . . . . . . . . . . . . . 55

4.2 Warehouse Distribution Scenarios . . . . . . . . . . . . . . . . . . . . . . . . 56

4.2.1 Unique Item-Level Scenario . . . . . . . . . . . . . . . . . . . . . . . 56

4.2.2 Unique Container-Level Scenario . . . . . . . . . . . . . . . . . . . . 57

4.2.3 Unique Company-Level Scenario . . . . . . . . . . . . . . . . . . . . 57

4.2.4 Unique Warehouse-Level Scenario . . . . . . . . . . . . . . . . . . . 58

4.3 Splitting Fitness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.3.1 Worst-Case Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.3.2 Perfect Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.3.3 Random Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.4 Unified Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.4.1 Unified Q-ary Tree Fundamental . . . . . . . . . . . . . . . . . . . . 60

4.4.2 Computation of Naive approach and Unified approach . . . . . . . . 61

4.4.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4.5 Joined Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.5.1 EPC Bits Prediction and Classification . . . . . . . . . . . . . . . . . 73

4.5.2 Unique Bits Computation . . . . . . . . . . . . . . . . . . . . . . . . 75

4.5.3 Tags Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76


4.5.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

4.6 Overall Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

5 Probabilistic Anti-Collision Approaches 89

5.1 Mathematic Fundamental for ALOHA-based Tag Estimation . . . . . . . . 89

5.2 Precise Tag Estimation Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 90

5.2.1 Slot Observation and Initial Q Value . . . . . . . . . . . . . . . . . . 91

5.2.2 Suggested Threshold for Frame-Size . . . . . . . . . . . . . . . . . . 91

5.2.3 PTES approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.2.4 Sample Tag Estimation and Allocation . . . . . . . . . . . . . . . . . 96


5.2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.3 Probabilistic Cluster-Based Technique . . . . . . . . . . . . . . . . . . . . . 109

5.3.1 Probabilistic Anti-Collision Algorithm using PTES . . . . . . . . . . 109

5.3.2 PCT Preliminary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

5.3.3 Sample Boundary Computation . . . . . . . . . . . . . . . . . . . . . 114

5.3.4 PCT Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118


5.3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

5.4 Overall Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

6 Conceptual Selective Technique Management 131

6.1 Chain Reaction from Data Collection Process . . . . . . . . . . . . . . . . . 131

6.2 Comparative Analysis of Deterministic and Probabilistic Techniques . . . . 132

6.2.1 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.2.2 Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 134

6.3 Strategies for Choosing Suitable Anti-Collision Techniques . . . . . . . . . . 136

6.3.1 Novel Decision Tree for Anti-Collision Methods Selection . . . . . . 137

6.3.2 Extended Solution for Complex Anti-Collision Methods Selection . . 141

6.3.3 Six Thinking Hats for Complex Anti-Collision Methods Selection . . 144

6.4 Applicability of Anti-Collision Techniques in Real World Scenario . . . . . . 149

6.4.1 Wine Warehouse Tag-and-Ship Scenario . . . . . . . . . . . . . . . . 149

6.4.2 Document Warehouse Scenario . . . . . . . . . . . . . . . . . . . . . 152

6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

7 Conclusions 155

7.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

7.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Bibliography 160

List of Figures

2.1 RFID Operational Frequencies . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 An example of: a) Refraction, b) Reflection, and c) Scattering . . . . . . . . 9

2.3 An example of how RFID tag, reader, middleware and application operate 10

2.4 An example of RFID-enabled Supply Chain System . . . . . . . . . . . . . 12

2.5 An example of: a) Simple antenna pattern, and b) Antenna pattern con-

taining protrusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2.6 Proper tag orientation for a linearly polarised antenna . . . . . . . . . . . . 15

2.7 Proper tag orientation for a circularly polarised antenna . . . . . . . . . . 16

2.8 Various types of anti-collision methods . . . . . . . . . . . . . . . . . . . . 18

2.9 An example of: a) Passive Tag, b) Semi-passive/Semi-active Tag, and c)

Active Tag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1 An example of three readers deployment, where R1 and R2 covered S1, and

R2 and R3 covered S2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Collision Problems in RFID System: a) Reader-Reader Collision, b) Reader-

Tag Collision, and c) Tag-Tag Collision . . . . . . . . . . . . . . . . . . . . 32

3.3 Taxonomy of RFID Readers anti-collision protocols . . . . . . . . . . . . . 33

3.4 Taxonomy of RFID Tags anti-collision protocols . . . . . . . . . . . . . . . 35

3.5 Binary Tree Memory based anti-collision protocol . . . . . . . . . . . . . . 38

3.6 Query Tree Memoryless based anti-collision protocol . . . . . . . . . . . . . 39

3.7 The starting point of tag identification in tree-based protocols . . . . . . . 40

3.8 Tree-based protocols: a) Query tree protocol, b) 4-ary tree protocol . . . . 41

3.9 A sample procedure of Frame-slotted ALOHA . . . . . . . . . . . . . . . . 42

3.10 Empty Slot, Successful Slot, and Collision Slot in EPC Class 1 Generation

2 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

4.1 Crystal Warehouse Scenario: a) Unique Item-Level, b) Unique Container-

Level, c) Unique Company-Level, and d) Unique Warehouse-level . . . . . 56

4.2 Splitting Fitness: a) Worst-Case Splitting, b) Perfect Splitting, and c) Ran-

dom Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4.3 A sample of 16 tags from the same pallet with the same Object Class and

16 unique Serial Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.4 A sample of: a) Naive 4-ary Tree, and b) Unified 4-ary & 8-ary Tree . . . . 61

4.5 Identification processes of: a) Naive 2-ary Tree, b) Naive 4-ary Tree, c)

Unified 2-ary & 4-ary Tree, and d) Unified 4-ary & 2-ary Tree, . . . . . . . 62

4.6 Level-Packaging: a) a case with 6 glasses, and b) a pallet with 27 cases . . . 65

4.7 Performances of Naive Q-ary Trees on different set of tags . . . . . . . . . 66

4.8 Performances of sixteen combination of Q-ary Trees (4 Naive and 12 Uni-

fied) where F = 36 and S = 60 . . . . . . . . . . . . . . . . . . . . . . . . . 68

4.9 Results of two Naive approaches (2-ary, 4-ary) and two Unified approaches

(2-ary & 4-ary, 4-ary & 2-ary) for number of Idle cycles, Collision cycles,

Successful cycles, and Overall cycles . . . . . . . . . . . . . . . . . . . . . . 69

4.10 Results of two Naive approaches (2-ary, 4-ary) and two Unified approaches

(2-ary & 4-ary, 4-ary & 2-ary) for Number of bits queried for Idle cycles,

Collision cycles, Successful cycles, and Overall cycles . . . . . . . . . . . . . 70

4.11 Performance Analysis of 2-ary Tree vs. 4-ary Tree on Unique bits of EPC,

Bit 61 - 68, until all tags are identified. Results of Overall cycles are displayed 71

4.12 A sample of: a) a Naive 4-ary Tree, b) a Naive 2-ary Tree, and c) a Joined

Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

4.13 Joined Q-ary Tree structure for GID-96 bits EPC . . . . . . . . . . . . . . 74

4.14 Joined Q-ary Tree structure for GID-96 bits EPC with 36 Identical bits

Header and GMN, 20 Identical bits OC, 4 Unique bits OC, 30 Identical bits

SN, and 6 Unique bits SN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.15 Performances comparison (GID-96) between Naive approaches and Joined

Q-ary approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.16 Percentage of improvement (GID-96) of Joined Q-ary Tree compared with

Naive 2-ary Tree and Naive 4-ary Tree . . . . . . . . . . . . . . . . . . . . . 82

4.17 Accumulative Bits Length (GID-96) of three approaches: a) Naive 2-ary

Tree, b) Naive 4-ary Tree, and c) Joined Q-ary Tree on different tag sets . . 83

4.18 Performances comparison between Naive approaches and Joined Q-ary ap-

proach using different Encoding Scheme: a) GID-96 bits, b) SGTIN-96 bits,

and c) GIAI 96 bits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

4.19 Percentage of improvement of Joined Q-ary Tree compared with Naive 2-

ary Tree and Naive 4-ary Tree, using different Encoding Scheme: a) GID-96

bits, b) SGTIN-96 bits, and c) GIAI 96 bits . . . . . . . . . . . . . . . . . . 87

5.1 Variable V1 and V2 for Collision slot and Empty slot calculation for

PTES[CE] method. There are ninety-nine possible combinations of V1 and

V2, in order to find optimal parameters for c and e prediction . . . . . . . . 94

5.2 A sample first round of tag allocation with Initial Q of 4. Collision slot c

= 7, Empty slot e = 4, and Successful slot s = 5 . . . . . . . . . . . . . . . 97

5.3 A sample of Q-adjust in each round of identification until all tags are identified 98

5.4 A sample second round of tag allocation with Initial Q of 4, V1 = 2.0, and

V2 = 0.5. Collision slot c = 2, Empty slot e = 5, and Successful slot s = 9 . 100

5.5 A sample third round of tag allocation with Initial Q of 3, V1 = 2.0, and

V2 = 0.5. Collision slot c = 1, Empty slot e = 3, and Successful slot s = 4 . 101

5.6 A sample fourth (final) round of tag allocation with Initial Q of 2, V1 =

2.0, and V2 = 0.5. Collision slot c = 0, Empty slot e = 2, and Successful

slot s = 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.7 Performance efficiency of PTES[C], Sch, and LB methods, using different

Initial Q: a) PTES[C] 200 tags and b) PTES[C] 300 tags . . . . . . . . . . . 103

5.8 Performance efficiency of PTES[CE], PTES[CCE], Sch, and LB methods,

using different Initial Q: a) PTES[CE] 200 tags, b) PTES[CE] 300 tags, c)

PTES[CCE] 200 tags and d) PTES[CCE] 300 tags . . . . . . . . . . . . . . 104

5.9 Performance efficiency (a) and Number of frames (b) of PTES[C] (V1 =

2.3 to 2.5) versus Sch methods, using Initial Q of 8 on different tag sets . . 106

5.10 Results of PTES[CE] and PTES[CCE] (V1 = 2.3, V2 = 0.1) versus Sch

methods using Initial Q of 8 on different tag sets: Performance efficiency

(a: PTES[CE], c: PTES[CCE]) and Number of frames (b: PTES[CE], d:

PTES[CCE]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.11 Performance efficiency of different frame-size on different number of tags . 111

5.12 The minimum and maximum boundaries and their correlated percentage

of efficiency for frame-size of 256 . . . . . . . . . . . . . . . . . . . . . . . . 113

5.13 Number of slots comparison (a) and Performance efficiency (b) for DFSA,

EDFSA, PCT128, PCT256, and PCT-E methods on different number of tags126

5.14 Percentage of improvement of PCT compared with DFSA and EDFSA

methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

6.1 Comparative analysis of deterministic versus probabilistic anti-collision

methods: a) Number of slots comparison and b) Performance efficiency . . 135

6.2 Novel Decision Tree for Anti-Collision Methods Selection . . . . . . . . . . 137

6.3 Novel Decision Tree for Local Pen Maker Company (SME) Anti-Collision

Methods Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

6.4 Novel Decision Tree for Local Notebook Manufacturer (SME)

Anti-Collision Methods Selection . . . . . . . . . . . . . . . . . . . . . . . . 139

6.5 Novel Decision Tree for International Stationery Enterprise Anti-Collision

Methods Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

6.6 Novel Decision Tree for International A-Grade Filing and Storage Group

Anti-Collision Methods Selection . . . . . . . . . . . . . . . . . . . . . . . . 141

6.7 Six Thinking Hats Framework . . . . . . . . . . . . . . . . . . . . . . . . . 143

6.8 Six Thinking Hats: Global Trading Enterprise (GTE) Scenario . . . . . . . 146

6.9 Wine Warehouse Tag-and-Ship Scenario . . . . . . . . . . . . . . . . . . . . 150

6.10 Document Warehouse Scenario . . . . . . . . . . . . . . . . . . . . . . . . . 153

List of Tables

2.1 The Uniform Resource Identifier (URI) encoding complements the EPC Tag

Encodings defined for use within RFID tags and other low-level architec-

tural components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.1 A sample of noise where * indicates a noise reading. Since the noise thresh-

old equals to 3 and the tag catch is only for GID encoding, any tag that

appears less than three times within a specific time frame or does not satisfy

tag catch requirement, is classified as noise . . . . . . . . . . . . . . . . . . 27

3.2 A sample of data duplication, where TagE is captured twice and TagF is

captured three times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3 A sample of Missed reads where at time 500msec, 800msec and 1000msec,

readings of TagA are missing . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.4 Identification process of Query Tree versus Hybrid Query Tree . . . . . . . 41

3.5 EDFSA Rule - The number of unread tags, optimal frame-size, and number

of group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1 The GID-96 includes three fields in addition to the Header, with a total of

96-bits binary value. Only ‘H’ is shown in Binary, while the rest are shown

in Decimal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.2 The SGTIN-96 includes six fields with a total of 96-bits binary value. Only

‘H’ is shown in Binary, while the rest are shown in Decimal . . . . . . . . . 54

4.3 SGTIN-96 and GIAI-96 Partitions in bits . . . . . . . . . . . . . . . . . . . 55

4.4 The GIAI-96 includes five fields with a total of 96-bits binary value. Only

‘H’ is shown in Binary, while the rest are shown in Decimal . . . . . . . . . 55

4.5 The Unified Q-ary Tree can be merged into twelve different combinations.

1, 2, 3, and 4 represent the Number of bits queries each time for splitting

tags when collision occurred . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.6 Calculation of Total memory bits required for two Naive and two Unified Q-

ary Trees. TNBL shows the Total Number of Bits required for the specific

Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.7 Sample Outcomes for 5 tags identification using Naive and Unified approaches 63

4.8 This Table shows Total Memory Bits required for each Q-ary Tree for 192

tags set identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.9 Performance Analysis of 2-ary Tree vs. 4-ary Tree on Unique bits of EPC,

Bit 61 - 68, until all tags are identified . . . . . . . . . . . . . . . . . . . . . 72

4.10 Formal structure of bits classification of EPC GID-96 bits. *UOC is number

of Unique bits within Object Class and **USN is number of Unique bits

within Serial Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.11 Sample bits classification of EPC GID-96 bits, where Object Class = 12

and Serial Number = 60 (Total of 720 tags) . . . . . . . . . . . . . . . . . . 75

4.12 Sample 36 bits tags with 24 Identical bits and 8 Unique bits . . . . . . . . . 76

4.13 Identification process of 2-ary Tree and 4-ary Tree on Identical bits and

Unique bits of EPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

4.14 Calculation of Total Bits Length required for two Naive Q-ary Trees and a

Joined Q-ary Tree. TNBL shows the Bits Length required for the specific

Naive/Joined Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

4.15 Performance Analysis of Naive 2-ary Tree, Naive 4-ary Tree, and Joined

Q-ary Tree on set of 10 sample tags . . . . . . . . . . . . . . . . . . . . . . 77

4.16 Chosen EPC Pattern for Experiment One . . . . . . . . . . . . . . . . . . . 78

4.17 Identical and Unique Bits classification of EPC GID-96 bits for Experiment

one - Test case A, B, and C. I = Identical bits, U = Unique bits . . . . . . 79

4.18 Actual Separating Point for Experiment one - Test case A, B, and C. At a

specific SP, Joined Q-ary Tree will adjust its branch to either 2-ary or 4-ary

Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

4.19 Chosen EPC Pattern for Experiment Two . . . . . . . . . . . . . . . . . . . 80

4.20 Percentage improvement of the proposed Joined Q-ary Tree versus existing

Naive 2-ary (N2) and Naive 4-ary (N4) approaches . . . . . . . . . . . . . . 81

4.21 Accumulative Bits Length of three approaches: Naive 2-ary Tree, Naive

4-ary Tree, and Joined Q-ary Tree . . . . . . . . . . . . . . . . . . . . . . . 82

4.22 Number of bits length of three approaches using different Encoding Scheme:

a) GID-96 bits, b) SGTIN-96 bits, and c) GIAI 96 bits . . . . . . . . . . . . 84

4.23 Percentage of improvement of Joined Q-ary Tree versus two Naive ap-

proaches using different Encoding Scheme: a) GID-96 bits, b) SGTIN-96

bits, and c) GIAI 96 bits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.1 Suggested frame-size boundary (B) and minimum and maximum number

of tags (NT) for specific estimated number of tags . . . . . . . . . . . . . . 91

5.2 PTES methods comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.3 Sample tag estimation and frame-size (Q) adjustment after the first round

of identification, using PTES[C] method . . . . . . . . . . . . . . . . . . . . 97

5.4 Sample tag estimation and frame-size (Q) adjustment after the second

round of identification, using PTES[C] method . . . . . . . . . . . . . . . . 98

5.5 Sample tag estimation and frame-size (Q) adjustment after the third round


5.6 Sample tag estimation and frame-size (Q) adjustment after the fourth round


5.7 Sample tag estimation and frame-size (Q) adjustment after the fifth round


5.8 Chosen Parameters for Experiment One . . . . . . . . . . . . . . . . . . . . 102

5.9 Chosen Parameters for Experiment Two . . . . . . . . . . . . . . . . . . . . 102

5.10 Performance efficiency of PTES[C], Sch, and LB methods, using Initial of

8 on different sets of tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.11 Performance efficiency of PTES[CE], PTES[CCE], Sch, and LB methods,

using Initial of 8 on different sets of tags . . . . . . . . . . . . . . . . . . . . 107

5.12 Available Information and Missing fields on System Efficiency. MinB =

Minimum point of occurrence, MaxB = Maximum point of occurrence . . . 112

5.13 Derived Equations for Missing fields on System Efficiency. MinB = Mini-

mum point of occurrence, MaxB = Maximum point of occurrence . . . . . . 114

5.14 The conversion of PCT rules to β Beta, κ Kappa, and µ Mu . . . . . . . . . 114

5.15 PCT256 Boundary Computation - number of group (Frame-Size 256 and

128), and minimum and maximum boundaries . . . . . . . . . . . . . . . . . 119

5.16 PCT256 Rule - The number of unread tags, optimal frame-size (A and B),

and number of group (A and B) . . . . . . . . . . . . . . . . . . . . . . . . . 120

5.17 PCT128 Boundary Computation - number of group (Frame-Size 128 and

64), and minimum and maximum boundaries . . . . . . . . . . . . . . . . . 121

5.18 PCT128 Rule - The number of unread tags, optimal frame-size (A and B),

and number of group (A and B) . . . . . . . . . . . . . . . . . . . . . . . . . 121

5.19 PCT-E Boundary Computation - number of group (Frame-Size 256, 128

and 64), and minimum and maximum boundaries . . . . . . . . . . . . . . . 123

5.20 PCT-E Rule - The number of unread tags, optimal frame-size (A, B, C),

and number of group (A, B, C) . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.21 Chosen Parameters for Experiment Three . . . . . . . . . . . . . . . . . . . 125

5.22 Number of slots comparison and Performance efficiency for DFSA, EDFSA,

PCT128, PCT256, and PCT-E methods on different number of tags . . . . 125

5.23 Percentage improvement of the proposed PCT128, PCT256, and PCT-E

versus existing EDFSA (ED) and DFSA (D) techniques . . . . . . . . . . . 127

6.1 Chosen EPC Pattern of Tree-based anti-collision methods for Comparative

Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

6.2 Chosen Parameters of ALOHA-based anti-collision methods for Compara-

tive Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

6.3 Number of slots and performance analysis for Joined Q-ary Tree (100 tags),

Joined Q-ary Tree (50 tags), PCT256 no group, and PCT256 on different

number of tags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

6.4 Preferred Anti-Collision Method for Each Location (Zone 1 - 4) in GTE

scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

6.5 Selected Anti-Collision Method using Decision Tree and Six Thinking Hats

Strategies. Joined Q-ary Tree = JQT; PCT Group = PCT-G; PCT no

Group = PCT-NG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149

Abbreviations and Symbols

List of Abbreviations

AABS Adaptive Binary SplittingAQS Adaptive Query Splitting

BBA Bit ArbitrationBBT Bit-by-Bit Binary TreeBFSA Basic Framed-Slotted ALOHABS Binary SplittingBTS Binary Tree Splitting

CCDMA Code Division Multiple AccessCEP Complex Event ProcessingCP Company Prefix

DDBSA Dynamic Binary Search AlgorithmDFSA Dynamic Framed-Slotted ALOHADoD DoD IdentifierDSPI Device Service Provider Interface

EEAS Electronic Article SurveillanceEBBT Extended Bit-by-Bit Binary TreeEDFSA Enhanced Dynamic Framed-Slotted ALOHAEM ElectromagneticEPC Electronic Product CodeESP Extensible Sensor Stream Processing

FFDMA Frequency Division Multiple AccessFV Filter Value

GGIAI Global Individual Asset IdentifierGID General Identifier NumberGMN General Manager NumberGRAI Global Returnable Asset IdentifierGTE Global Trading Enterprise

HH HeaderHF High FrequencyHQT Hybrid Query Tree

IIAR Individual Asset ReferenceID-BTS ID Binary Tree StackImpQT Improved QTIntQT Intelligent Query TreeIR Item ReferenceIRE Institute of Radio EngineeringISO International Organisation of StandardsIT Information Technology

JJQT Joined Query Tree

LLB Lower Bound methodLBT Listen Before TalkLF Low Frequency

MMBBT Modified Bit-by-Bit Binary TreeMF Microwave Frequency

NNBL Number of Bits per LevelNBQ Number of Bits per QueryNCN Number of Child Node

OOC Object Class

PPCT Probabilistic Cluster-Based TechniquePOS Point of SalePT PartitionPTES Precise Tag Estimation Scheme

QQT Query TreeQTR QT-based Reservation

RRF Radio FrequencyRFID Radio Frequency IdentificationRRE Redundant Reader EliminationRTLS Real-Time Location Systems

SSch Schoute methodSDMA Space Division Multiple AccessSGLN Serialised Global Location NumberSGTIN Serialised Global Trade Item NumberSME Small and Medium EnterpriseSN Serial NumberSP Separating PointSSCC Serialised Shipping Container Code

TTDMA Time Division Multiple AccessTNBL Total Bits Length/Total Number of BitsTS Tree Splitting

UUHF Ultra High FrequencyUOC Unique bits within Object ClassURI Uniform Resource IdentifierUSN Unique bits within Serial Number

List of Symbols

d e Ceiling (Round Up)β Betaκ Kappamax Maximummin MinimumQ Frame-Sizeµ MuV Variable

1Introduction

Radio Frequency Identification (RFID) technology uses radio frequency waves to auto-

matically identify people or objects. The main RFID systems consist of fast capturing

radio frequency tags and networked electromagnetic readers. RFID technology is cur-

rently emerging as an important technology for advancing a wide range of applications.

It has the potential to improve the efficiency of business processes by providing automatic

identification and data capture. The technology that forms the basis for RFID was first

developed during World War II where it was used to distinguish between friendly and

enemy aircrafts, or also known as Friend-or-Foe (Landt, 2001). The current interest in

RFID technology has grown rapidly and can now be certified by CompTIA RFID+ certi-

fication in order to validate the knowledge and skills of professionals who work with RFID

technology (CompTIA-RFID+, 2008). In the modern world, RFID technology is used in

different applications such as distribution and retail packaging, security, library system,

defence and military, health care, and baggage and passenger tracing at the airport.

RFID system mainly comprises the following components:

• Tag, which has a microchip attached to an antenna that transmits and responds to

radio signals of a particular frequency;

• Reader, which sends and receives RFID data to and from tags via antennas;

• Middleware, which preprocesses the RFID data and converts it into a meaningful

data; and

• Application software, which is a specific component that resides on host computer.

1

CHAPTER 1. INTRODUCTION

RFID reader retrieves information from tags and sends that information back to host

computer via middleware. RFID data streams, which is captured by readers, can be

accumulated very fast and does not carry much information because it is raw. These data

are inaccurate and need to be filtered in order to improve its database management.

In the past, RFID systems used proprietary technologies where no worldwide open

standards existed (Brown et al., 2007). There was only small inter-connectivity between

different RFID vendors’ products. Every vendor had their own readers, tags, antennas, and

equipment but none of the equipment could work together. This lack of inter-connectivity

made it challenging for companies to deploy RFID technology in a large global supply

chain. However, since 2006, many international and industrial organisations have created

open standards, which allow the problem to quickly disappear.

There are several methods of identification but the most common is to store a serial

number that uniquely identifies a person or object such as Electronic Product Code (EPC).

All EPC numbers contain strings of binary numbers, which provide a unique identity for

every physical object. All data captured by RFID readers before any further process are

known as dirty data. In order to improve efficiency of database, dirty data must be filtered

at the earlier stage soon after they were captured. The filtering of RFID data streams is

known as filtering at the edge, where data are still meaningless and easier to eliminate.

The main issue that usually arises in RFID data streams is the data stream errors.

There are four typical errors, which include unreliable reads, noises, missed reads, and

duplications/redundancies.

Unreliable reads occur when a deployment of RFID tags and readers has an environ-

mental interference such as metal or water nearby (Fishkin et al., 2004). It also occurs

due to the orientation or rotation of tags and readers, distance between tags and read-

ers, number of tags and readers in the interrogation zone, and number of objects moving

simultaneously. Noises occur when additional unexpected readings are generated. This

can be caused by RFID tags outside the normal reading scope accidentally captured by

the reader (Bai et al., 2006). Duplications or Redundancies can happen at two different

levels: Redundancy at reader level and Redundancy at data level (Derakhshan et al.,

2007). Redundancy at reader level occurs when there is more than one reader deployed to

cover a specific location, whilst Redundancy at data level occurs when data streams are

simultaneously captured more than once by a reader.

Several techniques for filtering RFID data have been proposed in literatures. However,

these techniques only filter specific kind of errors generated. Therefore, the amount of

wrong data is still recorded into the database. The most common errors are missed reads,

which usually happen in a situation of low-cost and low power hardware that lead to

a frequently dropped reads (Derakhshan et al., 2007). Another cause of missed reads is

simultaneous transmissions in RFID systems, which lead to collisions as the readers and

tags typically operate on the same channel. Tag collisions in RFID systems happen when

2

multiple tags simultaneously reflect their respective signals back to the reader at the same

time, preventing the reader from identifying all tags. Filling in dropped reads is one way

to alter missed reads but it is sufficient to prevent missing data from the beginning. RFID

collision problem can be solved by using anti-collision techniques, to prevent two or more

tags from responding to a reader at the same time, and to re-identify them again when

collisions occurred.

The current deterministic anti-collision methods suffer from identification delay and

high memories usage during the identification process, while the probabilistic anti-collision

methods suffer from tag starvation problems due to inaccurate frame-size estimation and

low performance efficiency. In this research, a “Unified Q-ary tree” and a “Joined Q-ary

tree” anti-collision schemes are proposed, based on deterministic Q-ary Tree. The mo-

tivation of this work is the improvement of data quality obtained, and the minimal use

of memories required per complete identification. Methodologies for Unified and Joined

Q-ary Tree are first derived and experimental evaluations are then conducted, in order to

prove the efficiency of the proposed techniques. The results and analysis of the experi-

ments have indicated that both Unified and Joined Q-ary Tree can effectively reduce total

memories required, compared with current state-of-the-art techniques, which then results

in the minimal identification delay.

Additionally, we propose a new “Precise Tag Estimation Scheme” (PTES) for Backlog

estimation and frame-size prediction compatible with any probabilistic anti-collision tech-

nique. The motivation of this work is to achieve a more accurate estimation of number of

tags within an interrogation zone, which leads to a more accurate frame-size prediction

and system efficiency. The methodology for PTES is first derived, and experiments are

then conducted in order to prove the efficiency of the proposed technique. We also intro-

duce a group-selection approach called a “Probabilistic Cluster-Based Technique” (PCT)

method, to improve identification time and minimise number of frames and slots used

during an identification process. The experiment results have indicated that PTES, using

various parameters, has an impact on probabilistic anti-collision system efficiency. There-

fore, to achieve the best performance and solve the tag starvation problem, tags should be

grouped into specific size according to PCT threshold and the parameters for frame-size

prediction should be dynamically adjusted over the identification process.

We also assess our two proposed anti-collision techniques and carry out the compar-

ative analysis in order to find the benefits and disadvantages of each method. We then

propose two strategies for selective anti-collision technique management, in order to ob-

tain the optimal outcome of anti-collision method selection. From the investigation, we

have discovered that different anti-collision method has advantage over the other in some

cases. We found that, by correctly identifying the most suitable anti-collision technique

using our proposed “Novel Decision Tree Strategy” and “Six Thinking Hats Strategy”,

the data collection process can be improved; and the chain reaction toward the next level

of data transformation, aggregation, and event processing can be decreased. Thus, it is

3

CHAPTER 1. INTRODUCTION

important that the correct type of anti-collision algorithm is applicable to different sce-

narios. In addition, we also demonstrated the applicability of our proposed techniques

toward real world scenarios.

The remainder of this thesis is organised as follows:

In Chapter 2, some background information is provided on RFID including a brief

history of RFID, the fundamentals and technology overview, system components, and

RFID data management issues. The focus is particularly on the use and issues of RFID

technology in supply chain because there has been a great deal of interest in the topic

mainly over the past few years. The main components of RFID system including tags,

readers, and antennas are also described in depth.

In Chapter 3, four typical errors and their causes in RFID data streams are identified.

A literature on some current methods and their drawback are also discussed. These include

errors handling techniques for unreliable reads, noises, missed reads, and duplications.

Particularly, we focus on discussion of specific anti-collision methods for missed reads

caused by collisions. The shortcomings of existing methods are identified, and research

question is also proposed in this chapter.

In Chapter 4, major problems are investigated on existing deterministic anti-collision

schemes. Two proposed novel tree-based anti-collision methods, the Unified Q-ary Tree

and the Joined Q-ary Tree, are described and analysed. From experimental evaluation,

these approaches overcome the limitation of previously proposed approaches, and also

significantly preserve memories and improved the identification time. We have also iden-

tified and confirmed certain properties of importance for the deterministic anti-collision

methods in general.

In Chapter 5, main issues are addressed on existing probabilistic anti-collision schemes.

We discussed our proposed frame-size estimation method, PTES, and identified strategies

to overcome limitations of inaccurate Backlog estimation technique. Furthermore, we

described and analysed the PCT approach, to improve identification time and minimise

number of frames and slots used during an identification process. Certain properties of

importance for the probabilistic anti-collision methods in general, are also clarified in this

chapter.

In Chapter 6, comparative analysis is conducted between the deterministic Joined Q-

ary Tree and the probabilistic PCT. We discovered that both methods have advantages and

disadvantages over one another, depending on each specific case. We then introduced two

strategies, the Novel Decision Tree and the Six Thinking Hats, in order to find the optimal

method for specific scenarios. Additionally, we form a new concept and applicability of

each type of anti-collision approach, which we then apply to a sample real-world scenario.

Finally in Chapter 7, we conclude the thesis with a summary of the main findings of

our study and a discussion of future research plan.

4

2RFID Background

In this chapter, we present an overview of some background information including the

history of Radio Frequency Identification, the fundamentals and technology overview,

system components, and RFID data management issues.

2.1 History of RFID

Although many people believe that RFID is a new technology, it has an extensive history.

A more precise description of RFID is as an emerging technology, and its emergence is

best recognised by evaluating the history of RFID.

RFID systems are complex technologies that can be utilised in many ways. Some

RFID technologies have existed for a long time and have become more pervasive in the

supply chain. Other RFID technologies have been utilised in other industries such as

animal tracking, and they have unique advantages (Jones and Chung, 2008). Although

the history of RFID can be traced to the 1930s, the technology underlying RFID finds its

roots back in 1897, when Guglielmo Marconi invented the radio (Bhuptani and Moradpour,

2005).

In order to better define the development of RFID technology, the time-line summaries

are shown below (Hunt et al., 2007).

Pre 1940s: Beginning in 1896, Marconi, Alexanderson, Baird, Watson, and many

others, had tried to apply Electromagnetic Energy laws in radio communications and

radar. The work undertaken during this period had become the core of RFID technology.

5

CHAPTER 2. RFID BACKGROUND

1940s: After World War II, scientists and engineers continued the research in radio

frequency communications and radar. In October 1948, Harry Stockman published a paper

in the Proceedings of the Institute of Radio Engineering (IRE) called “Communications

by Means of Reflected Power”.

1950s: During the 1950s, many of the technologies related to RFID were explored by

researchers. For example, Harris Patent published a paper called “Radio Transmission

Systems with Modulatable Passive Responders”. The United States military also began

to implement an early form of aircraft, using RFID technology called “Friend-or-Foe”.

1960s: Some commercial activities began in the late 1960s, such as Sensormatic and

Checkpoint, which developed Electronic Article Surveillance (EAS) equipment for an-

titheft and security applications. EAS later became the first widespread commercial use

of RFID.

1970s: Government laboratories, academic institutions and companies, became in-

creasingly involved in RFID. In 1975, a paper titled “Short-Range Radio-telemetry for

Electronic Identification Using Modulated Backscatter”, written by Alfred Koelle, Steven

Depp, and Robert Freyman was released by Los Alamos Scientific Laboratory. By 1978,

a passive microwave tag had been accomplished and in 1979, these tags were used for

Animal Tagging.

1980s: The first widespread commercial use of the RFID systems began in the 1980s.

The systems were simple, such as livestock management, keyless entry, and personnel

access systems. In 1987, the world’s first Motor Vehicle Toll Collection was implemented

in Norway; and also in Dallas in 1989. However, all of the RFID systems implemented

in the 1980s were proprietary systems, which kept costs high and slowed down industry

growth.

1990s: By 1994, RFID toll systems could operate at highway speeds, which mean that

drivers could pass through toll points without the need to slow down. During this time,

several standards organisations also worked on publishing guidelines, including the Inter-

national Organisation of Standards (ISO). In 1999, the Auto-ID Center at Massachusetts

Institute of Technology (MIT) was also established for standard organisation purposes.

2000s: By early 2000, 5-cents tags had become a possible picture, and RFID tech-

nology could someday replace barcode systems. In 2003, both the world’s largest retailer

(Wal-Mart) and the world’s largest supply chain (the DoD) issued RFID mandates requir-

ing suppliers to begin employing RFID technology by 2005. Furthermore, the Auto-ID

Center was merged into EPCGlobal in 2006, and all standards were converted to one that

serves to increase competition among players in the industry, lower the costs of RFID, and

quicken the deployment of RFID technology.

2010s: Many industries continuously adopt and integrate RFID technology toward

their organisations. Challenges and opportunities that have arisen in RFID system have

been constantly tackled and improved.

6

2.2. RFID FUNDAMENTALS

2.2 RFID Fundamentals

Radio frequency uses the electromagnetic (EM) waves with different frequencies for com-

munication. Radio frequencies involve a small portion of a larger EM spectrum, where

the radio signals are affected in many ways. The total EM spectrum includes other higher

frequency waves such as light, ultraviolet, X-ray, gamma-ray, and cosmic-ray. This section

explains further elements of RF communication, the radio spectrum, and the interference

and Multipath of RF waves.

2.2.1 Elements of Radio Frequency Communication

Radio frequency (RF) communication uses the electromagnetic waves with frequencies

from a specific part of the EM frequency spectrum (Sanghera et al., 2007). Therefore, the

underlying physics behind RF communication is the same as for any communication that

uses electromagnetic waves to carry information.

The followings are the four major elements that make this communication happen:

• Data signal: This is the wave that actually contains the information that needs to

be sent to the receiver.

• Carrier signal: This is the wave that carries the data signal.

• Modulation: This is the process that encodes the data signal into the carrier signal

and creates the radio wave that is actually transmitted by the antenna.

• Antenna: This is a device used to transmit and receive signals.

2.2.2 Radio Spectrum

Within the radio spectrum is an enormous range of frequencies. To categorise and man-

age the different areas of the spectrum, the radio spectrum is split into many different

segments, but RFID technology uses only four of these segments, as shown in Figure 2.1.

Figure 2.1 shows multiple frequencies in relation to the entire radio spectrum. Only

frequencies between 125-134kHz, 13.56MHz, 860-960MHz, and 2.45GHz & 5.8GHz from

Low Frequency (LF), High Frequency (HF), Ultra High Frequency (UHF) and Microwave

Frequency (MF) are used respectively.

2.2.3 Interference and Multipath of RF waves

Radio signals are affected in many ways by objects in their path and by the media through

which they travel (Brown et al., 2007). Interference is the interaction of two or more

radio waves resulting in a new wave pattern. When waves generated due to propagation

7


Figure 2.1: RFID Operational Frequencies

effects take a different path than the original wave, it is called Multipath. As radio waves

travel, they interact with objects and the media they encounter. This interaction causes

absorption, diffraction, refraction, reflection, scattering, and free space loss of the wave

(Brown et al., 2007; Sanghera et al., 2007).

Absorption When an RF wave hits a material object, some of its energy will be ab-

sorbed by that object, depending on the frequency of the wave and the material of the

object. Water and objects containing water such as liquid products and metal objects, are

likely to absorb the RF waves. UHF waves, due to their shorter wavelengths, are more at

risk to absorption than LF and HF waves.

Diffraction Diffraction refers to the bending of an EM wave when it comes into contact

with the sharp edges or when it passes through narrow gaps.

Refraction Refraction is the change in direction of a wavefront when it hits the interface

of two different media but it does not return to the medium. Instead, it passes from one

medium into another. Figure 2.2a) illustrates refraction.

Reflection Reflection is the change in direction of a wavefront when it hits the interface

of two different media; and returns into the medium which it hits. When a radio signal is

8

2.3. RFID TECHNOLOGY OVERVIEW

Figure 2.2: An example of: a) Refraction, b) Reflection, and c) Scattering

reflected, some loss of signal normally happened, either through absorption or as a result

of signal passing into the medium. Figure 2.2b) illustrates reflection.

Scattering Scattering occurs when the medium, which the wave travels through, have

smaller dimensions compared to the wavelength. When RF wave is scattered due to rough

surfaces of small objects, it results in the loss of the signal or dispersion of the wave, as

shown in Figure 2.2c).

Free Space Loss If the space through which the RF wave travels is free of all obstructing

material, there will be no absorption, refraction, reflection, diffraction or scattering effects.

However, there will still be some loss in signal strength called free space loss.

2.3 RFID Technology Overview

RFID technology is an automated wireless technology that incorporates the use of the

electromagnetic spectrum to uniquely identify people or objects. There are several meth-

ods of identification but the most common is to store a serial number that identifies a

person or object such as Electronic Product Code (EPC). RFID may only consist of a tag

and a reader but a complete RFID system involves many other technologies, for example,

computer, network, Internet, and software such as middleware and user applications. The

term data streams in this thesis refer to the raw data, which is being communicated and

exchanged between RFID readers and tags. The raw data has no meaningful informa-

tion and needs to be further processed, extracted, integrated or transformed, before being

stored into the database. RFID data streams also have common characteristics, which is

fundamental for RFID data management.

This section explains further on RFID system mechanism, the characteristic of RFID

data, the main commercial applications in RFID, and the RFID technology in supply

chain.

9


2.3.1 RFID System Mechanism

A typical RFID system is divided into two layers: the physical layer or device layer and

Information Technology (IT) layer or application layer (Brown et al., 2007; Bornhovd

et al., 2005).

The physical layer consists of:

• one or more reader antennas;

• one or more readers (Interrogator);

• one or more tags (Transponder); and

• a deployment environment.

The IT layer consists of:

• one or more host computers connected to readers (directly or through a network);

and

• appropriate software such as device drivers, filters, middleware, databases, and user

applications.

Nevertheless in some cases, the middleware is classified as its own separate layer, and

involved data integration and aggregation.

Figure 2.3: An example of how RFID tag, reader, middleware and application operate

Figure 2.3 shows how RFID reader retrieves information from tag and sends that

information back to host computer via middleware. Middleware first needs to convert raw

data retrieved by the reader to a meaningful data, before sending them to an application

layer.

10

2.3. RFID TECHNOLOGY OVERVIEW

2.3.2 Characteristic of RFID data

RFID data share common characteristics, which is fundamental for RFID data manage-

ment. These characteristics are as follows:

Streaming and raw data RFID does not carry much information as it is raw. In order

to transform this raw data into a meaningful data, several level of inference must be done.

Large in volume nature RFID data are generated automatically and accumulated

very fast. Some of this data must be filtered and will require a scalable storage scheme to

ensure efficient queries and updates.

Temporal and dynamic RFID applications dynamically generate observations and the

data carry state changes (Wang and Liu, 2005; Wang et al., 2006, 2010; Liu et al., 2006).

Thus, it is crucial to model such information in an expressive data that is suitable for

application level including tracking and monitoring data.

Implicit and inaccuracy of data When the observation occurs in an RFID system,

a reader observed EPC, EPC value, and the timestamp. These data carry implicit in-

formation, such as changes of states and locations. It is also inaccurate since the real

world deployment is often in 60-70 percent range, which means that 30 percent of data are

discarded (Derakhshan et al., 2007; Jeffery et al., 2005, 2006a,b). Thus, raw observations

data need to be transformed into business logic data. At this stage, erroneous readings

should be handled, such as unreliable reads, duplicate reads, missed reads, and noises.

2.3.3 Main RFID Commercial Applications

There are many applications in modern days that integrate the use of RFID technology

in order to improve their business process. Some of the major benefits that the RFID

system provides are security and authentication, safety, convenience, and process efficiency

(Bhuptani and Moradpour, 2005).

The following describes application areas, which are currently used in RFID technology

(Polniak, 2007; Finkenzeller, 2003; Ahson and Ilyas, 2008; Chawathe et al., 2004; Collins,

2006; Harrop, 2005; Swedberg, 2005; Ferguson, 2006; Chiesa et al., 2002):

• Transportation and Distribution: Fixed Asset, Tracking Aircraft, Vehicles, Rail

Cars, Containers Equipment, Real-Time Location Systems, and Healthcare Manage-

ment.

• Retail and Consumer Packaging: Supply Chain Management, Carton Tracking,

Crate/Pallet Tracking, Item Tracking, and Smart shelves.

11


• Security and Access Control: Child Tracking, Animal Tracking, Airport and Bus

Baggage, Anti-Counterfeiting, Computer Access, Employee Identification, Forgery

Prevention, Branded Replication, Parking Lot, Access Room, Laboratory and Facil-

ity Access, Toll collection, Library System.

• Monitoring and Sensing: Pressure, Temperature, Volume, Weight Special, Facil-

ity Access, Facility Security Access, and Location within Facility Monitoring.

• Point of Sale (POS): Automated Payments, Customer Recognition, Smart Card,

and RFID Security.

Figure 2.4: An example of RFID-enabled Supply Chain System

2.3.4 RFID Technology in Supply Chain

Over the past few years, there has been a great deal of interest in RFID technology,

mainly within the retail industry. Most of the leading supplier claim to provide some level

of RFID systems (Derakhshan et al., 2007). Indeed, RFID will help companies leverage

real-time information about their stock level and help them improve their replenishment

process (Myerson, 2007). For example, the following are some of the benefits from using

RFID technology in supply chain management and retailers:

Inventory shrinking Retailers’ replenishment decisions are based on the inventory

stock level stored in the supply chain, which is assumed to be accurate. However, the

count in the inventory system sometimes does not reflect the correct amount of items in

the actual inventory, due to shrinkage or loss of stock. Moreover, handling this type of

problem is a very costly operation that requires a regular manual stock take (Lee et al.,

2005a). With RFID technology, the cost of a regular stock take can be decreased by

maintaining the most accurate amount of stock stored in the data warehouse.

12

2.4. RFID CORE COMPONENTS

Inventory replenishment Inventory management is a critical task for retail businesses.

Most of the time, items on the store’s shelves are out of stock while there is a lot of stock

available in the business storage. This is because there is no automatic process for detecting

items out of stock and restocking the shelf once it becomes empty. However, with RFID

technology, shelf inventory can be tracked automatically (Lee et al., 2005a).

Visibility of inventory across the supply chain The RFID technology provides a

visibility of inventory throughout the entire supply chain. Figure 2.4 shows an example

of an RFID-enabled supply chain system. Each product is tagged so that items that

move around a business can be monitored from the supplier’s warehouse to the store’s

shelves until the item is checked out at the register. These items tend to move and stay

together through different locations especially in an earlier stage of distribution, as seen in

Figure 2.4 (Gonzalez et al., 2006b,c,a, 2007). Nevertheless, the advantage of bulky items’

movement is that all items from the same company, pallet, and case, will have the same

pattern of encoding (more explanation in Section 2.4.3.3) and will be easier to monitor

and manage as data streams.

2.4 RFID Core Components

RFID involves detecting and identifying a tagged object through the data it transmits.

This requires a tag (transponder), a reader (interrogator) and antennae (coupling devices)

located at each end of the system. The reader is usually connected to a host computer

or other device which will further process the data captured. One key element of RFID

operation is data transfer. This occurs with the connection between a tag and a reader, also

known as coupling, through the antenna on either end (Bhuptani and Moradpour, 2005;

Karmakar, 2010). In this section, we describe the three common hardware components

present in all RFID systems, antenna, reader, and tag.

2.4.1 RFID Antenna

An antenna is a conductive structure that radiates an EM wave when an electrical current

is applied to it; and its electronic component is designed to transmit or receive radio

waves. It converts electrical energy into a radiating field that extends infinitely outward

(Brown et al., 2007; Sweeney, 2005; Karmakar et al., 2008). All RFID systems include

two different types of antennas: the reader antenna and the tag antenna. The antenna

performance characteristic is one of the most critical elements of any RFID installation

because the antenna transfers power and data from the reader to the passive RFID tags

(more explanation in Section 2.4.3.1) and receives the tags’ reply.

13


2.4.1.1 Antenna Footprint (pattern)

The footprints of the reader’s antennas determine the interrogation zone (reader zone)

of a reader. In general, an antenna footprint, also called an antenna pattern, is a three-

dimensional region shaped to look like a balloon projecting out from the front of the

antenna. In the real world, the balloon is distorted by interference patterns of radio waves

reflected from surrounding objects. Within the reader zone, the antenna’s energy is most

effective and a reader can read a tag placed inside this region with the least difficulty.

Figure 2.5a) shows an example of a simple antenna pattern.

Figure 2.5: An example of: a) Simple antenna pattern, and b) Antenna pattern containingprotrusions

In reality, because of the antenna’s characteristics, the footprint of an antenna is never

uniformly shaped like a balloon but almost always contains protrusions. Each protrusion

is surrounded by dead zones, and such dead zones are also called nulls (Lahiri, 2005). A

tag placed in one of the protruded regions will read but not when the tag shifts into the

dead zone. Because of the irregular shape of the antenna footprint, an RFID tag may be

readable or not readable based on tiny changes in location or orientation. Therefore, it is

important to place the tag within the main interrogation zone without depending on the

protruded zone. Some of the read range has to be sacrificed but better read rate will be

provided as a result. Figure 2.5b) shows an example of an antenna with protrusions.

2.4.1.2 Polarisation

The readability of the tag greatly depends on the polarisation of the antenna and the angle

the tag makes with the reader. For a maximum transfer of power, the reader and the tag

antennas should have the same polarisation. For example, if the transmitting antenna

is horizontally polarised and the receiving antenna is vertically polarised (or vice versa),

not much power can be transferred. If the receiving antenna is circularly polarised, it will

receive some radiation regardless of the polarisation of the transmitting antenna. This is

because a circular polarisation has both components of the linear polarisation; horizontal

and vertical.

14


Linearly polarised antennas Linear polarisation is relative to the surface of the earth

where horizontally polarised waves travel parallel to the ground; and vertically polarised

waves travel perpendicular to the ground (Lahiri, 2005). A linearly polarised antenna has

a narrower radiation beam with a longer read range compared to a circularly polarised

antenna. A narrower radiation beam helps a linearly polarised antenna to read tags within

a longer, narrow but well-defined read region, instead of reading tags randomly from its

surroundings. A linearly polarised antenna is sensitive to tag orientation with respect to

its polarisation’s direction. These types of antenna are therefore useful in applications

where the tag orientation is fixed and predictable. Figure 2.6 shows how a tag should be

oriented with respect to a linear antenna for its proper reading.

Figure 2.6: Proper tag orientation for a linearly polarised antenna

Circularly polarised antennas A circularly polarised wave basically spins as it trav-

els. If the wave rotates in right-hand/left-hand manner, the antenna is considered to be

right-hand/left-hand circularly polarised (Brown et al., 2007; Lahiri, 2005). A circularly

polarised antenna has a wider radiation beam and hence reads tags in a wider area com-

pared to a linearly polarised antenna. A circularly polarised antenna is largely unaffected

by tag orientation. In a mixed environment where orientation cannot be controlled, cir-

cular antennas work best. A circularly polarised antenna is preferred for an RFID system

that uses high UHF or microwave frequencies in an operating environment, where there

is a high degree of RF reflectance (due to presence of metals and/or waters). Figure

2.7 shows how a tag should be oriented with respect to a circular antenna for its proper

reading.

2.4.2 RFID Reader

The reader, also referred to as the interrogator, is a device that captures and processes tag

data. Although some readers can also write data onto a tag, the device is still referred to

15


Figure 2.7: Proper tag orientation for a circularly polarised antenna

as a reader or interrogator (Bhuptani and Moradpour, 2005; Finkenzeller, 2003; Karmakar,

2010). The reader is also responsible for interfacing with a host computer.

2.4.2.1 Reader Types

Readers come in multiple formats, which can be separated into three main categories

(Sanghera et al., 2007):

Fixed readers Fixed readers are fixed-position interrogators mounted at specific loca-

tions through which the tagged items are expected to pass, such as conveyors, dock doors,

and retail store checkout points. The advantage of a fixed-mount reader is that the tags

are read automatically, and the disadvantage is the possibly harsh environment that comes

with the location where the reader is mounted.

Handheld readers Handheld readers are mobile interrogators. Therefore, they contain

all the basic elements in one device, including antenna and application software. The

information collected from the tags is stored in the reader and later transferred to a data

processing system, if the application requires it. The advantage of handheld reader is that

a user can bring the reader close to the tagged item and collect the information. The

disadvantage is that the read range is less than that of a fixed reader. Handheld readers

can be used for applications such as tracking and scanning items in medical, office, and

retail environments because they can be easily moved around.

Vehicle-mount readers Vehicle-mount readers are mobile mount interrogators that

can be mounted on a vehicle such as a forklifts, paper trucks, cargo trucks, and pallet

jacks. The advantage of vehicle-mount reader is that its read range is larger than that of

a handheld reader, and can cover more area than fixed-mount reader. The disadvantage is

16


that it might have to work in the vicinity of metallic materials. This could pose a challenge

because metals can reflect the RF signal. In addition, a vehicle-mount reader usually has

a special shape for easier installation on a vehicle, and a rugged design to survive the

vibrations and other environmental conditions.

2.4.2.2 Dense Reader Mode

Dense reader mode or dense interrogator mode allows for operation of multiple readers

located within close proximity of each other, without causing reader interference. Dense

reader mode allows for coordination of readers so that no two readers are transmitting

at the exact same moment using exactly the same frequency, which causes interference

(Brown et al., 2007). To do this, many readers perform frequency hopping and support a

function known as Listen Before Talk (LBT).

LBT is often used with frequency hopping, a technology that forces a reader to change

channels constantly within each frequency. LBT is where readers use an antenna to listen

for the frequency on which the reader is about to transmit. If another reader is communi-

cating in that channel, the reader will automatically switch to the next available channel

and transmit there instead. Another way to avoid failed communication between reader

and tag is by using anti-collision protocol, especially when passive tags are used in UHF

frequency.

2.4.2.3 Authentication and Data Encryption/Decryption

High-security systems also require the reader to authenticate system users. For instance,

Point of Sale (POS) systems, in which money is exchanged and transferred, would be prone

to fraud if precaution were not taken. There are two types of RFID authentications; mutual

symmetrical and derived keys (Hunt et al., 2007). In both of these systems, an RFID tag

provides a key code to the reader, in order to determine if the key is correct and if the tag

is authorised to access the system.

Data Encryption/Decryption is another security measure that must be taken to prevent

external attacks to the system. In the POS example, if user’s key is stolen by a criminal,

that information can be used to make fraudulent purchases. The reader must implements

data encryption and decryption, in order to protect the integrity of data transmitted

wirelessly, and to prevent interception by a third party.

2.4.2.4 Anti-Collision

Most reader operates within UHF band and must have some sort of tag anti-collision

algorithms. This is because in UHF band, tags can be captured faster by reader, which

may cause collision, and no tag would be identified. The various types of tag anti-collision

17


methods can be reduced to two basic types, deterministic and probabilistic, as shown in

Figure 2.8. Tag anti-collision is needed to prevent two or more tags to response to a

reader at the same time (Bhatt and Glover, 2006). These anti-collision techniques will be

explained in detailed in Chapter 3.

Figure 2.8: Various types of anti-collision methods

2.4.3 RFID Tag

RFID tags come in many different designs, shapes, and sizes. A tag is designed for

a particular application depending on the object or material to which the tag is to be

attached. The frequency of operation, functionality, and read range of a tag also varies

(Bhuptani and Moradpour, 2005; Bhatt and Glover, 2006; Sweeney, 2005).

2.4.3.1 Tag Types

Tags may be classified under different categories, depending on how the tags obtain power,

the frequency at which they operate, and the various functionalities implemented on the

tags. RFID tags are mainly categorised into Chipped and Chipless tags. This thesis

focuses on Chipped RFID tags because Chipless tags do not contain a chip or electronic

circuit, and thus store information purely in the electromagnetic materials which comprise

the tag (Preradovic et al., 2009; Balbin and Karmakar, 2009). Since the absence of an

electronic circuit makes it more difficult to store information in a compact area, chipless

RFID tags are generally limited to a data capacity of less than 32 bits, although in some

cases more bits are possible.

Chipped RFID Tag types are separated into three categories known as Passive Tag,

Semi-Passive Tag, and Active Tag.

Passive tag Passive tag (Figure 2.9a) does not have its own power source, and it has

no battery on-board (Lahiri, 2005). The tag obtains power from radio waves received

from the reader. Passive Tags are small and light weight, and their functionalities are

18


Figure 2.9: An example of: a) Passive Tag, b) Semi-passive/Semi-active Tag, and c) ActiveTag

limited due to power source. Due to a lack of enough power, it cannot support an active

transmitter to communicate with the reader. In addition, passive tags do not contribute

to radio noise due to lack of transmitter; and they also have longer life of around 20 years

compared to semi-passive and active tag. The read range of passive tags is around few

inches to 20 feet. In RFID applications, passive RFID tags are often used. Moreover,

passive tags are well suited in applications for which tags are not reusable, because of

their low cost. The tags become part of the object to which they are attached and have

the same life cycle as the object itself.

Semi-passive tag Semi-passive tag (Figure 2.9b) is also called semi-active tag (Brown

et al., 2007). This tag has an on-board battery but similar to a passive tag, it does not

have an active transmitter. It modulates the reflection of the waves from the reader and

requires a reader to send data. Semi-passive tags have a longer read range of more than

100 feet compared to passive tags. Since no transmitter is present, semi-passive tags does

not contribute to radio noise but they can have more memory compared to passive tag, and

can store more data. The extra functionalities of an on-tag battery creates a few problem

such as extra weight, larger size, higher cost, shorter life, and temperature sensitivity. An

integrated battery means the tag dies when the battery dies. The battery life lasts around

2 to 7 years.

Active tag Active tag (Figure 2.9c) has an on-board power source, usually a battery

and an active transmitter (Sanghera et al., 2007). It does not need emitted power or radio

signals from the reader to transmit its data. Its typical read range is 300 to 750 feet.

The read range depends on the battery power and type of transmitter on the tag. An

active tag, similar to a semi-passive tag, may have on-board sensors or external sensors

connected to it. With more processing power, the tag may collect data from the sensors

and locally process the data before broadcasting. Active tags are often used by Real-Time

Location Systems (RTLSs).

19


2.4.3.2 Tag Frequencies

RFID tags are categorised according to the frequency at which they are designed to oper-

ate. Primary frequency ranges are allocated into four categories for use by RFID systems

(previously mentioned in Section 2.2.2).

Low Frequency (LF) Tags within the LF range include frequencies from 30 to 300kHz,

but only 125kHz to 134kHz are commonly used. A typical LF RFID system operates at

125kHz or 134.2kHz, and this range is available all over the world. The LF tags are passive

tags that have no or limited anti-collision capabilities. Therefore, reading multiple tags

simultaneously in the interrogator zone is impossible or very difficult. However, the LF

tags can be easily read while attached to objects containing water, metal, wood, and

liquids because they are not sensitive to radio noise. LF tags are used in access control,

asset tracking, animal identification, automotive control, healthcare, and various point-of-

sale applications. The automotive industry is the largest user of LF tags where LF tag is

embedded inside the ignition key. When that key is inserted into the key hole and tag ID

is correct, the car can be started.

High Frequency (HF) The HF ranges from 3 to 30MHz, while the only typical fre-

quency being used for HF RFID systems is 13.56MHz. This frequency is now available

for RFID applications worldwide. HF tags are passive tags that may have anti-collision

capability which allow reading of multiple tags simultaneously in the interrogator zone.

However, since the read range of many HF tags and readers is small, they usually do not

implement anti-collision. HF tags are ideal choice for applications such as a smart shelf,

credit cards, smart cards, library books, airline baggage, and asset tracking. Due to no

restrictions on the use of HF frequency, HF tags are currently the most widely used tags

around the world.

Ultra High Frequency (UHF) The UHF range includes frequencies from 300 to

1000MHz, but only two frequency ranges, 433MHz and 860-960MHz, are used for UHF

RFID systems. The 433MHz frequency is used for active tags, while the 860-960MHz

range is used for passive tags or semi-passive tags. All the protocols in the UHF range

have some type of anti-collision capability, which allow multiple tags to be read simulta-

neously within the interrogator zone. However, the UHF tags cannot be easily read while

attached to objects containing water or metal because they absorb UHF waves and detune

the tag.

Microwave Frequency (MF) The MF range includes frequencies from 1 to 10GHz,

but only two frequency ranges of around 2.45GHz and 5.8 GHz are used for Microwave

RFID systems. Microwave tags are available as passive, semi-passive, and active types.

20


Japan is the largest user of passive microwave tags. The 2.4GHz frequency range is called

Industry, Scientific, and Medical (ISM) band and is accepted worldwide.

2.4.3.3 Tag Identification Method

There are several methods of identification but the most common is to store a serial number

that uniquely identifies a person or object such as Electronic Product Code (EPC). The

EPC is designed as a universal identifier that provides a unique identity for every physical

object globally. Its structure is defined in the EPCglobal Tag Data Standard (EPCGlobal,

2006, 2005, 2008), which is an open standard, and is freely available. The EPC Class 1

Generation 2 is widely used in the UHF range for communications at 860-960MHz. The

passive RFID tag used within the UHF range is sometime referred to as EPC Gen-2 tag.

An EPC tag contains a 96-bit unique identifier, which is a really big number that will

never be repeated or allocated to anything except that tag. The two primary reasons why

EPC numbers contain only a unique identifier, as opposed to actual information about

the product, are security and cost (Sweeney, 2005).

The most common encoding scheme with 96 bits encoding currently used includes: the

General Identifier (GID-96), the Serialised Global Trade Item Number (SGTIN-96), the

Serialised Shipping Container Code (SSCC-96), the Serialised Global Location Number

(SGLN-96), the Global Returnable Asset Identifier (GRAI-96), the Global Individual Asset

Identifier (GIAI-96), and the DoD Identifier (DoD-96).

In order to manage and monitor the traffic of RFID data effectively, the EPC pattern

is usually used to keep the unique identifier on each of the items arranged within a specific

range (Darcy et al., 2011). The EPC pattern does not represent a single tag encoding,

but rather refers to a set of tag encodings. For instance, the General Identifier (GID-

96) includes three fields in addition to the Header with a total of 96-bits binary value.

25.1545.[3456-3478].[778-795] is a sample of the EPC pattern in decimal, which later will be

encoded to binary and embedded onto tags. Thus, within this sample pattern, the Header

is fixed to 25 and the General Manager Number is 1545, while the Object Class can be

any number between 3456 and 3478, and the Serial Number can be anything between 778

and 795.

Within each EPC, the Uniform Resource Identifier (URI) encoding complements the

EPC Tag Encodings, defined for use within RFID tags and other low-level architectural

components. URIs provide an information for application software to influence EPC in

a way that is independent of any specific tag-level representation. The URI forms are

also provided for pure identities, which contain just the EPC fields which are used to

distinguish one item from another. For instance, for the EPC GID-96, the pure identity

URI representation is as follows:

urn:epc:id:gid:GeneralManagerNumber.ObjectClass.SerialNumber

21


In this representation, the three fields GeneralManagerNumber, ObjectClass, and Seri-

alNumber correspond to the three components of an EPC General Identifier (EPCGlobal,

2008). There are also pure identity URI forms defined for identity types that correspond

to certain encodings. The URI representations corresponding to these identifiers are as

shown in Table 2.1.

Table 2.1: The Uniform Resource Identifier (URI) encoding complements the EPC TagEncodings defined for use within RFID tags and other low-level architectural components

Encoding Scheme Uniform Resource IdentifierGID urn:epc:id:gid:GeneralManagerNumber.ObjectClass.SerialNumber

SGTIN urn:epc:id:sgtin:CompanyPrefix.ItemReference.SerialNumberSSCC urn:epc:id:sscc:CompanyPrefix.SerialReferenceSGLN urn:epc:id:sgln:CompanyPrefix.LocationReference.ExtensionComponentGRAI urn:epc:id:grai:CompanyPrefix.AssetType.SerialNumberGIAI urn:epc:id:giai:CompanyPrefix.IndividualAssetReferenceDoD urn:epc:id:usdod:CAGECodeOrDODAAC.serialNumber

An example encoding of GRAI is demonstrates as follows:

urn:epc:id:grai:0652642.12345.1234

From the above example, the corresponding GRAI is 06526421234581234. Referring

to Table 2.1, the CompanyPrefix, AssetType, and SerialNumber of GIAI are represented

as 0652642, 12345, and 1234 respectively. Some of the major encoding schemes, which are

used and incorporated within our methodology, will be further explain within the thesis.

2.5 RFID Data Management Issues

RFID data management is one of many issues surrounding the deployment of RFID.

Palmer (2004) stated that data should be absorbed closer to the source, which caters

to the need for pre-processed data, so that only relevant and meaningful information is

passed to the application software. This is where the raw data had been collected and

should be filtered before being passed into the applications. Filtering must be done in

this capture layer, and the data captured at this layer is considered to be Dirty data.

This theory is supported in (Derakhshan et al., 2007) where it is described that, in RFID

data management, Dirty data appears in four general forms: unreliable reads, missed

reads, noise, and duplication. After the data has been filtered, it is then turned into more

meaningful data and stored into the databases.

RFID data management is classified into three stages: 1) Data Capturing Process

where Dirty data are being captured by RFID devices; 2) Data Processing and Event

Management where Dirty data are being computed, transformed, aggregated, and inte-

grated into meaningful data; and 3) Data Warehousing and Data Mining where data are

stored into the database for a later use (Melski et al., 2007).

22

2.6. SUMMARY

2.5.1 Data Capturing Process

Data capturing process is considered as the most important stage in RFID system. This is

because any data captured during this stage will be used for further process by the other

two stages. The data capture layer is responsible for coordinating and detecting multiple-

tagged objects and filtering incoming data before sending to the next layer (Derakhshan

et al., 2007). Therefore, any errors that occur in data capturing level will be carried on

toward the rest of the procedure. Such data stream errors are: unreliable reads, missed

reads, noise, and duplication.

2.5.2 Data Processing and Event Management

Simple and complex event detection is one of the primary roles played by data processing

and event management stage. Simple events in RFID applications are those which are

generated during the interactions between readers and tagged objects (Derakhshan et al.,

2007). In order to detect more complex events, we need to filter and correlate massive

number of simple events. To model the process of complex events, we need to define a

language that would we able to filter and correlate the events. Complex event languages

have been discussed in different contexts in order to transform, aggregate, and integrate

Dirty data into meaningful data (Abadi et al., 2004; Wu et al., 2006; Gyllstrom et al.,

2007).

2.5.3 Data Warehousing and Data Mining

The final stage in RFID system management is the data warehousing and data mining.

Data warehousing is defined as a process of centralised data management and retrieval.

Data warehousing represents an ideal vision of maintaining a central warehouse of all

organisational data. All data from the data warehouse must be cleaned before they can

be effectively used by the business application. Data mining, on the other hand, is the

process of analysing data from data warehouse and summarising it into useful information.

This step of data management is also important as some errors from data capturing and

event processing may remains (Darcy et al., 2009, 2010a,b).

2.6 Summary

In this chapter, an overview of some background information such as history of RFID,

RFID basics and technology overview, were presented. Particular attention was given to

the component of RFID systems, which are Antenna, Reader, and Tag. Current appli-

cations that are currently in use, such as RFID technology in supply chain, were also

discussed.

23


Some data management prospective was also identified in this chapter. RFID data are

raw, implicit and inaccurate, and accumulates very fast. In order to manage these data

efficiently and effectively, these data must be cleaned and filtered before storing them

into the database. Therefore, in this research, the focus will be on data capturing level

where RFID data is being collected before any further process. The specific issues and the

current methods for filtering RFID data streams, particularly Anti-Collision techniques,

will be discussed in the next chapter.

24

3RFID Data Streams Management Techniques

In this chapter, we examine different types of data stream filtering techniques, particularly

the anti-collision methods. We survey existing filtering approaches for different types of

RFID data stream errors, and analyse missed reads caused by data collision, which is the

most crucial type of error. We also discuss the anti-collision techniques, and conclude this

chapter with discussion on the research problems and limitation of existing methods.

3.1 Filtering of RFID Data Streams

Due to the low-power and low-cost constraints of RFID passive tags, the reliability of

RFID data capture process has become more challenging in many circumstances (Brusey

et al., 2003). There are several data filtering processes that handled different types of

data stream errors. This section describes and discusses four major data stream errors

including Unreliable reads, Missed reads, Noise, and Duplication.

3.1.1 Unreliable Reads

In an RFID deployment, environmental interference such as metal or water can sometimes

cause unreliable reads. Moving tags (objects) is also a common problem that causes

unreliable reads. For instance, baggage with RFID tags in airports can move too fast on

conveyor belts and is not properly detected by the reader.

25

CHAPTER 3. RFID DATA STREAMS MANAGEMENT TECHNIQUES

Tag deployment affects the RFID data capturing process in many ways and also caused

unreliable reads, as described below:

• Tag Orientation and Location: Tag performance is affected by the orientation of

the tag, relative to the reader’s antenna. The best tag orientation occurs when

the tag orientation and the antenna orientation are parallel to each other. As the

tag is rotated away from parallel position, it collects less power and the tag read

range decreases as the collected power decreases. The location of the tag within the

interrogator zone also affects the tag’s performance (Sweeney, 2005).

• Tag Placement: Placement of the tag on an object affects the tag’s performance

especially for passive tag used in supply chain warehouse. This is because radio

wave will be mostly absorbed if located near liquids and metals. The best place

to attach a passive tag on cases (at case-level tagging) is where the item packaging

provides the most separation from the liquids inside. Various test methods must be

used in order to determine the best tag location for specific product types (Brown

et al., 2007).

• Tag Stacking/shadowing: Tag stacking occurs when several tags are placed close to

each other. For example, item B is placed behind item A. From the perspective of

the reader, tag A that is closer to the antenna, absorbs and reflects most of the radio

energy. Therefore, tag B receives a very weak signal and could be missed by the

reader. To avoid this problem, packaging method should be designed carefully and

the case should be rotated in front of several antennas, installed at various angles to

the case. Tag stacking is also referred to as tag shadowing (Sanghera et al., 2007).

According to Fishkin et al. (2004) and D’Mello et al. (2008), the experiment testings

have shown that some deployment of tags and readers can result in the way reader emits

a low-power radio signal through its antenna to the passive tag. These studies can help

solve some unreliable reads, which is caused by different environment.

Unreliable reads can be solved at the hardware level, where relative parameters can

be set according to specific guidelines. The followings are parameters that affect the way

tags received power from readers:

• Flooring: Metal floor results in reader failure to detect tags.

• Distance between tag and reader: If tags are out of reader’s range (or in a weak

range), the percentage of tags detected by reader are very low (or 0).

• Number of tags on an object and their placement on that object: A certain number

of tags can increase accuracy to the reading but too many tags in reading scope can

result in tag collisions.

26

3.1. FILTERING OF RFID DATA STREAMS

• Number of readers and their deployment topology: Too many readers covering the

same area can result in a lot of duplication.

• Number of nearby tags: Nearby tags may be detected by a reader outside their

range. This resulted in noisy readings.

• Number of objects moved simultaneously: The experiment shows that by using

handheld reader walking passed tags, most tags are detected by the handheld reader

especially those tags facing directly to the handheld reader.

• Tag orientation and rotation: A rotation of 30 and 60 degrees results in all tags being

detected by an RFID reader. However, a 90 degrees rotation of a reader results in

no tags being captured.

3.1.2 Noises

Noises refer to the additional unexpected readings generated. This can be caused by an

RFID tag outside the normal reading scope of a reader being captured for unknown reason

(Bai et al., 2006). Table 3.1 shows that since three tags i.e. TagD, TagF, and TagG, are

below the specific threshold, these tags are classified as noise readings. In addition, TagB

(SGTIN encoding) and TagH (GRAI encoding) are also classified as noise as these tags

have different encoding schemes.

Table 3.1: A sample of noise where * indicates a noise reading. Since the noise thresholdequals to 3 and the tag catch is only for GID encoding, any tag that appears less than threetimes within a specific time frame or does not satisfy tag catch requirement, is classifiedas noise

Tag EPC(CATCH:gid:1000.101.101-115) Count threshold = 3TagA urn:epc:id:gid:1000.101.101 Count1TagA urn:epc:id:gid:1000.101.101 Count2TagA urn:epc:id:gid:1000.101.101 Count3TagB urn:epc:id:sgtin:3000.203.100* Count1TagA urn:epc:id:gid:1000.101.101 Count4TagC urn:epc:id:gid:1000.101.103 Count1TagC urn:epc:id:gid:1000.101.103 Count2TagD urn:epc:id:gid:1000.101.104 Count1TagD urn:epc:id:gid:1000.101.104 Count2*TagC urn:epc:id:gid:1000.101.103 Count3TagE urn:epc:id:gid:1000.101.105 Count1TagE urn:epc:id:gid:1000.101.105 Count2TagE urn:epc:id:gid:1000.101.105 Count3TagF urn:epc:id:gid:1000.101.106 Count1*TagG urn:epc:id:gid:1000.101.107 Count1TagG urn:epc:id:gid:1000.101.107 Count2*TagH urn:epc:id:grai:2000.302.100* Count1

From literature survey, Bai et al. (2006) proposed several algorithms including both

noise removal and duplication elimination. Three sliding window based algorithms for

27


denoising were proposed in the paper. According to the authors, a sliding window is a

window with certain size that moves with time. RFID reading tags will enter the window

and will expired at certain time. The noise readings are readings with count of distinct

tag EPC values below a threshold. The following briefly summarised the three algorithms:

• First algorithm is “Baseline denoising”. For each incoming reading of value R, a full

scan of window size is performed. If R is higher than a threshold, it is classified

as a non-noise reading. The readings of the same key are then output if threshold

condition is satisfied. To ensure a particular reading is never output more than

once, a state-of-output with each reading in the window buffer, is kept and set to

true when it is output once.

• Second algorithm, “Lazy denoising”, is an improved version of the first algorithm.

The output from Baseline denoising algorithm can be out of order. This affects

all further RFID data processing where correct ordering of observations is critical,

such as complex RFID event detections for real-time RFID applications, and RFID

data aggregation. To solve this out-of-order problem, a Lazy denoising algorithm

is proposed using hash table, along with output order preserving. The output from

this algorithm will be delayed until they expire from sliding window, to ensure that

everything will be output in order.

• Third algorithm, “Eager denoising”, is an improved version from Lazy denoising

algorithm and is also implemented with output order preserving. In the second

algorithm, the output was delayed until the window expires. This could be a problem

when the width of window is quite long. This third algorithm can output data earlier

while preserving correct time order. This is because the issue of wrong order occurs

only when a reading has been output, before the change of labeling on some earlier

reading within the window. Therefore, for a non-noise reading that is known as no

other earlier noise reading presented in the sliding window, it can be safely output

without the risk of order problems.

3.1.3 Duplications

As stated by Derakhshan et al. (2007), the Duplication problem is recognised as a seri-

ous issue in RFID and sensor networks because it often cause the reduction in system

robustness. Duplication can happen at two different levels: duplication at reader level and

duplication at data level :

3.1.3.1 Duplication at reader level

Duplication at reader level occurs when there is more than one reader deployed to cover

a specific location. Figure 3.1 shows that readers R1 and R2 cover the same shaded area

28

3.1. FILTERING OF RFID DATA STREAMS

on the left (S1 ); and readers R2 and R3 cover the same shaded area on the right (S2 ).

Figure 3.1: An example of three readers deployment, where R1 and R2 covered S1, andR2 and R3 covered S2

Carbunar et al. (2005) proposed an algorithm called “Redundant Reader Elimination”

(RRE), which is a randomised, decentralised, and localised approximation algorithm for

the RRE problem. This has been done to eliminate duplication at the reader level. The

RRE algorithm has three different steps. Firstly, the algorithm detects the set of RFID

tags placed around the covered area of reader. Secondly, each RFID reader attempts to

write a number of covered tags count onto all of its covered tags. The reader that issued

the highest count for a tag will mark the tag. Finally, each reader queries all its covered

tags and determines the one it has marked. A reader that has not marked any of its

covered tags is declared as a duplicate reader.

3.1.3.2 Duplication at data level

Duplication at data level occurs as data streams. The RFID data can be captured very

fast and usually less meaningful without transformation. Some of these data had been

captured more than once, so it is possible to identify and eliminate them before passing

it to the application. Table 3.2 shows that TagE is captured twice and TagF is captured

three times. Since these duplicated reads are close to each other, it is assumed that this

may happen because the tag remained in the scope of a reader for a long time and is read

by the same reader multiple times.

Two algorithms for duplication removal at data level called “Baseline merge” and

“Hash merge” were proposed by Bai et al. (2006). Baseline merge eliminates duplication

by maintaining a timestamp to indicate the last time a reading, with the same key as the

incoming reading, appears. Hash merge uses hash table to keep the last appearance times-

tamp for each distinct key value. For each incoming reading, its timestamp is compared

with the corresponding entry for this key in the hash table. The reading is determined to

be a new tag reading if the key does not appear in the table, or the time distance is larger

than threshold. Furthermore, Pupunwiwat and Stantic (2007), also proposed a “Location

Filtering and Duplication Elimination” technique to eliminate any data duplication, and

at the same time located where the data has been captured.

29


Table 3.2: A sample of data duplication, where TagE is captured twice and TagF iscaptured three times

Tag EPC(CATCH:gid:1000.101.101-115) Count = 1TagA urn:epc:id:gid:1000.101.101 Count1tagB urn:epc:id:gid:1000.101.102 Count1TagC urn:epc:id:gid:1000.101.103 Count1TagD urn:epc:id:gid:1000.101.104 Count1TagE urn:epc:id:gid:1000.101.105 Count1TagE urn:epc:id:gid:1000.101.105 Count2*TagF urn:epc:id:gid:1000.101.106 Count1TagF urn:epc:id:gid:1000.101.106 Count2TagF urn:epc:id:gid:1000.101.106 Count3*TagG urn:epc:id:gid:1000.101.107 Count1TagH urn:epc:id:gid:1000.101.108 Count1TagI urn:epc:id:gid:1000.101.109 Count1

3.1.4 Missed Reads

Missed Reads are very common in RFID applications and often happened in a situation

of low-cost and low power hardware, which leads to a frequently dropped readings referred

to in other work (Derakhshan et al., 2007). Another source of missed reads is when

multiple tags are detected by a reader and Radio Frequency (RF) collisions occur, causing

RF signals to interfere with each other and preventing the reader from identifying any

tags (Bai et al., 2006). Dropped reading can be easily filtered using Smoothing techniques,

where missing data from specific time can be filled (Jeffery et al., 2005, 2006a,b). However,

preventing data resulting from RF collisions can be hard. In order to solve this problem,

anti-collision is usually performed at the edge, to prevent two or more tags from responding

to a reader at the same time. Tag anti-collision protocols or sometimes referred to as

Multi-Access, can be classified into probabilistic and deterministic methods, which will be

explained further in Section 3.2. Table 3.3 shows an example of missed read where at time

500msec, 800msec and 1000msec, a reading of TagA is missing.

Table 3.3: A sample of Missed reads where at time 500msec, 800msec and 1000msec,readings of TagA are missing

Time (msec) Tag EPC(CATCH:gid:1000.101.101-115)100 TagA urn:epc:id:gid:1000.101.101200 TagA urn:epc:id:gid:1000.101.101300 TagA urn:epc:id:gid:1000.101.101400 TagA urn:epc:id:gid:1000.101.101500 - -600 TagA urn:epc:id:gid:1000.101.101700 TagA urn:epc:id:gid:1000.101.101800 - -900 TagA urn:epc:id:gid:1000.101.1011000 - -

As mentioned earlier, Smoothing technique can be used to handle missed reads.

Smoothing technique is part of a proposed technique called “Extensible Sensor stream

30

3.2. COLLISION HANDLING IN RFID DATA STREAMS

Processing” (ESP), which focuses on cleaning data at the edge of the network. It allows

raw data to be cleaned by processing multiple data streams, and exploiting the temporal

aspect of data to produce a single improved output stream that can be used directly by

applications. In addition, a duplication elimination method called Arbitrate stage of ESP

is proposed by the authors.

According to the surveys (Jeffery et al., 2005, 2006a,b), ESP has five different stages

for data filtering:

• The Point stage filter individual values e.g. RFID tags or obvious Missed reads;

• At Smoothing stage, ESP interpolates for lost reading within a temporal granule.

ESP runs this query over each reader data stream. The query begins by breaking

the streams into smaller slices that correspond with the size of granule. Through

these slices window operation, smooth filled in Dropped readings for any tags, that

are seen at least once in slice time period;

• The Merge stage corrects the Missed reads and removes outliers spatially;

• The Arbitrate stage deals with conflicts, such as duplication;

• The Virtualise stage combines readings from different types of devices together.

Floerkemeier and Lampe (2004) and Floerkemeier and Lampe (2005) have identified a

solution to reduce a tag collision that causes a missed reads, using a playing card scenario.

Based on a “Framed-ALOHA” technique, the authors used a different layout of playing

card to determine the tag captured by reader within a set time period. The result indi-

cated that stacked card has the worst tag recognition rate since it has the most collision

when stacked together. To reduce the missed reads that arise from the tag collisions, the

bandwidth of RFID technology should be increased. Since the 900 MHz in UHF band

offers significantly more bandwidth in the communication from the reader to the tag than

the regulations on the 13.56 MHz in HF band, an RFID system operating in the UHF

band can detect tags much faster. Nevertheless, tag collision issues remain a challenging

topic in current research.

3.2 Collision Handling in RFID Data Streams

RFID collision handling is one of the most heavily researched topics because it is a very

important step to determine a quality of captured data. The better quality of data at the

earlier stage of data processing means less complex algorithms are needed for RFID event

process and database management. This section explains the type of each collision, clas-

sification of Multi-Access, taxonomy of RFID tag anti-collision protocols, and literature

surveys on existing deterministic and probabilistic anti-collision methods.

31


3.2.1 RFID Collision Types

Simultaneous transmissions in RFID systems lead to collisions as the readers and tags

typically operate on the same channel. Three types of collisions are possible: Reader-

Reader collision, Reader-Tag collision, and Tag-Tag collision.

Figure 3.2: Collision Problems in RFID System: a) Reader-Reader Collision, b) Reader-Tag Collision, and c) Tag-Tag Collision

3.2.1.1 Reader Collisions

There are two types of Reader collision: 1) Reader-to-Reader, and 2) Reader-to-Tag (Leong

et al., 2005; Jain and Das, 2006).

Reader-to-Reader Collisions Interference occurs when one reader transmits a signal

that interferes with the operation of another reader, and prevents the second reader from

communicating with tags in its interrogation zone (Jain and Das, 2006). Reader-to-reader

collision can be easily avoided by determining the appropriate reader’s deployment that

prevents direct signal interference between two or more readers. Figure 3.2a) shows an

example of Reader-to-Reader collision.

Reader-to-Tag Collisions Interference occurs when one tag is simultaneously located

in the interrogation zone of two or more readers, where more than one reader attempts to

communicate with that tag at the same time (Jain and Das, 2006). Figure 3.2b) shows

an example of Reader-to-Tag collision.

The classification and solution of Reader collisions problems are illustrated in Figure

3.3.

32

3.2. COLLISION HANDLING IN RFID DATA STREAMS

Figure 3.3: Taxonomy of RFID Readers anti-collision protocols

3.2.1.2 Tag Collisions

Tag collision in RFID systems, sometimes known as Multi-Access, happens when multiple

tags are energised by the RFID reader simultaneously, and reflect their respective signals

back to the reader at the same time. This problem is often seen whenever a large volume

of tags must be read together in the same reader zone. The reader is unable to differentiate

these signals. Figure 3.2c) shows an example of Tag-to-Tag collision.

3.2.2 Division Classification for Multi-Access

Tag collisions or Multi-Access problem is more complex than those within reader collision

categories. There are several techniques in the literature (Shih et al., 2006; Tang and

He, 2007; Liu, 2010) that explain and differentiate between each type of Multi-Access

Divisions. These divisions are summarised below:

SDMA (Space Division Multiple Access): The term SDMA relates to techniques

that reuse a certain resource, such as channel capacity in spatially separated areas (Shih

et al., 2006; Tang and He, 2007). One method is to reduce the range of a single reader,

but to remain the capability of certain coverage area by grouping together a large number

of readers to form an array. As a result, the channel capacity of attaching readers is made

available simultaneously. A disadvantage of the SDMA technique is high implementa-

tion cost of the complicated reader’s deployment. The use of this type of anti-collision

procedure is therefore restricted to a few specialised applications.

FDMA (Frequency Division Multiple Access): The term FDMA relates to tech-

niques in which several transmission channels on various carrier frequencies are simulta-

neously available to the communication members (Shih et al., 2006; Tang and He, 2007).

In RFID systems, this can be achieved using tags with a freely adjustable transmission

frequency. One disadvantage of the FDMA procedure is the high cost of the readers,

33


since a specific receiver must be provided for every reception channel. This anti-collision

procedure also remains limited to a few specialised applications.

CDMA (Code Division Multiple Access): There are actually a number of different

subtypes to CDMA depending on how the spreading is done. The common factor is that

CDMA uses spread spectrum modulation techniques based on pseudo random codes, to

spread the data over the entire spectrum. Even though CDMA would be ideal in many

ways, the disadvantage is that it adds a high number of complexity and would be too

computationally intense for RFID tags (Shih et al., 2006; Tang and He, 2007; Dabas

et al., 2009).

TDMA (Time Division Multiple Access): The term TDMA relates to techniques

in which the entire available channel capacity is divided chronologically between the par-

ticipants (Shih et al., 2006; Tang and He, 2007). In RFID systems, TDMA procedures are

the largest group of anti-collision procedures. Tag-driven and reader-driven procedures

have been differentiated as follows:

• Tag-driven: Tag-driven procedures function asynchronously because the reader

does not control the data transfer. For example, in the Pure ALOHA procedure,

a tag begins transmitting as soon as it is ready and has data to send. Tag-driven

procedures are naturally very slow and inflexible.

• Reader-driven: Most applications use procedures that are controlled by the reader

as the master. These procedures can be considered as synchronous, since all tags

are controlled and checked by the reader simultaneously. An individual tag is first

selected from a large group of tags in the interrogation zone; and then communication

takes place between the selected tag and the reader. Examples of algorithms using

reader-driven procedure are the deterministic algorithm, such as Query Tree; and

the probabilistic algorithm, such as Framed-Slotted ALOHA.

3.2.3 Taxonomy of RFID Tag Anti-Collision Protocols

The various types of anti-collision methods for multi-access/tag collision can be reduced

to two basic types: probabilistic method and deterministic method (Klair et al., 2007;

Choi and Lee, 2007; Bang et al., 2009; Alotaibi et al., 2009; Li et al., 2009; Klair et al.,

2010; Zhu and Yum, 2011).

In probabilistic methods, tags respond at randomly generated times. If a collision

occurs, colliding tags will have to identify themselves again after waiting for a random

period of time. This technique is faster than deterministic but suffers from tag starvation

problem where not all tags can be identified due to the random nature of chosen time.

34

3.3. DETERMINISTIC ANTI-COLLISION PROTOCOLS

The deterministic method begins an identification process by issuing a prefix until it

gets matching tags. Then it continues to ask for additional prefixes until all tags within

the region are found. This method is slow but leads to fewer collisions and have high

successful identification rate.

There are also some hybrid anti-collision protocols that combine the advantages of

tree-based and ALOHA-based approaches. From literature, it is clear that most hybrid

protocols combine the Query Tree protocol with ALOHA variant (Klair et al., 2010).

Figure 3.4 shows the classification of tag anti-collision protocols implemented in

TDMA, including probabilistic and deterministic methods. Deterministic method is also

divided into Memory and Memoryless protocols; where Memory protocols need

additional memory other than tags’ ID, while Memoryless only need an ID of tag for the

whole identification process. The most advanced and efficient probabilistic anti-collision

is the Framed-Slotted ALOHA approach, while the memoryless Query Tree is the

simplest and most robust technique. The literature survey on both deterministic and

probabilistic anti-collisions are explained in detail in the next two sections.

Figure 3.4: Taxonomy of RFID Tags anti-collision protocols

3.3 Deterministic Anti-Collision Protocols

Deterministic methods can be classified into a Memory tree-based algorithm and a Mem-

oryless tree-based algorithm. In the Memory algorithm, which can be grouped into “Tree

Splitting”, “Binary Search”, and “Bit Arbitration”, the reader’s inquiries and the re-

sponses of the tags are stored and managed in the tag memory. This results in an equip-

ment cost increase especially for RFID tags. In contrast, in the Memoryless algorithm,

the responses of the tags are not determined by the reader’s previous inquiries. The tags’

responses are determined only by the present reader’s inquiries so that the cost for the

tags can be minimised. “Query Tree” is classified as Memoryless algorithm.

35


Depending on the number of tags that respond to the interrogator, there are three

cycles of communication between tag and reader in deterministic approaches.

• Collision cycle: Collision cycle occurs when the number of tags that respond to

the reader is more than one. The reader cannot identify the ID of tags.

• Idle cycle: Idle cycle occurs when there is no response from any tag to the reader.

This type of cycle is unnecessary and should be minimised.

• Successful cycle: Successful cycle happens when exactly one tag responds to the

reader and the reader can identify the ID of that tag.

The rest of this section discusses each type of tree-based anti-collision algorithm and

their benefits and drawbacks.

3.3.1 Binary Search

“Binary Search” (BS) algorithm (Finkenzeller, 2003) involves the reader transmitting a

string of EPC to tags, which the tag then compare against its ID. Those tags respond, with

ID equal to or lower than the requested string. The reader then monitors tags’ responses

bit by bit using Manchester coding, where the value of bit is defined by the change in level

(negative or positive transition) within a bit window. A logic 0 is coded by a positive

transition, while a logic 1 is coded by a negative transition. The no transition state is

not permissible during data transmission and is recognised as an error. Once a collision

occurs in BS, the reader splits tags into subsets, based on collided bits.

The enhanced version of the BS protocol is called the “Dynamic Binary Search Algo-

rithm” (DBSA) (Finkenzeller, 2003). In DBSA, the reader and tags do not use the entire

length of EPC and tags ID during the identification process. For example, if a reader

receives the response 01X, tags only need to transmit the remaining part of their ID since

the reader has already identified the prefix 01. This enhancement effectively reduces the

amount of data sent by the reader to tags.

Additionally, there are several improved and enhanced BS algorithms (Yu et al., 2005;

Liu et al., 2005; Chen and Liao, 2010), which introduce higher complexity in implemen-

tation. Nevertheless, all types of BS algorithms require extra memories beside the EPC

data itself, especially the most enhanced version, which requires additional memory to

store information on current stage of reading process.

3.3.2 Bit Arbitration

“Bit Arbitration” (BA) algorithms are memory-based anti-collision and are less robust

than those within memoryless category. The key feature of BA algorithms is that bit

36


replies are synchronised, meaning that multiple tags’ responses of the same bit value will

result in no collision. A collision is observed only if two tags respond with different bit

values. Moreover, the reader has to specify the bit position it wants to read. There are

several algorithms in this category including “ID Binary Tree Stack”, “Bit-by-bit Binary

Tree”, “Modified Bit-by-Bit Binary Tree”, and “Enhanced Bit-by-Bit Binary Tree” (Klair

et al., 2010).

In the “ID Binary Tree Stack” (ID-BTS) (Feng et al., 2006), the reader uses a stack

to store tags position on the tree, while a tag has a counter to record the depth of the

reader’s stack. Based on this counter value, a tag determines whether it is in the transmit

or wait state. In other words, a counter value of zero moves a tag into the transmit state;

otherwise, the tag enters the wait state. Once a tag is identified, it enters the sleep state.

This technique needs a highly preserved memory in the reader, due to the heavy use of

stack.

Jacomet et al. (1999) presented a “Bit-by-Bit Binary Tree” (BBT) arbitration method,

where a separate channel is used for binary 0 and 1. When requested, each tag transmits

the specified bit in one of these channels. If the reader receives a different response from

both channels, it sends a control bit silencing the subset of tags that replied with 0 or 1.

On the other hand, if the reader receives a response of bit in only one of the two channels,

that bit is then successfully identified. Similar to ID-BTS, the reader has a stack and each

tag has a counter to store its tree position.

Choi et al. (2004) proposed the “Modified Bit-by-Bit Binary Tree” (MBBT), which

operates in a similar manner to the BBT algorithm. The key difference is that MBBT does

not use multiple time-slots to receive binary 0s and 1s. Moreover, Choi et al. (2004) also

proposed an “Enhanced Bit-by-Bit Binary Tree” (EBBT). In EBBT, a reader first requests

tags to respond with their complete ID. The assumption here is that tags’ responses are

synchronised. From these responses, the reader identifies collided and non-collided ID bits.

The reader then uses MBBT to identify the collided bits.

3.3.3 Tree Splitting

“Tree Splitting” (TS) protocols operate by splitting responding tags into multiple subsets,

using a random number generator. In this category of anti-collision, the reader needs

less preserving memory than those within Binary Search and Bit Arbitration categories

because TS only needs to store information of random binary numbers. We present two

algorithms in this category: “Binary Tree Splitting” and “Adaptive Binary Splitting”.

3.3.3.1 Binary Tree Splitting

The “Binary Tree Splitting” (BTS) uses random binary numbers generated for the splitting

procedure (Myung and Lee, 2006a). The tag has a counter initialised to 0 at the beginning

37


of the process. The tag transmits ID when the counter value is 0. The reader transmits a

response to inform tags of the event of tag collision. The tag randomly generates a binary

number when its transmission causes collision. By adding the selected binary number to

the counter, a set is split into two subsets, ‘0’ or ‘1’ as shown in Figure 3.5.

Figure 3.5: Binary Tree Memory based anti-collision protocol

3.3.3.2 Adaptive Binary Splitting

Tag identification in BTS protocols starts from one tag set including all tags, which cause

more tag collisions for splitting tag sets. The colliding tag needs to re-transmit ID when-

ever tag collision occurs. The “Adaptive Binary Splitting” (ABS) uses information on the

last frame of the tree and makes a new tag identification start from multiple tags sets.

Hence, the reader can recognise tags with less collision. ABS begins tag identification

from only readable cycles of the last frame and uses random numbers for splitting tag

sets. This technique is an improvement on the BTS protocol. However, it requires extra

memory to store information from previous frame. ABS requires tags to support both the

transmission and reception at the same time.

3.3.4 Query Tree

In TS variants, tags require a random number generator and a counter to track their

tree position, thus making them costly and computationally complex. “Query Tree” (QT)

algorithms overcome these problems by storing tree construction information at the reader,

and tags only need to have a prefix matching circuit. Numerous variants of query tree

algorithms exist. Among all tree protocols, QT protocols promise the simplest tag design

(Klair et al., 2010).

For the tree-based anti-collision, we focus on QT-based protocols because it is the

most acceptable and is an effective anti-collision technique for passive UHF tags (Klair

et al., 2010). There are several improved anti-collision methods based on QT, such as an

“Adaptive Query Splitting” (AQS) proposed by Myung and Lee (2006b), and a “Hybrid

38


Query Tree” (HQT) proposed by Ryu et al. (2007). The AQS requires tags to support

both the transmission and reception at the same time, thereby making it difficult to apply

to low-cost passive RFID systems. On the other hand, the HQT managed to reduce

collision cycles but at the same time introduce too many idle cycles. Accordingly, the QT

Algorithm, which is currently adopted as the anti-collision protocol in EPC Class 1, may

be limited to the tree based anti-collision protocol that can be implemented effectively

(Choi et al., 2008).

The QT (Law et al., 2000) is a data structure for representing prefixes that is sent by

the RFID reader. The QT algorithm consists of loops, and in each loop, the reader issues a

query with specific prefixes, and the matching tags respond with their information. If only

one tag replies, the reader successfully recognises the tag. If more than one tag tries to

respond to reader’s query, tag collision occurs and the reader cannot get any information

about the tags. The reader, however, can recognise the existence of tags to have ID that

matches the query. To further identify collided tags, the QT algorithm tries to query with

1-bit longer prefixes in next round of identification. By extending the prefixes, the reader

can recognise all the tags.

Figure 3.6: Query Tree Memoryless based anti-collision protocol

Figure 3.6 displays an example of a QT procedure. An identification process starts

at Level one of tree, where QT uses tag IDs to split a tag set. It can be seen that Tag

1010 is successfully identified in the first round because from all three tags, only Tag 1010

has ‘1’ for the first bit of string. In the second round of identification, idle cycle was

created, as there was no tag starting with ‘00’ for the first two bits. In the third round of

identification, the other two tags, Tag 0100 and Tag 0111, are successfully identified.

3.3.4.1 Adaptive Query Splitting

The “Adaptive Query Splitting” (AQS) uses information on the last frame of the tree

for tag identification, so that the reader can recognise tags with less collision. The AQS

recognises tags with query that is sent by a reader, which includes a bit string. The basic

idea of AQS is based on QT where tag responds with its ID when its first bits of ID are

39


equal to the bit string of the query. The reader has queued Q, which maintains bit strings

for queries. At the beginning of the frame, Q is initialised with queries of all the leaf

nodes in the tree of the last frame. AQS keeps information that is acquired during the

last identification process, in order to shorten the collision period. This technique also

requires tags to support both the transmission and reception at the same time as in ABS.

In addition, according to Bhatt and Glover (2006), the adaptive splitting protocol is only

compatible with EPC class 0 and class 1 generation 1; and is more complex than basic

BTS and QT.

Figure 3.7 shows that ABS (Refer to Tree Splitting Section) and AQS start the tree

search from the leaf nodes of the tree from the last frame.

Figure 3.7: The starting point of tag identification in tree-based protocols

3.3.4.2 Hybrid Query Tree

“Hybrid Query Tree” (HQT) utilises a 4-ary query tree instead of a binary query tree

(Ryu et al., 2007). Figure 3.8 shows an example of identification process between QT (a)

and HQT (b). This technique increases too many idle cycles despite reducing collision

cycles, while extra memory needed also increases, as an identification process gets longer.

This is because each query increases the prefixes by 2-bits instead of 1-bit.

Table 3.4 compares operations of the QT protocol and HQT protocol from Figure 3.8

sample. As we can see, HQT reduces the number of query commands as well as the number

of collisions. However, the number of idle cycles had been increased as a side-effect.

There is a basic Idle cycles elimination (slotted back-off tag response mechanism) for

HQT, but this requires more time and memory. The extended version of HQT also requires

extra memory, since it mimics the AQS for the last identification information to be kept.

Nevertheless, HQT is better than QT in reducing collision between tags, especially at

higher number of tags.

40


Figure 3.8: Tree-based protocols: a) Query tree protocol, b) 4-ary tree protocol

Table 3.4: Identification process of Query Tree versus Hybrid Query TreeQuery Tree Protocols

Step Query string Query result Query queue1 0 collision 1,00,012 1 collision 00,01,10,113 00 successful 01,10,114 01 successful 10,115 10 collision 11,100,1016 11 idle 100,1017 100 successful 1018 101 successful empty

Hybrid Query Tree ProtocolsStep Query string Query result Query queue

1 empty string collision 00,01,102 00 successful 01,103 01 successful 104 10 collision 1000,1001,10105 1000 successful 1001,10106 1001 idle 10107 1010 successful empty

3.3.4.3 Other Query Tree-Based Algorithms

There are other improved version of QT, which enhances the performance but increases

implementation cost, due to the more complex execution algorithms. These techniques

include the “Improved QT” (ImpQT) algorithm (Zhou et al., 2004), the “QT-based Reser-

vation” (QTR) algorithm (Choi et al., 2007), and the “Intelligent Query Tree” (IntQT)

(Bhandari et al., 2006).

For the deterministic anti-collision approaches, it is preferred that the algorithms are

simple, since the adoption of the tree-based techniques are in the older RFID system. The

recent technology uses ALOHA-based anti-collision algorithms rather than the tree-based.

From the observation and literature survey, we discover that for tree-based approaches,

41


Figure 3.9: A sample procedure of Frame-slotted ALOHA

the number of identification cycles, the total memory bits required, and the similarity of

IDs, mostly affects the delay of tags’ identification. Therefore, we make the assumption

that by taking advantage of EPC pattern and bulky movement of items (see Chapter 2),

the identification ability of the reader can be improved without the need for complex

anti-collision algorithm.

3.4 Probabilistic Anti-Collision Protocols

In a probabilistic approach, tags respond to readers at randomly generated times. If a col-

lision occurs, colliding tags will have to identify themselves again after waiting a random

period of time (Choi and Lee, 2007; Li et al., 2009; Bang et al., 2009; Klair et al., 2010).

When we mentioned the probabilistic anti-collision approach in RFID, we usually refer to

the ALOHA-based approach, which is the most widely used type of anti-collision. “Slotted

ALOHA” (Quan et al., 2006), which initiates discrete time-slots for tags to be identified by

reader at the specific time, was first employed as an anti-collision method in an early days

of RFID technology. The principle of Slotted ALOHA techniques is based on the “Pure

ALOHA” introduced in early 1970s (Abramson, 1970), where each tag is identified ran-

domly. To improve the performance and throughput rate, different anti-collision schemes

were suggested in the past literature. “Framed-Slotted ALOHA” technique is the most

improved ALOHA-based technique currently applied in many applications. The three

most accepted Framed-Slotted ALOHA techniques are “Basic Framed-Slotted ALOHA”,

“Dynamic Framed-Slotted ALOHA”, and “Enhanced Dynamic Framed-Slotted ALOHA”.

Several researchers (Wang et al., 2007; Lee et al., 2005b, 2008a; Cho et al., 2007) have also

attempted to improve the throughput rates by implementing a more accurate Frame-size

Estimation algorithms.

Figure 3.9 shows an example of Frame-slotted ALOHA anti-collision protocols. Each

frame is formed of specific number of slots that is used for communication between the

readers and the tags. Any slot that has more than one tags responding to it is classified

as a collision slot, while any slot that has exactly one tag responding to it is a successful

slot. Empty slot occurs when no tag respond within that specific time slot. Figure 3.9

42

3.4. PROBABILISTIC ANTI-COLLISION PROTOCOLS

shows that Slot 1 and 2 of Frame one and, Slot 5 of Frame two, are collision slots; Slot

3 of Frame one, Slot 4 of Frame two, and Slot 6 of Frame three, are successful slots; and

Slot 7 of Frame three is an empty slot.

The rest of this section describes each type of ALOHA-based anti-collision algorithms

and their benefits and limitations.

3.4.1 BFSA Method

The “Basic Framed-Slotted ALOHA” (BFSA) is the most basic ALOHA-based algorithms

that use a fixed frame-size throughout the identification round. The reader offers infor-

mation to the tags, including the frame-size specification and the random number selected

by each slot within the frame. Each tag selects a slot using the random number and then

sends its ID back to the reader (Ding and Liu, 2009; Lee and Lee, 2006; Lee et al., 2008b).

Since the frame-size of the BFSA is fixed, its implementation is simplistic. However, the

system’s efficiency drops significantly in the event of there being too large or too small

tag counts. For instance, no tag may be identified in a read cycle if there are too many

tags within the interrogation zone. On the other hand, under small tag counts where

large frame-size is used, lots of empty slots are produced resulting in decreased system

efficiency.

3.4.2 DFSA Method

The “Dynamic Framed-Slotted ALOHA” (DFSA) overcomes the problems associated with

BFSA, by dynamically changing the frame-size according to estimated number of Backlog,

which is a number of tags that have not been read. In DFSA, each tag in an interrogation

zone selects one of the given N slots to transmit its identifier; and all tags will be recognised

after a few frames. Each frame is formed of specific number of slots that is used for

communication between the readers and the tags. To determine the frame-size, it gathers

and uses information such as number of successful slots, empty slots, and collision slots

from previous round, to predict the appropriate frame-size for the next identification

round (Ding and Liu, 2009; Lee and Lee, 2006; Devarapalli et al., 2007; Fan et al., 2008a).

DFSA can identify the tag efficiently because the reader adjusts the frame-size according

to the estimated number of tags. However, the frame-size change alone cannot sufficiently

reduce the tag collision when there are a number of tags because it cannot increase the

frame-size indefinitely. DFSA has various versions depending on different tag estimation

methods used. There have been several researches to improve the accuracy of frame-size

by implementing a frame-size estimation techniques (Lee et al., 2005b, 2008a; Cho et al.,

2007; Fan et al., 2008b). According to the DFSA protocol, the reader picks tag within an

interrogation zone by the command “Select”, then issues “Query”, which contains a ‘Q’

parameter to specify the frame-size (frame-size F = 2Q - 1). Each selected tag will pick

a random number between 0 to 2Q - 1 and put it into its slot counter. The tag, which

43


picks zero as its slot number, will respond and backscatter its EPC to reader. Then, reader

issues “QueryRep” or “QueryAdjust” command to initiate another slot (Wang et al., 2007;

Zhu and Yum, 2009).

Similar to the Tree-based anti-collision, there are three kinds of slot in ALOHA-based

anti-collision, as shown in Figure 3.10: 1) Empty slot where there is no tag reply; 2)

Successful slot where there is only one tag reply; and 3) Collision slot where there is more

than one tag reply. The term initial Q refers to the first ‘Q’ or frame-size, which applies

to a specific identification cycle. In Figure 3.10a), the reader first initiates a “Query” and

broadcasts the signal to nearby tags. Since there is no tag that picks zero as its slot counter,

the slot is counted as an empty slot. Figure 3.10b) shows that, after the first “Query” was

sent, each tag deducted its slot counter by one. The reader then sends “QueryRep” to

tags in close proximity; and any tag that has zero as its slot counter replies. When there

is only one tag that responds, a successful slot occurs and the tag replies to the reader

with its RN16. Figure 3.10c) demonstrates that when two tags respond to the reader at

the same time, a collision slot occurs and in this case, no information is transmitted.

Figure 3.10: Empty Slot, Successful Slot, and Collision Slot in EPC Class 1 Generation 2Protocol

44


3.4.3 EDFSA Method

The DFSA algorithms change the frame-size to increase the performance efficiency of the

tag identification. However, as the number of tags becomes larger than the frame-size, the

probability of collision increases rapidly. If the number of unread tags can be estimated

accurately, frame-size can be determined to maximise the system efficiency or minimise the

tag collision probability. For instance, when the number of tags is large, the probability of

tag collision can be reduced by increasing the frame-size. However, the frame-size cannot

be increased indefinitely. When the number of unread tags is too large to achieve high

system efficiency, the number of responding tags somehow must be restricted so that the

optimal number of tags responds to the given frame-size (Lee et al., 2005b; Lee and Lee,

2006).

The “Enhanced Dynamic Framed-Slotted ALOHA” (EDFSA) first estimates the num-

ber of unread tags. If the number of tags within the interrogation zone is larger than the

maximum frame-size, the EDFSA algorithm splits the number of Backlog into number of

groups and allows only one group of tags to respond. When the reader limits the number

of responding tags, it transmits the number of tag sets and a random number to the tags,

when it issues the query. Only the tag that picks zero as its slot counter responds to the

request. If the number of estimated Backlog is below the threshold, the reader adjusts the

frame-size without grouping the unread tags. After each read cycle, the reader estimates

the number of unread tags and adjusts its frame-size. This procedure repeats until all the

tags are read (Lee et al., 2005b; Lee and Lee, 2006).

Table 3.5 shows the derived rule for tag grouping of EDFSA method. The table

demonstrates that if the number of estimated unread tags is equal to or less than 354

tags, the EDFSA algorithm will not split tag into group. However, according to the rule,

if there are more than 354 tags remaining in the interrogation zone, the EDFSA algorithm

will split unread tags into groups. For instance, if there are 1245 estimated remaining

tags, the EDFSA algorithm will divide tag into four groups (refer to the rule in Table 3.5).

The problem with EDFSA method is that it assumes that 256 is the optimal frame-size

and splits tags into group by using the power of two (2,4,8...). This results in decreased

system efficiency when the number of tags is just above the threshold and the number of

group doubled.

3.4.4 Other ALOHA-Based Methods

There has been a number of methodologies proposed to improve the performance efficiency

of ALOHA-based anti-collision methods. This includes partitioning algorithms (Shin and

Kim, 2007; Kim, 2008), which have claimed to have had higher efficiency than the EDFSA

approach, but lacks signaling robustness. Despite the wide array of approaches, only the

BFSA, DFSA and EDFSA methods (Klair et al., 2010; Cheng and Jin, 2007) are com-

45


Table 3.5: EDFSA Rule - The number of unread tags, optimal frame-size, and number ofgroup

The number of unread tags Frame-Size Group.... .... ....

708 to 1416 256 4355 to 707 256 2177 to 354 256 182 to 176 128 141 to 81 64 120 to 40 32 112 to 19 16 16 to 11 8 1

.... .... ....

monly used for comparative analysis in past literature. Additionally, Backlog estimation

approaches have also been a popular research topic in this domain.

3.4.5 Backlog Estimation Techniques

In order to predict accurate number of unread tags and to determine the new frame-

size for the next identification round, BFSA, DFSA, and EDFSA algorithms gather and

use information such as number of successful slots, empty slots, and collision slots from

previous round. There have been several other methods mentioned in literature related

to Backlog estimation, including Schoute method (Schoute, 1983), Lowerbound method,

Chen1 and Chen2 methods (Chen, 2006), Vogt method (Vogt, 2002), and Bayesian method

(Floerkemeier, 2007). Some of these methods are either having worse performances than

simple Schoute and Lowerbound methods, or are too complicated to be implemented for

RFID system. These methods are explained as follows:

3.4.5.1 Schoute Backlog Estimation Technique

Schoute (1983) developed a Backlog estimation technique for Dynamic Framed-Slotted

ALOHA using Poisson distribution. The Backlog, after the current frame Bt, is given by

equation:

Bt = 2.39× c

Where c represents the number of collided slot in the current frame, and Bt represents the

remaining Backlog. This technique has the best performance where fewest frames were

used, compared with other algorithms.

Schoute method is the simplest, easy to implement with low overhead computation,

and provides accurate tag estimation.

46


3.4.5.2 Lowerbound Backlog Estimation Technique

The Lowerbound estimation function is obtained under the assumption that a collision

involves at least two different tags. Therefore, Backlog after the current frame Bt is

defined by equation:

Bt = 2× c

Where c is the number of collided slot in the current frame, and Bt represents the

remaining Backlog.

Lowerbound method is also simple, easy to implement with low overhead computation,

and provides accurate tag estimation.

3.4.5.3 Chen1 and Chen2 Estimation Techniques

Most of the static algorithms estimate the Backlog with the number of collided slot.

However, Chen1 method (Chen, 2006) estimates the Backlog, based on the empty slot

information, through the probability of finding h empty slots after completing a frame.

Chen2 method (Chen, 2006) is a simpler way to estimate the number of tags, which is

illustrated by the following equation:

n = (L− 1)× s

h

Where n is the number of Backlog, L is frame length, s is the number of successful slots,

and h is the number of empty slots. If h = 0, n is set to a certain upper bound for the

tag’s estimate.

According to Wang et al. (2007), Chen1 and Chen2 methods have worse performances

than simple Schoute method. Chen1 method also requires complex computation, which

leads to high overhead and delays the tag identification process.

3.4.5.4 Vogt Estimation Techniques

In (Vogt, 2002), a procedure to estimate Backlog is presented by minimising the difference

between the observed value, including number of empty slot h, successful slot s, collision

slot c, and the expected value E(H), E(S), E(C). In order to find the comparative precise

Backlog, the reader needs to resolve the equation below:

47


min

h

s

c

− EN (H)

EN (S)

EN (C)

Vogt method presents the most accurate tag estimation. However, the complexity of

the algorithm resulted in high overhead and therefore cannot be applied to EPC Gen2

protocol (Lee et al., 2008b).

3.4.5.5 Bayesian Estimation Techniques

Bayesian method (Floerkemeier, 2007; Wu and Zeng, 2010) first computes the frame size

L, based on the current probability distribution of the random variable N that represents

the number of tags transmitting. Then it starts frame with L slots, waits for tag replies,

and updates probability distribution of N, based on evidence from the reader at the end

of the frame. The evidence comprises the number of empty slots, successful slots, and

collision slots in the last frame. The method then adjusts probability distribution N by

considering newly arrived tags and departing tags, including the ones that successfully

replied and did not transmit in subsequent slots. Bayesian method requires the most

complex computation and implementation of algorithm. This results in high overhead

and therefore delays the identification process.

3.5 Discussion

In this section, we analyse the shortcomings of existing data stream filtering and anti-

collision methods. We also identify research issues, and outline the research problems

being investigated in this thesis.

3.5.1 Limitations of Existing Methods

There are approaches previously discussed that can be applied to handle unreliable reads,

noises and duplications. However, many challenges remained for missed reads, which is the

most crucial issue in RFID applications, and is the hardest to identify and filtered. Filling

in dropped readings is one way to alter missed reads, but it is easier to fix the error from

the source where data is missing in the first place. The cause of these missed reads is the

RF collision, which occurs when two or more tags attempt to respond to a reader at the

same time. To solve RF collision problem, several anti-collision protocols are proposed in

the literature. However, these approaches still suffer from performance inefficiency, high

delay in identification time, and overhead computation of algorithms.

48

3.5. DISCUSSION

3.5.1.1 Limitation on Data Stream Filtering Techniques

Literature surveys on unreliable reads demonstrate that, if physical equipment including

tags, readers, and antennas are set up accordingly, the unreliable reads can be avoided in

most cases. The environmental selection for the RFID hardware deployment is also very

crucial to minimise the fault readings.

Noise readings can be simply filtered by scheduling a specific sliding window, which

expires over a certain time. If the count of a certain tag falls below the threshold, it is safe

to assume that the tag is outside the normal reading zone and is accidentally captured by

the reader.

Duplication at reader level can be avoided using the algorithm that identified the

unnecessary readers, and disabled them to minimise the number of reader within one

reading zone. In addition, duplication at data level can be filtered using several techniques

proposed in literatures. For instance, if the same set of tags are captured several times

within a specific time-frame, it is safe to assume that these tags are redundant and must

be removed.

As for missed reads, which is the most critical issue in RFID applications, several

techniques have been proposed to surrogate the missing data. However, it is preferred

that the missed read does not occur from the beginning. The common cause of missed

reads is the RF collision, which can be solved by applying anti-collision protocols to

prevent two or more tags from communicating to a reader at the same time. The two

types of tag anti-collision algorithms accepted and widely used in RFID systems are the

tree-based anti-collision, and the ALOHA-based anti-collision techniques.

3.5.1.2 Limitation on Tree-based Anti-Collision Techniques

There are several tree-based anti-collision techniques that can effectively prevent tag col-

lisions. Most memory anti-collision algorithms including “Binary Search”, “Bit Arbitra-

tion”, and “Tree Splitting”, require higher computational complexity compared with the

memoryless “Query Tree”. Nevertheless, some techniques from QT category still have

drawbacks and limitations, as described below:

• “Query Tree” protocols suffer from a long identification delay in the case where there

are a large number of tags within an interrogation zone. The delay is also caused

by similarity of ID and mobility of tags, where tags are not static (stay at the same

spot at all time).

• “Adaptive Query Splitting” technique introduces more complexity than QT because

information on last identification must be kept, in order to accelerate identification

process. This technique also requires tags to support both the transmission and

49


reception at the same time, thereby making it difficult to apply to low-cost passive

RFID systems.

• “Hybrid Query Tree” reduces collision cycles by querying 2-bits of prefixes for each

loop instead of 1-bit as in QT. However, it produces even more idle cycles than QT

because at one level it generates 4-leaf nodes, especially if there are not many tags

in the interrogation zone.

• There is a basic Idle cycles elimination (slotted back-off tag response mechanism)

for HQT, but this requires more time and memory. The extended version of HQT

also requires extra memory since it mimics the AQS based for last identification

information to be kept. However, HQT is better than QT in reducing collision

between tags, especially at higher number of tags.

3.5.1.3 Limitation on ALOHA-based Anti-Collision Techniques

ALOHA-based anti-collision technique is the most widely used type of anti-collision within

the probabilistic category. The earlier type of ALOHA anti-collision such as “Pure

ALOHA” and “Slotted ALOHA” performe poorly, while the more advanced “Framed-

Slotted ALOHA” has better performance. However, some techniques from Framed-Slotted

ALOHA category still have drawbacks and limitations, as described below:

• “Basic Framed-Slotted ALOHA” has the worst performance compared with other

Framed-Slotted ALOHA methods. This approach suffer from inaccurate frame-size

for each round of identification because it uses fixed frame-size. Therefore, the

system’s efficiency drops significantly in the event of there being too large or too

small tag counts.

• “Dynamic Framed-Slotted ALOHA” suffers from different level of insufficiency, de-

pending on frame-size prediction technique applied. If the number of unread tags are

not estimated accurately, correct frame-size cannot be determined to maximise the

system efficiency or minimise the tag collision probability. Thus, the performance of

DFSA depends highly on the selection of frame-size estimation technique.

• “Enhanced Dynamic Framed-Slotted ALOHA” assumes that the optimal frame-size

is fixed to 256. The number of group in EDFSA increases, using the power of two

(2,4,8...), which results in decreased system efficiency when the number of tags is

just above the threshold, and the number of group doubled-up.

• Current “Backlog Estimation” methods suffer from low performances or are too

complicated to be implemented for RFID system. Schoute’s method is the simplest,

easy to implement with low overhead computation, and provides accurate tag es-

timation. Other Backlog Estimation methods such as Chen1 and Chen2 methods,

50

3.5. DISCUSSION

Vogt method, and Bayesian method, have good simulated performance but cannot

be realistically applied to the actual passive RFID system. Specifically, the Bayesian

method requires the most complex computation and implementation of algorithm.

Overall, the literature review on current state-of-the-art techniques demonstrates that

some of the existing techniques are inefficient, while other methods are too complex with

high overhead cost of implementation. Some approaches cannot be further improved but

we can take advantage of other constraints, to improve their capability. For instance, a

basic tree-based methods such as QT (2-ary) and HQT (4-ary) are the best naive tree-

based methods but cannot be improved any further, in terms of simplicity, without the

need for complex algorithm. Thus, we need to take advantage of other constraints such as

EPC pattern, and a possible use of a combination of two trees, in order to improve memory

and power efficiency. Additionally, for probabilistic anti-collision, the DFSA method is

the simplest and most accurate method. However, in this case, to keep the simplicity of

the DFSA algorithm, only frame-size prediction scheme can be further improved.

3.5.2 Research Problem

It remains an open problem to find optimal solutions, to improve performance of the cur-

rent RFID anti-collision techniques. Two main goals for both tree-based deterministic and

ALOHA-based probabilistic anti-collision methods are to achieve the maximum efficiency

and to minimise identification time and resource wasted during the identification process.

Structuring anti-collision methods in RFID system is extremely important because it is a

step that determines the effectiveness and the overall quality of data captured.

In this thesis, the research problem is to investigate a suitable structure of tree-based

deterministic and ALOHA-based probabilistic anti-collision approaches such that new

efficient methods can be developed to improve performance of anti-collision technique in

RFID system. Given the limited resources of RFID components including the readers

and the tags, it is important to develop the anti-collision method that minimises power

and memory usage in the RFID reader, and to simplify the structure of algorithm so that

identification time can be minimised.

There are two main constraints in developing effective anti-collision algorithms. These

include limited power source from RFID reader and limited memory in both readers and

tags. By constructing complex anti-collision algorithms, high memory capacity and power

sources are needed, which is impractical in RFID system. Therefore, our aim is to de-

velop anti-collision schemes that are simple, with low overhead computation, and perform

effectively, compared with existing techniques.

To address our research problem, we compare our newly proposed methods to specific

existing approaches, which have simple algorithm structure, high robustness, and accom-

plished high performance with minimum time requirement. We also compare our proposed

51


tree-based method and ALOHA-based method, and analyse the benefit and detriment

of both methods toward different circumstances. Additionally, we introduced two novel

conceptual selective technique management, to employ the correct type of anti-collision

algorithm for specific scenario.

3.6 Summary

In this chapter, we have investigated different types of data stream errors including du-

plication, noise, unreliable reads, and missed reads. Particular attention was given to the

filtering of missed reads, which is the most crucial type of data stream error, mainly caused

by collision. We first described existing data stream filtering methods and anti-collision

methods, and categorised them into different classes. We then discovered and analysed

the shortcoming and limitations of these methods. Furthermore, we identified interest-

ing research issues concerning tag anti-collisions for RFID data stream. These research

problems are then addressed in this thesis.

The next three chapters describe a detailed investigation of tree-based and ALOHA-

based anti-collision, and the selective technique management.

52

4Deterministic Anti-Collision Approaches

In this chapter, we tackle problems on existing deterministic tree-based anti-collision

schemes including the amount of identification cycles produced and total memories used

during the identification process. We introduce two main methods derived from the fun-

damental of tree-based anti-collision protocols; 1) a Unified Q-ary Tree (Pupunwiwat and

Stantic, 2009a), (Pupunwiwat and Stantic, 2009b) and 2) a Joined Q-ary Tree (Pupunwi-

wat and Stantic, 2010c) with the intended goal to minimise memory usage queried by the

RFID reader. As mentioned in literature, most implementation of Tree-based algorithms

are deployed with older type of EPC class 1, which has limited memory and capability.

Although recent technology uses ALOHA-based anti-collision algorithms rather than the

Tree-based, we decided to improve the Tree-based approach with simple implementation

in order to suit the backward compatibility for older RFID systems. The remaining of

this chapter comprises the explanation on different types of EPC encoding schemes, the

typical scenarios discussion, the Splitting Fitness justification, the foundation of Unified

Q-ary Tree and Joined Q-ary Tree, and the experimentation evaluations.

4.1 EPC Encoding Schemes Analysis

There are many types of encoding schemes compatible with RFID passive tags. The most

common type of encoding is the General Identifier 96 bits scheme, which is independent of

any existing identity specification or convention and can be used in most events. There are

also different types of encoding designed specifically for special instances such as Serialised

Global Trade Item (SGTIN) 96 bits, which permit the direct embedding of GS1 System

53

CHAPTER 4. DETERMINISTIC ANTI-COLLISION APPROACHES

standard GTIN and serial number codes on EPC tags (EPCGlobal, 2008). In this thesis,

we observe three different types of encoding schemes and conduct experiments to find

impacts of each type of encoding. For the main part of our methodology, we use General

Identifier as our major encoding scheme since it is the most general form of encoding. We

select the other two encoding schemes based on their bit lengths and number of fields.

The three chosen encoding schemes are explained in the following subsections.

4.1.1 General Identifier 96 Bits

The General Identifier (GID) is defined for a 96-bit EPC, and is independent of any

existing identity specification or convention. In addition to the Header which guarantees

uniqueness of the encoding type, the General Identifier is composed of three fields; the

General Manager Number (GMN), Object Class (OC) and Serial Number (SN), as shown

in Table 4.1.

Table 4.1: The GID-96 includes three fields in addition to the Header, with a total of96-bits binary value. Only ‘H’ is shown in Binary, while the rest are shown in Decimal

GID-96 Bit Max.Decimal/BinaryHeader (H) 8 0011 0101

General Manager Number (GMN) 28 268,435,455Object Class (OC) 24 16,777,215

Serial Number (SN) 36 68,719,476,735

Table 4.1 shows an example of GID-96 EPC generation 2 encoding scheme. The

general structure of EPC tag encodings is a string of bits, consisting of a fixed length

(8-bit) Header followed by a series of numeric fields whose overall length, structure, and

function are completely determined by the Header value. There are four major fields in

the GID-96 bits.

Table 4.2: The SGTIN-96 includes six fields with a total of 96-bits binary value. Only ‘H’is shown in Binary, while the rest are shown in Decimal

SGTIN-96 Bit Max.Decimal/BinaryHeader (H) 8 0011 0000

Filter Value (FV) 3 Various depend on type000, 001, 010, 011, 100, 101, or 110

Partition (PT) 3 Various depend on typeRefer to Table 4.3

Company Prefix (CP) 20 - 40 999,999 - 999,999,999,999Item Reference (IR) 24 - 4 9,999,999 - 9Serial Number (SN) 38 274,877,906,943

4.1.2 Serialised Global Trade Item Number 96 Bits

The EPC tag encoding scheme for Serialised Global Trade Item Number (SGTIN) permits

the direct embedding of GS1 System standard GTIN and serial number codes on EPC tags.

54

4.1. EPC ENCODING SCHEMES ANALYSIS

In addition to a Header, the SGTIN-96 is composed of five fields: the Filter Value (FV),

Partition Value (PV), Company Prefix (CP), Item Reference (IR), and Serial Number

(SN), as shown in Table 4.2.

Table 4.2 shows an example of SGTIN-96 EPC generation 2 encoding scheme. The

general structure of EPC tag encodings is a string of bits, with a Header in binary value

of 0011 0000. The FV is not part of the SGTIN pure identity but is an additional data

that is used for fast filtering of basic logistics types. The available values of PV and the

corresponding sizes of the CP and IR fields are defined in Table 4.3. The CP contains

a literal embedding of the GS1 company prefix and the IR contains a literal embedding

of the GTIN item reference number. Finally, the SN contains a unique serial number of

each individual item being tagged by the RFID passive tag.

Table 4.3: SGTIN-96 and GIAI-96 Partitions in bitsPartition Company Prefix Item Reference Individual Asset Reference

(PT) (CP) (IR) - SGTIN96 (IAR) - GIAI-96000 40 4 42001 37 7 45010 34 10 48011 30 14 52100 27 17 55101 24 20 58110 20 24 62

Table 4.3 shows a SGTIN-96 bits and GIAI-96 (explained in the next subsection)

partition values. The Partition is an indication of where the subsequent CP and IR or

IAR are divided.

4.1.3 Global Individual Asset Identifier 96 Bits

The EPC tag encoding scheme for Global Individual Asset Identifier (GIAI) permits the

direct embedding of GS1 System standard GIAI codes on EPC tags. In addition to a

Header, the EPC GIAI-96 is composed of four fields: the FV, PV, CP, and Individual

Asset Reference (IAR), as shown in Table 4.4.

Table 4.4: The GIAI-96 includes five fields with a total of 96-bits binary value. Only ‘H’is shown in Binary, while the rest are shown in Decimal

GIAI-96 Bit Max.Decimal/BinaryHeader (H) 8 0011 0100

Filter Value (FV) 3 Various depend on type000, 001, 010, 011, 100, 101, or 110

Partition (PT) 3 Various depend on typeRefer to Table 4.3

Company Prefix (CP) 20 - 40 999,999 - 999,999,999,999Individual AssetReference (IAR) 62 - 42 4,611,686,018,427,387,903 - 4,398,046,511,103

55


Table 4.4 shows an example of GIAI-96 EPC generation 2 encoding scheme. The

general structure of EPC tag encodings is a string of bits, with a Header in binary value

of 0011 0100. The FV is not part of the GIAI pure identity but is an additional data

that is used for pre-selection of basic logistics types. The available values of PV and

the corresponding sizes of the CP and IAR numbers are defined in Table 4.3. The CP

contains a literal embedding of the GS1 company prefix and the IAR is a mandatory

unique number for each individual instance.

The difference between the SGTIN-96 bits and the GIAI-96 bits is that the GIAI-

96 contains one less field compared with SGTIN-96, since it uses only IAR as a unique

identifier instead of IR and SN. Thus, we chose the two encoding schemes for our experi-

mentation evaluation to see if there is any impact on our proposed tree-based anti-collision

methods. For the remaining of this chapter, all samples and methodology clarification will

use GID-96 bits encoding scheme, and the SGTIN-96 bits and the GIAI-96 bits will be

used in our experiment.

4.2 Warehouse Distribution Scenarios

In this chapter, we are examining specific scenarios based on the assumption that items

tend to move and stay together through different locations especially in a large warehouse.

We focus on Crystal warehouse scenario using GID-96 bits encoding scheme, which can be

classified into four different scenarios: 1) Unique Item-Level, 2) Unique Container-Level,

3) Unique Company-Level, and 4) Unique Warehouse-Level.

Figure 4.1: Crystal Warehouse Scenario: a) Unique Item-Level, b) Unique Container-Level, c) Unique Company-Level, and d) Unique Warehouse-level

4.2.1 Unique Item-Level Scenario

This scenario occurs when two collided tags (GID-96 encoding) are captured and they have

the same Encoding Scheme/Header (=), same GMN (=), same OC (=), but different SN

56

4.2. WAREHOUSE DISTRIBUTION SCENARIOS

( 6=). We can assume that all items are from the same warehouse that uses the same

encoding scheme throughout the warehouse, and the warehouse also keeps different kind

of products from different companies.

Figure 4.1 illustrates a sample Crystal warehouse scenario where:

a) Unique Item-Level: Two containers of crystal red-wine have the same Header (=),

GMN (=), and OC (=), but different SN (6=)

b) Unique Container-Level: Crystal white-wine and crystal red-wine containers have

the same Header (=) and GMN (=), but different OC ( 6=) and SN (6=)

c) Unique Company-Level: Crystal white-wine and crystal plate containers have the

same Header (=), but different GMN ( 6=), OC (6=), and SN (6=)

d) Unique Warehouse-level: Crystal plate and plastic plate containers have different

Header ( 6=), GMN (6=), OC (6=), and SN (6=)

As for Unique Item-Level circumstance, by using the Crystal warehouse scenario ex-

ample from Figure 4.1a), it can be seen that two collided tags are captured with the same

Encoding Scheme, General Manager Number, and Object Class. We believe that both tags

are each attached to two different cases of red-wine.

4.2.2 Unique Container-Level Scenario

The Unique Container-Level Scenario takes place when two collided tags are captured and

they have the same Header (=), same GMN (=), different OC (6=), and different SN (6=).

Figure 4.1b) shows that crystal red-wine glasses and crystal white-wine glasses are packed

in different case and pallet because they are different type of wine glasses. Within this

scenario, each case of wine glasses will have a unique SN attached to it, with different OC

for each pallet of white-wine or red-wine.

4.2.3 Unique Company-Level Scenario

The Unique Company-Level Scenario is illustrated in Figure 4.1c). Two collided tags are

captured and they have the same Header (=), and unique GMN ( 6=), OC (6=), and SN (6=).

We believe that one tag is attached to crystal plate case, while the other tag is attached

to white-wine case. We can assume that there are two different companies producing

separate crystal ware; and the wine glasses and plates are from different companies but

share the same warehouse because they are both crystal.

57


4.2.4 Unique Warehouse-Level Scenario

Unique Warehouse-Level Scenario occurs when two collided tags are captured and they

have different Header ( 6=), GMN (6=), OC (6=), and SN (6=). We can assume that all items

are from different companies that use different encoding schemes. For example, Figure

4.1d) shows that two wine glasses with different sculpture, one made from crystal and the

other from plastic, are allocated in the same warehouse. This Unique Warehouse-Level

scenario will not be discussed any further in this chapter because we are only looking at a

large warehouse distribution where most items move together as a group. Therefore, most

items from the same type of manufacturing will stick together until they are deployed to

smaller retailer.

4.3 Splitting Fitness

Splitting Fitness is the measurement level for the performance of our proposed tree-based

anti-collision methods. Splitting Fitness can be classified into Worst-Case splitting, Per-

fect splitting, and Random splitting. All three cases are discussed in the following subsec-

tions.

Figure 4.2: Splitting Fitness: a) Worst-Case Splitting, b) Perfect Splitting, and c) RandomSplitting

4.3.1 Worst-Case Splitting

Worst-Case splitting is when tags spliced into an unbalanced tree, where one child node

has no further node in a binary tree case. Figure 4.2a) shows that there are 16 tags at

Level 0 tree; then at Level 1, tags spliced into 16 tags on the left-hand node and no tag

on the right-hand node. As there is no tag left, no further splitting is necessary on the

right-hand node. This case of splitting will likely happen for the first few bits of EPC

identification in real world warehouse environment because most items have Massive tag

movement and usually belong to the same EPC pattern with similar ID. The Worst-Case

58

4.3. SPLITTING FITNESS

splitting caused more Idle cycles because all tags will be traveling down to only one side

of the tree, which results in further collision.

4.3.2 Perfect Splitting

Perfect splitting happens when a set of tags spliced to the left and right child node equally.

Figure 4.2b) shows that there are 16 tags at Level 0 tree; then at Level 1, tags spliced

equally into 8 nodes. Further splitting is required for both left-hand and right-hand

nodes until only one tag is left. This case of splitting is almost impossible in real world

scenario but will be the closest case to the latter stages (bits) of EPC identification within

warehouse environment because most items belong to the same group of EPC pattern. For

example, one pallet of white-wine glasses containing 20 cases move into one interrogation

zone. All items from the pallet will have the same OC and will travel along the same side

of child node at earlier levels; resulting in Worst-Case Splitting. However, the remaining

few bits will be unique for each EPC because they belong to SN. These remaining bits

encoded within the same EPC pattern will split almost equally to the left and right child

nodes. Both child nodes of left-hand side and right-hand side of binary tree will not be

exactly equal since data captured are not always even. Therefore, we call this situation

Partial-Perfect splitting.

Figure 4.3: A sample of 16 tags from the same pallet with the same Object Class and 16unique Serial Numbers

Figure 4.3 shows an example of warehouse environment tags splitting process. It can

be seen that there are 16 tags at the start; then at Level 1 to Level 3, all tags only move

down the left-hand child node while no tag moves down the right-hand node. We can

make the assumption that all of these tags have the same Header, same GMN, and same

OC. However, from Level 4 onward, tags start splitting into the left and right child nodes

59


of binary tree, which means that the query reached unique bits of tags that are SN so the

EPC ID becomes similar but no longer identical. At Level 7, all 16 tags are successfully

identified. Note that this Figure only shows an ideal sample of the identification process.

However, in the real world scenarios, tags will most likely spliced into Partial-Perfect

splitting from Level 4 to Level 7.

4.3.3 Random Splitting

Random splitting happens when a set of tags spliced to the left and right child node

randomly and splitting pattern cannot be found. Figure 4.2c) shows that there are 16 tags

at Level 0 tree; then at Level 1, tags spliced into 5 and 11 tags. Further at Level 2, tags

spliced into 2, 3, 4, and 7 where no specific splitting pattern exists. Thus, this situation

is called Random Splitting, which will likely happen in retail distribution environments

(belong to Unique Warehouse-Level Scenario) because all items usually come from different

locations. Therefore, this splitting case will not be further discussed as this chapter will

only focus on warehouse environment.

4.4 Unified Q-ary Tree

Instead of using a plain Q-ary tree, which uses every 1 (2-ary), 2 (4-ary), 3 (8-ary), or 4 (16-

ary) bits of tag ID to split a tag set, we propose a “Unified Q-ary Tree” or a combination

of two Q-ary trees (12 combinations), which can reduce more collision and at the same

time, memory usage can be minimised. For example, we can combine 4-ary tree with 8-ary

tree and apply this anti-collision to 96-bits EPC where the challenge is to configure the

right partition, so that 4-ary tree can be applied to the first half bits of EPC and 8-ary

tree can be applied to the remaining bits.

The remainder of this section will focus on two approaches: 1) a Naive approach,

where Q-ary tree is non-unified and only a single Q-ary tree is used as an anti-collision;

and 2) a Unified approach, where two Q-ary trees are combined as an anti-collision with

12 possible combinations.

4.4.1 Unified Q-ary Tree Fundamental

Our proposed Unified Q-ary Tree combined 2-ary, 4-ary, 8-ary, and 16-ary tree together

with 12 possible combinations. This approach will be applied on each collided tags EPC,

which will be split using every 1, 2, 3, or 4-bits of tag ID for the first few queries; and then

at one point every 1, 2, 3, or 4-bits will be queried. With the fact that most items from a

warehouse have bulky movement, the first few bits of EPC will be identical. Based on the

GID-96 bits encoding scheme, the first 8-bits of EPC are Header, which will be the same

for all items using the same encoding and they usually came from the same company and

60

4.4. UNIFIED Q-ARY TREE

in the same pallet. These 8-bits of EPC can be bypassed faster using 4-ary tree instead

of 2-ary tree but by doing so, too many Idle cycles will be produced. By using 4-ary tree

instead of 2-ary tree, the Number of bits needed for each query also accumulates faster.

Thus, we need to optimise the performance of Unified Q-ary Tree by configuring the right

separating point between the two Q-ary trees. The objective of Unified Q-ary tree is

to maintain the minimal number of cycles, and to minimise the Number of bits used for

querying all tags within an interrogation zone in order to improve the overall identification

time. Figure 4.4 shows the example of the Naive 4-ary tree (4.4a) and the Unified 4-ary

& 8-ary tree (4.4b).

Figure 4.4: A sample of: a) Naive 4-ary Tree, and b) Unified 4-ary & 8-ary Tree

Table 4.5: The Unified Q-ary Tree can be merged into twelve different combinations. 1, 2,3, and 4 represent the Number of bits queries each time for splitting tags when collisionoccurred

2-ary 4-ary 8-ary 16-aryF S F S F S F S

2-ary - 2 1 3 1 4 14-ary 1 2 - 3 2 4 28-ary 1 3 2 3 - 4 316-ary 1 4 2 4 3 4 -

For Number of cycles and Number of bits computation purposes, let ‘F’ be the first

half of EPC where bits are identical; and let ‘S’ be the second half of EPC where bits

are unique. Table 4.5 shows possible combinations between four of the Q-ary trees; 2-ary,

4-ary, 8-ary, and 16-ary.

4.4.2 Computation of Naive approach and Unified approach

To observe the difference between the processes of the Naive approach versus the Unified

approach, we initiate a computational process between the two approaches using two Naive

and two Unified Q-ary Trees as an example. The Naive 2-ary tree, Naive 4-ary tree, Unified

2-ary & 4-ary tree, and Unified 4-ary & 2-ary tree, are selected for the sample case.

61


Figure 4.5 shows a comparison between Unified approach (2-ary & 4-ary, 4-ary & 2-

ary) and Naive approach (4-ary, 2-ary) on the five EPC data. We can see that the Naive

4-ary tree has the shortest level of tree; however, by examining Table 4.7, 4-ary tree does

not have the lowest Total number of bits. This proves that levels of tree have an impact

on the Total number of bits and Overall cycles, but does not necessarily result in the best

performance of tree.

Figure 4.5: Identification processes of: a) Naive 2-ary Tree, b) Naive 4-ary Tree, c) Unified2-ary & 4-ary Tree, and d) Unified 4-ary & 2-ary Tree,

In order to calculate a Total number of bits required for the whole identification process,

information on Number of Child Nodes (NCN) for each level of tree and Number of Bits

per Query (NBQ) for that specific level is needed. Number of Bits per Level (NBL) can

be calculated as follows:

62


NBL = NCN ×NBQ (4.1)

Table 4.6: Calculation of Total memory bits required for two Naive and two Unified Q-aryTrees. TNBL shows the Total Number of Bits required for the specific Q-ary Tree

Level 2 2, 4 4, 2 41 2 2 8 82 4 4 16 163 6 6 24 244 8 8 32 325 10 10 18 406 12 12 40 487 14 14 22 -8 16 16 24 -9 18 40 - -10 40 48 - -11 22 - - -12 24 - - -

TNBL 176 160 184 168

After calculating the NBL for each level of tree, the Total number of bits (TNBL)

required can be found by doing the summation of all NBL. For example, in Figure 4.5a) it

can be seen that the tree has twelve levels where all levels, except Level 10, have two child

nodes each. For each Level, NBQ increased by 1-bit since this is a Naive 2-ary tree. Thus,

NBL for each level are (NCN x NBQ): 2 or (2x1), 4 or (2x2), 6 or (2x3), 8 or (2x4), 10 or

(2x5), 12 or (2x6), 14 or (2x7), 16 or (2x8), 18 or (2x9), 40 or (4x10), 22 or (2x11), 24 or

(2x12) respectively. After adding all NBL together, the TNBL of 176-bits is as shown in

Table 4.6.

Table 4.7: Sample Outcomes for 5 tags identification using Naive and Unified approachesCombination 2 2, 4 4, 2 4

Collision Cycles (F) 8 8 4 4Collision Cycles (S) 4 1 4 1

Total Collision Cycles 12 9 8 5Idle Cycles (F) 8 8 12 12Idle Cycles (S) 1 2 1 2

Total Idle Cycles 9 10 13 14Successful Cycles (F) 0 0 0 0Successful Cycles (S) 5 5 5 5

Total Successful Cycles 5 5 5 5Overall Cycles 26 24 26 24

Number of bits (F) 72 72 80 80Number of bits (S) 104 88 104 88

Total number of bits 176 160 184 168

Table 4.7 shows that both Naive 2-ary tree and Unified 4-ary & 2-ary tree have the

same number of Overall cycles. However, the Total number of bits for the two approaches

63


is different. The same goes with Naive 4-ary tree and Unified 2-ary & 4-ary tree where

Overall cycles are the same but have a different Total number of bits. As for the impact

of EPC data, we can see that when EPC IDs are identical (bit 1-8), the 2-ary tree works

better since it uses less Number of bits than the 4-ary tree. This difference cannot be

seen without calculating a proper Total number of bits because for the ‘F’, both 2-ary and

4-ary trees have the same number of Collision cycles and Idle cycles. However, for each

of these cycles, different Number of bits is used for querying, thus 4-ary tree uses more

bits than 2-ary tree. For ‘S’, 4-ary tree uses less Number of bits than 2-ary tree since the

number of Collision cycles happened more in 2-ary tree. Although a 4-ary tree produces

more Idle cycles than 2-ary tree in the ‘S’, it still produces less total number of Collision

cycles and Idle cycles. We can now assume that for Identical bits of EPC, lower level tree

(2-ary) can perform better than the higher level (4-ary) and for Unique bits of EPC, a

higher level tree is more suitable.

4.4.3 Experimental Evaluation

In order to show the significance of our proposed Unified Q-ary Tree methods, we con-

ducted three experimental evaluations and compared our methods with existing tech-

niques. There are three major data sets in the experiments. We performed ten runs on

each test case and presented the average results.

4.4.3.1 Environment

To study the proposed Unified Q-ary Tree method, all experiments are performed accord-

ing to a Crystal warehouse scenario. The experiments are assumed to be set up in a well

controlled environment where there is no metal or water nearby. We randomly generate all

data sets with assumptions that a UHF RFID reader is used and mounted on a dock door

at the end of a conveyor belt. Passive RFID tags are attached to each case of crystal ware.

Each pallet of wine glasses, plates, and bowls are moved along this conveyor belt. At this

stage, we assume that all pallets move in and out at the same time to an interrogation

zone, and no arriving tag or leaving tags are present during each identification round.

4.4.3.2 Experiment One Data sets

For the first experiment, the impact of different number of tags in an interrogation zone

is examined. The aim of this experiment is to find the best and the worst performance

of Q-ary tree for specific set of tags; therefore, only four Naive Q-ary trees are examined.

There are four test cases used in this experiment:

• Test case A: 2 pallets, 24 cases each, total 48 tags

• Test case B: 4 pallets, 24 cases each, total 96 tags

64


Figure 4.6: Level-Packaging: a) a case with 6 glasses, and b) a pallet with 27 cases

• Test case C: 6 pallets, 24 cases each, total 144 tags

• Test case D: 8 pallets, 24 cases each, total 192 tags

4.4.3.3 Experiment Two Data sets

In experiment two, the impact of Separating Point for a specific set of tags is examined.

The aim of this experiment is to find the best and the worst performance of the Naive

and Unified Q-ary Tree under different Separating Point. Thus, number of tags in an

interrogation zone is fixed to 192 tags (8 pallets of 24 cases each). There are three test

cases used in this experiment:

• Test case A: ‘F’ = 36 bits and ‘S’ = 60 bits

• Test case B: ‘F’ = 60 bits and ‘S’ = 36 bits

• Test case C: ‘F’ = 88 bits and ‘S’ = 8 bits

4.4.3.4 Experiment Three Data sets

After we identified the best Q-ary trees from the two experiments, the aim of the third

experiment is to compare the performance of the best Unified Q-ary Tree versus the Naive

Q-ary Tree. Results presented in this experiment are related to the Unique Item-Level

scenario mentioned earlier in Section 4.2. For the data set, there are 81 tags/EPC used

in this experiment. Each tag contains 60 Identical bits for ‘F’ and 36 Unique bits for ‘S’.

Each pallet contains 27 tags (See Figure 4.6) and three pallets are assumed to be visible

to the reader each time. We applied two Naive approaches, 2-ary and 4-ary trees, to the

data set with no partition. On the other hand, we applied Unified approaches to the data

set using ‘x’ = 60 and ‘y’ = 36 based on the nature of Unique Item-Level scenario where

the first 60-bits are identical.

65


Figure 4.7: Performances of Naive Q-ary Trees on different set of tags

Figure 4.6 displays a level-packaging, where each case contains 6 glasses and each

pallet contains 27 cases. Each case has different Serial Number and same Object Class if

in the same pallet. For our experiment, three of these pallets will be visible to the reader

attached to the dock’s door next to the conveyor belt.

4.4.4 Results

This section presents the results and performance measurement of Unified Q-ary Tree.

These results are displayed as follows:

4.4.4.1 Experiment One Results

Based on the experiment simulation, Figure 4.7 shows the result of four Naive Q-ary Trees:

2-ary, 4-ary, 8-ary, and 16-ary, using four different data sets with specific number of tags.

From Figure 4.7, we can see that the Naive 4-ary tree requires the least Total memory

bits, while the Naive 16-ary tree requires the most, regardless of number of tags within an

interrogation zone. The Total memory bits increased, providing the increment of number

of tags although the best performance tree is still 4-ary tree. It can now be concluded

that the best performing tree out of the four Q-ary trees is 4-ary tree, while number of

tags within the interrogation zone have no impact on the performance.

66


4.4.4.2 Experiment Two Results

Table 4.8 shows all results in Total memory bits required for the Naive and Unified Q-ary

trees. According to Table 4.8 and Figure 4.8, we can see that when the Separating Point

is between bit 36 and 37 (‘F’ = 36, ‘S’ = 60), a Unified 2-ary & 4-ary tree requires the

least Total memory bits, while the Naive 16-ary requires the most. This supports the

result from experiment one where the Naive 16-ary tree has the worst performance, and

the Naive 4-ary and the Naive 2-ary tree perform best respectively.

Table 4.8: This Table shows Total Memory Bits required for each Q-ary Tree for 192 tagsset identification

F=36, S=60 F=60, S=36 F=88, S=8Naive 2-ary 81792 81792 81792Naive 4-ary 71352 71352 71352Naive 8-ary 87696 87696 87696Naive 16-ary 120128 120128 1201282-ary & 4-ary 71316 71522 712982-ary & 8-ary 87156 86762 755302-ary & 16-ary 118580 115634 809144-ary & 2-ary 81828 81622 818464-ary & 8-ary 87192 86592 741124-ary & 16-ary 118616 115464 809688-ary & 2-ary 82332 82726 939588-ary & 4-ary 71856 72456 849368-ary & 16-ary 119120 116568 9736816-ary & 2-ary 83340 86286 12100616-ary & 4-ary 72864 76016 11051216-ary & 8-ary 88704 91256 110456

When the two trees, 2-ary and 4-ary are combined (Figure 4.8 Case e), the Total

memory bits required are slightly reduced from the Naive 4-ary and greatly reduced from

the Naive 2-ary tree. However, when combining and applying 4-ary tree on the ‘F’ and

2-ary tree on the ‘S’ (Figure 4.8 Case h), the Total memory bits are increased from the

Naive 2-ary and the Naive 4-ary tree. This happens because ‘F’ mostly engage Worst-Case

Splitting (Referred to 4.3), where a higher level tree produced more cycles in each level

than lower level tree. Thus, when applying 4-ary tree on the ‘F’, four nodes are produced

instead of two as in 2-ary tree; and three of the four nodes are Idle cycles which are waste

of resources and increase Number of bits required. On the contrary, by applying 4-ary

tree on the ‘S’, Number of bits required are less than 2-ary tree, which result in less Total

memory bits needed when combining 2-ary and 4-ary tree together.

The results of other Unified Q-ary Trees also show improvement to one of their Naive

methods. For example, Figure 4.8 Case k: 8-ary & 2-ary tree (82,332) outperformed the

Naive 8-ary tree (87,636) but did not outperformed the Naive 2-ary tree (81,792). The

same outcome also applied to other trees (Case f, g, i-p), which demonstrated that our

proposed Unifed Q-ary Trees contain significant improvement compared with the existing

anti-collision algorithms.

67


Figure 4.8: Performances of sixteen combination of Q-ary Trees (4 Naive and 12 Unified)where F = 36 and S = 60

Table 4.8 also shows the performances of both Naive Q-ary Trees and Unified Q-ary

Trees where ‘F’ = 60 and ‘S’ = 36. It can be seen that the Naive 4-ary tree requires the

least Total memory bits out of the sixteen approaches. Both Unified 2-ary & 4-ary and

4-ary & 2-ary did not outperformed the Naive 4-ary since the Separating Point is different

between Test case A and Test case B. According to the test case applied, we know

that all data have the same Header and GMN, which are the first 36-bits of EPC only.

OC is different for each pallet, thus eight pallets requires eight different OC (Bit 37 to

Bit 60). The Separating Point of these results is between bit 60 and 61, which already

pass the OC. Therefore, a 2-ary tree requires higher Number of bits than 4-ary tree for ‘F’,

resulting in higher Total memory bits required when combining the two trees together.

The performances of both Naive Q-ary Trees and Unified Q-ary Trees, where ‘F’ = 88

and ‘S’ = 8, are displayed in Table 4.8. The result shows that the Unified 2-ary & 4-ary

tree requires the least Total memory bits, while a Unified 16-ary & 2-ary tree requires the

most. This is because after tags spliced into eight different pallets, the remaining bits of

EPC became identical again until almost the last few bits. Therefore, 2-ary tree requires

less Number of bits than 4-ary tree for ‘F’, resulting in less Total memory bits required for

the Unified 2-ary & 4-ary tree.

From experimental evaluation, we can now summarise that a Unified 2-ary & 4-ary

Tree performed the best overall by reducing the Total memory bits required, where it

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Figure 4.9: Results of two Naive approaches (2-ary, 4-ary) and two Unified approaches(2-ary & 4-ary, 4-ary & 2-ary) for number of Idle cycles, Collision cycles, Successful cycles,and Overall cycles

outperformed both of its Naive methods, which means that identification time can be

minimised.

4.4.4.3 Experiment Three Results

Figure 4.9 shows the average results, from ten runs, on all four combinations: Naive 2-ary,

Unified 2-ary & 4-ary, Unified 4-ary & 2-ary, and Naive 4-ary tree. From the figure, we

can see that the Naive 4-ary tree produced the most Idle cycles while the Naive 2-ary

tree produced the least. In contrast, the Naive 2-ary tree produced the most Collision

cycles while the Naive 4-ary tree produced the least. Both Naive 2-ary and Unified 4-ary

& 2-ary have the same total number of cycles, which corroborate our methodology. In

addition, the total number of cycles for Naive 4-ary tree and Unified 2-ary & 4-ary tree

are also equal. The total number of cycles can, at one point, clarify the performance of all

four methods. We notice that both Naive 4-ary tree and Unified 2-ary & 4-ary tree have

less total cycles than 2-ary and 4-ary & 2-ary. This means that the first two methods will

use less Number of bits in querying for all 81 tags than the other two. However, without

looking into the actual results of Number of bits, we still cannot conclude which of the two

methods will achieve the least identification time for querying.

Based on Figure 4.9, we are now aware that Successful cycles of all four methods are all

equal to 81, which means that all tags in the interrogation zone are 100 percent identified.

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Figure 4.10: Results of two Naive approaches (2-ary, 4-ary) and two Unified approaches(2-ary & 4-ary, 4-ary & 2-ary) for Number of bits queried for Idle cycles, Collision cycles,Successful cycles, and Overall cycles

We can also see that all 81 tags were recognised at the later stages, where all bits (bit

no. 61-82) are unique. As for Identical bits of Idle cycles and Collision cycles, the sum

of Idle cycles and Collision cycles have an outcome of 120 cycles, which means that both

methods of 2-ary or 4-ary tree have no impact in the sense of cycles count. However, as

mentioned earlier, we need to calculate the actual Number of bits in order to clarify the

difference of the performance of both methods. The next Figure (Figure 4.10) shows the

Number of bits for Idle cycles, Collision cycles, Successful cycles, and Overall cycles, of

each method.

Figure 4.10 shows all the actual bits for all queries that occur during tags identification.

It can be seen that the Unified 2-ary & 4-ary tree has the lowest Number of bits queried

for entire identification process. This verify our theory that by using a lower level tree

for Identical bits of EPC and higher level tree for Unique bits of EPC, Number of bits

queried can be minimised and identification process can be accelerated. There is not much

difference in results but we can assume that as the number of tags in an interrogation zone

increases, and when our proposed Joined Q-ary Tree (Section 4.5) is applied instead of

the Unified Q-ary Tree, we will be able to see more differences in the outcome.

For Identical bits of EPC, there is a slight difference between the Number of bits queried

by the four methods. While Figure 4.9 shows that there is no difference between total

number of cycles for Identical bits for all four methods, we can see clearly that the Total

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Figure 4.11: Performance Analysis of 2-ary Tree vs. 4-ary Tree on Unique bits of EPC,Bit 61 - 68, until all tags are identified. Results of Overall cycles are displayed

number of bits is different for each case in Figure 4.10. This is because each query inquired

each time issues different Number of bits. For example, 4-ary tree issues two extra bits

from the last query (from the parent node), while 2-ary tree only append one extra bit

to the last query. The Unified 2-ary & 4-ary tree performed the best overall and required

60 bits less than the Naive 4-ary tree, and 924 bits less than the Naive 2-ary tree. In

contrast, the Unified 4-ary & 2-ary tree performed the worst out of all four methods. This

is because a higher level tree was used at the earlier stages of identification where all bits

are identical. This means that more than 75 percent of the queries were Idle cycles which

are waste of resources (See Figure 4.10 - 4-ary & 2-ary; Idle cycles:Collision cycles = Ratio

of 3:1 or 75%:25%). By using 2-ary tree instead of 4-ary tree for Identical bits, 60 bits of

queries were reduced (3720 minus 3660).

For Unique bits of EPC, Number of bits query rises rapidly compared with Identical

bits. Figure 4.10 shows that, by using 4-ary tree for Unique bits of EPC, Number of bits

were slightly reduced (see Total bits queried for unique bits). The performance of each

method on Unique bits of EPC will be specified in detail in Figure 4.11 and Table 4.9.

Figure 4.11 and Table 4.9 show the number of Idle cycles, Collision cycles, Successful

cycles and Overall cycles produced in each query round. We can see that at bit 63-64 to

bit 65-66, the difference between Overall cycles of 2-ary and 4-ary tree grows. After bit

67-68, there is not much difference between the two methods. From bit 73-74 to bit 79-80,

there are no Successful cycles for both methods; thus, there are no differences for their

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Table 4.9: Performance Analysis of 2-ary Tree vs. 4-ary Tree on Unique bits of EPC, Bit61 - 68, until all tags are identified

Bit 61, 62 Bit 63, 64 Bit 65, 66 Bit 67, 682-ary 4-ary 2-ary 4-ary 2-ary 4-ary 2-ary 4-ary

Idle Cycles 0 0 0 0 8 16 35 59Collision Cycles 6 4 23 15 45 23 42 20

Successful Cycles 0 0 1 1 21 21 13 13Overall Cycles 6 4 24 16 74 60 90 92

Bit 69, 70 Bit 71, 72 Bit 73, 74 Bit 75, 762-ary 4-ary 2-ary 4-ary 2-ary 4-ary 2-ary 4-ary

Idle Cycles 36 56 36 57 38 57 38 57Collision Cycles 40 20 38 19 38 19 38 19

Successful Cycles 4 4 4 4 0 0 0 0Overall Cycles 80 80 78 80 76 76 76 76

Bit 77, 78 Bit 79, 80 Bit 81, 822-ary 4-ary 2-ary 4-ary 2-ary 4-ary

Idle Cycles 38 57 38 57 19 38Collision Cycles 38 19 38 19 19 0

Successful Cycles 0 0 0 0 38 38Overall Cycles 76 76 76 76 76 76

Overall cycles. We can now assume that at bit 61-62 to bit 71-72, the EPC are similar but

not identical, which results in the unstable change in number of Overall cycles. On the

other hand, at bit 73-74 to bit 79-80, we can assume that all bits become identical again,

resulting in no change in Overall cycles. The number of collided tags at bit 73-74 to bit

79-80 are exactly two, since the ratio of Idle cycles to Collision cycles is 1:1 for 2-ary tree

and 3:1 for 4-ary tree respectively. Finally, all tags were identified at bit 81-82, resulting

in the same number of Overall cycles for both 2-ary tree and 4-ary tree.

From the experiments, we can now conclude that by using a lower level 2-ary tree for

Identical bits of EPC, and by using a higher level 4-ary tree for Unique bits of EPC, the

Total number of bits for querying can be decreased. By reducing the Total number of bits,

identification time for each round can be minimised.

4.5 Joined Q-ary Tree

The Joined Q-ary Tree employs the right combination of Q-ary trees for each specific

scenario. The joined Q-ary Tree adaptively adjust its tree branches to suit EPC pattern

rather than only split once as in the Unified Q-ary Tree. This procedure will further

reduce accumulative bits from the reader’s queries and improve the robustness of the

overall identification process.

The Joined approach is a combined Q-ary trees, specifically 2-ary tree and 4-ary tree,

which have been identified to be the best Q-ary trees in our previous researches (See

experiment on Unified Q-ary Tree: Section 4.4.4). The Joined approach will be applied

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4.5. JOINED Q-ARY TREE

on each collided tags EPC, which will be split using every 1 or 2-bits of tag ID for the first

few queries; and then at one point, every 1 or 2-bits will be queried. In order to optimise

the performance of Joined Q-ary Tree, the right Separating Point (SP) between the two

Q-ary trees needs to be configured. The objective of Joined Q-ary Tree is to reduce the

Bits Length queried by a reader so that identification time can be minimised. In this

section, we will investigate and compare the “Naive Q-ary Tree” approach and our newly

proposed “Joined Q-ary Tree”.

Figure 4.12: A sample of: a) a Naive 4-ary Tree, b) a Naive 2-ary Tree, and c) a JoinedQ-ary Tree

Figure 4.12 shows the example of a) Naive 2-ary, b) Naive 4-ary, and c) Joined Q-ary

Tree. Joined Q-ary Tree bonded both 2-ary and 4-ary trees together and applied to specific

bits of EPC, depending on how Identical or Unique they are.

4.5.1 EPC Bits Prediction and Classification

In warehouse distribution environment according to Unique Item-Level and Unique

Container-Level Scenarios, it is known that the first 36-bits of EPC (Header and GMN)

are definitely identical. However, 24-bits of OC can be both Identical and Unique for all

tags, depending on how many pallets existed within one interrogation zone. For example,

if there are five pallets of 12 cases each in the interrogation zone, there will be five

different OC and sixty unique SN for all sixty items (cases).

Since OC involved 24-bits of EPC (allow 16,777,215 unique tags) but only five unique

OC is needed, we must calculate a certain number of Unique bits needed in order to apply

the right Q-ary tree. This also applies to SN that contains 36-bits of string. Assuming

that EPC pattern is used, not all 36-bits of these strings will be Unique.

Table 4.10 shows a formal structure for bits classification of GID-96 bits EPC. It can

be seen that the Identical bits of EPC always equal to 36-bits for the first 36-bits of EPC.

This includes 8-bits of Header and 28-bits of GMN, which are always the same for all tags.

For Object Class, 24-bits are available where Unique bits within Object Class (UOC) can

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be predicted using Equation 4.2. In addition, Unique bits within Serial Number (USN)

with 36-bits can also be predicted using the same Equation.

Table 4.10: Formal structure of bits classification of EPC GID-96 bits. *UOC is numberof Unique bits within Object Class and **USN is number of Unique bits within SerialNumber

Length Identical UniqueHeader 8 8 0

General Manager Number 28 28 0Object Class 24 24 - UOC UOC*

Serial Number 36 36 - USN USN**

Our method is executed based on the assumption that the approximate number of tags

(pallets, cases) is known, prior to the identification process. This information is needed for

Unique bits calculation: UOC, and USN from Table 4.10. However, in most circumstances,

number of tags is usually unknown until the first query is issued by the reader. Therefore,

UOC and USN of Joined Q-ary Tree can be initially set to zero and after the first round

of identification, these two parameters can be computed.

Joined Q-ary Tree adaptively adjusts their tree branches at specific SP. These SP is

configured according to Identical bits and Unique bits within an EPC data. In order to

calculate the estimated number of Unique bits within an EPC, we need the average number

of tags within an interrogation zone, and then to apply the equation below:

B = log2(N) (4.2)

Where N = Number of tags, B = Unique Bits of EPC.

Figure 4.13: Joined Q-ary Tree structure for GID-96 bits EPC

Figure 4.13 illustrates tag splitting behaviour of massive tag within a warehouse. The

first 36-bits of EPC belongs to Header and GMN, therefore 2-ary tree is applied to these

bits since it is simplified to be the most suitable tree for Identical bits of EPC. UOC and

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USN on the other hand, can be split using 4-ary tree since it is proven as the most suitable

tree for Unique bits of EPC.

4.5.2 Unique Bits Computation

To demonstrate a calculation of Unique bits of EPC, we examine a Massive tag movement

of 720 tags within 12 pallets (OC). By using Equation 4.2, UOC and USN can be calculated

as follows:

UOC = log2(N) =log10(N)

log10(2)=

log10(12)

log10(2)≈ 4

USN = log2(N) =log10(N)

log10(2)=

log10(60)

log10(2)≈ 6

Therefore, number of Unique bits required to cover all unique OC is approximately

4-bits and approximately 6-bits for SN.

Table 4.11: Sample bits classification of EPC GID-96 bits, where Object Class = 12 andSerial Number = 60 (Total of 720 tags)

Length Identical UniqueHeader 8 8 0GMN 28 28 0

Object Class 24 20 4Serial Number 36 30 6

Figure 4.14: Joined Q-ary Tree structure for GID-96 bits EPC with 36 Identical bitsHeader and GMN, 20 Identical bits OC, 4 Unique bits OC, 30 Identical bits SN, and 6Unique bits SN

Table 4.11 shows a sample structure for bits classification of 720 tags with GID-96 bits

EPC encoding scheme. Corresponding with Figure 4.14, we can see that the Identical bits

of EPC always equal to 36-bits for the first 36-bits of EPC. 2-ary tree is applied to these

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bits. Identical bits of OC (20-bits) and SN (30-bits) are also spliced by 2-ary tree. UOC

(4-bits) and USN (6-bits) on the other hand, can be split using 4-ary tree.

4.5.3 Tags Splitting

We are now initiating a sample comparison between the performance of Naive 2-ary tree,

Naive 4-ary tree, and Joined Q-ary Tree. Table 4.12 shows ten sample EPC of 36-bits

tags with 24-bits Identical and 8-bits Unique. We assumed that all Identical bits of EPC

belong to Header and GMN, while Unique bits of EPC belong to OC and SN.

Table 4.12: Sample 36 bits tags with 24 Identical bits and 8 Unique bitsIdentical bits Unique bits

0011 0101 1010 1101 0000 1010 1011 01010011 0101 1010 1101 0000 1010 0100 10110011 0101 1010 1101 0000 1010 1101 11000011 0101 1010 1101 0000 1010 0010 01000011 0101 1010 1101 0000 1010 1100 10000011 0101 1010 1101 0000 1010 0001 01000011 0101 1010 1101 0000 1010 0011 00110011 0101 1010 1101 0000 1010 1110 10100011 0101 1010 1101 0000 1010 1111 00100011 0101 1010 1101 0000 1010 0000 0110

4.5.3.1 Tag Identification

After applying different trees on the sample tag sets, we can see that the Naive 2-ary tree

has the best performance on Identical bits of EPC by using smallest number of queries

(Bits Length) as shown in Table 4.13. On the other hand, 4-ary tree performed better on

Unique bits than 2-ary tree.

Table 4.13: Identification process of 2-ary Tree and 4-ary Tree on Identical bits and Uniquebits of EPC

Performances on Identical bitsQ-ary Idle Collision Successful Total BitsTree Cycles Cycles Cycles Cycles Length2-ary 24 24 0 48 6004-ary 36 12 0 48 624

Performances on Unique bitsQ-ary Idle Collision Successful Total BitsTree Cycles Cycles Cycles Cycles Length2-ary 0 8 10 18 364-ary 2 0 10 12 32

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4.5.3.2 Bits Length Calculation

In order to calculate a Total Bits Length required for the whole identification process

for 2-ary tree, 4-ary tree, and Joined Q-ary Tree, information on Number of Child Nodes

(NCN) for each level of tree and Number of Bits per Query (NBQ) for that specific level,

is needed. Number of Bits per Level (NBL) can be calculated using Equation 4.1:

NBL = NCN ×NBQ

After calculating the NBL for each level of tree, the Total Bits Length (TNBL) required

can be found by doing the summation of all NBL. After adding all NBL together, the

TNBL of 828-bits, 848-bits, and 728-bits, are shown in Table 4.14 respectively for 2-ary

tree, 4-ary tree, and Joined Q-ary Tree.

Table 4.14: Calculation of Total Bits Length required for two Naive Q-ary Trees and aJoined Q-ary Tree. TNBL shows the Bits Length required for the specific Naive/JoinedQ-ary Tree

Level Naive 2-ary Naive 4-ary Joined Q-ary1-12 156 624 15613-14 54 224 5415-24 390 0 39025-26 104 0 12827-28 124 0 0

TNBL 828 848 728

Table 4.15: Performance Analysis of Naive 2-ary Tree, Naive 4-ary Tree, and Joined Q-aryTree on set of 10 sample tags

Different Q-ary Tree on Sample TagsQ-ary Idle Collision Successful Total Identical Unique TotalTree Cycles Cycles Cycles Cycles Bits Bits Bits2-ary 24 32 10 66 600 228 8284-ary 38 12 10 60 624 224 848

Joined 26 24 10 60 600 128 728

Table 4.15 shows the overall calculation of Bits Length queried on Identical bit and

Unique bits of EPC. We can see that the Joined Q-ary Tree required the least Total Bits

Length compared with the two Naive Q-ary Trees. Joined Q-ary Tree reduced Bits Length

by almost 15 percent compared with Naive 4-ary tree and by 12 percent compared with

Naive 2-ary tree.


In order to show the significance of our proposed Joined Q-ary Tree methods, we conducted

two experimental evaluations and compared our methods with existing techniques. There

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are two major data sets in the experiments. We performed ten runs on each test case and

presented the average results.

4.5.4.1 Environment

To study the proposed Joined Q-ary Tree method, all experiments were performed accord-

ing to a Crystal warehouse scenario and were assumed to be under the same environment

as for the experimental evaluation of the Unified Q-ary Tree (Section 4.4.3).

4.5.4.2 Experiment One Data sets

In the first experiment, we conducted an experiment using three different tag sets: 288

tags, 576 tags, and 864 tags. The impact of different number of tags in an interrogation

zone and performances of Joined Q-ary Tree approach is to be evaluated. Data sets using

EPC pattern from Table 4.16 are applied:

Table 4.16: Chosen EPC Pattern for Experiment OneEPC Pattern

H 0011 0101GMN 104,426,055OC [9,872,273 - 9,872,308]SN [26,292,755,245 - 26,292,755,268]

There are three test cases used in this experiment:




For the joined Q-ary Tree approach, the SP of each test case must be calculated using

Equation 4.2.

Theoretical Bits Prediction Assuming that the existence of tags are known before

identification process, by using Equation 4.2, UOC and USN of Test case A, B, and C

can be calculated as follows:

Test case A:

UOC = log2(12) ≈ 4 , USN = log2(24) ≈ 5

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Test case B:

UOC = log2(24) ≈ 5 , USN = log2(24) ≈ 5

Test case C:

UOC = log2(36) ≈ 6 , USN = log2(24) ≈ 5

Therefore, number of Unique bits required to cover all unique OC and SN are as

shown in Table 4.17.

Table 4.17: Identical and Unique Bits classification of EPC GID-96 bits for Experimentone - Test case A, B, and C. I = Identical bits, U = Unique bits

288 tags 576 tags 864 tagsI U I U I U

H 8 0 8 0 8 0GMN 28 0 28 0 28 0OC 20 4 19 5 18 6SN 31 5 31 5 31 5

Actual Separating Point Configuration After theoretical bits estimation, we as-

sumed that the actual encoding of Unique bits may be 1 to 2-bits longer than predicted.

Therefore, we added 2-bits to the predicted Unique bits of each data set. If the predicted

bits added up as odd number, one more bit is further attached. For example, UOC of

data set two (576 tags) is predicted to be 5-bits long. By affixing additional 2-bits, total

of 7-bits are applied to UOC. However, this added up as odd number, therefore, one more

bit is further attached (Total 8-bits). Table 4.18 shows an actual SP for each data set.

At specific SP, the Joined Q-ary Tree will adaptively change its branch to 2-ary or 4-ary.

Since 2-ary tree is applied from SP1 to SP3, the Joined Q-ary Tree only needs to adjust

its branch at SP4, SP5, and SP6.

Table 4.18: Actual Separating Point for Experiment one - Test case A, B, and C. At aspecific SP, Joined Q-ary Tree will adjust its branch to either 2-ary or 4-ary Tree

288 tags 576 tags 864 tagsSP 2 4 2 4 2 4

H 1 0 - 0 - 0 -GMN 2 0 - 0 - 0 -OC(I) 3 37 - 37 - 37 -OC(U) 4 - 55 - 53 - 53SN(I) 5 61 - 61 - 61 -SN(U) 6 - 89 - 89 - 89

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4.5.4.3 Experiment Two Data sets

We conducted a second experiment using three different tag sets: 288 tags, 576 tags, and

864 tags. The impact of different encoding schemes and performances of Joined Q-ary Tree

approach is to be evaluated. Data sets using EPC pattern from Table 4.19 are applied:

Table 4.19: Chosen EPC Pattern for Experiment TwoGID-96 bits EPC Pattern

H 0011 0101GMN 104,426,055OC [9,872,273 - 9,872,308]SN [26,292,755,245 - 26,292,755,268]

SGTIN-96 bits EPC PatternH 0011 0000

FV 011PT 101CP 9,352,006IR [595,914 - 595,949]SN [121,705,236,366 - 121,705,236,389]

GIAI-96 bits EPC PatternH 0011 0100

FV 011PT 011CP 581,162,659IAR [815,223,149,060,764 - 815,223,149,061,627]

There are three test cases used in this experiment:




4.5.5 Results

This section presents the results and performance measurement of Joined Q-ary Tree.

These results are displayed as follows:

4.5.5.1 Experiment One Results

Based on the experiment results shown in Figure 4.15, the Joined Q-ary Tree always

performed the best out of the three approaches considered, while the 4-ary tree has the

worst performance, regardless of number of tags within an interrogation zone. This corre-

sponds with our methodology that if the Separating Point and the Q-ary trees are applied

correctly to the EPC data, the optimal results can be achieved by the Joined Q-ary Tree.

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Figure 4.15: Performances comparison (GID-96) between Naive approaches and JoinedQ-ary approach

Table 4.20 demonstrates that 2-ary tree has the better performance than 4-ary tree

by about 1 percent, while Joined Q-ary Tree’s performance is approximately 12 percent

better than the 2-ary tree. The 4-ary tree has the worst performance out of the three

approaches considered.

Table 4.20: Percentage improvement of the proposed Joined Q-ary Tree versus existingNaive 2-ary (N2) and Naive 4-ary (N4) approaches

2-ary Tree 4-ary Tree Joined Q-aryImproved Improved Improved Improved Improved Improvedfrom N2 from N4 from N2 from N4 from N2 from N4

288 0 1.04 -1.05 0 11.72 12.64576 0 0.92 -0.93 0 12.37 13.18864 0 0.52 -0.52 0 13.02 13.47

Figure 4.16 illustrates the improvement in percentage of Joined Q-ary Tree compared

with the 2-ary tree and the 4-ary tree. The percentage of improvement increases more

slowly once the number of tags within the interrogation zone gets higher.

To further analyse and compare performances of Naive Q-ary approaches and Joined

Q-ary approach, Table 4.21 shows Accumulative Bits Length of each approach until all

tags were identified. We can see that the number of Bits Length accumulates faster

when the identification reached SP4. This is when EPC data became more Unique and

larger Bits Length issued by reader were required. All three approaches have the same

pattern of increment, where number of tags in an interrogation zone has no impact on the

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Figure 4.16: Percentage of improvement (GID-96) of Joined Q-ary Tree compared withNaive 2-ary Tree and Naive 4-ary Tree

Table 4.21: Accumulative Bits Length of three approaches: Naive 2-ary Tree, Naive 4-aryTree, and Joined Q-ary Tree

2-ary Tree288 tags 576 tags 864 tags

SP1 72 72 72SP2 1332 1332 1332SP3 2970 2756 2756SP4 3682 3730 3926SP5 17178 27642 38310SP6 25642 43690 61958

4-ary Tree288 tags 576 tags 864 tags

SP1 80 80 80SP2 1368 1368 1368SP3 3024 2808 2808SP4 3736 3776 3816SP5 17400 27968 38536SP6 25912 44096 62280

Joined Q-ary Tree288 tags 576 tags 864 tags

SP1 72 72 72SP2 1332 1332 1332SP3 2970 2756 2756SP4 3358 3308 3348SP5 17246 27948 38628SP6 22638 38284 53892

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Figure 4.17: Accumulative Bits Length (GID-96) of three approaches: a) Naive 2-ary Tree,b) Naive 4-ary Tree, and c) Joined Q-ary Tree on different tag sets

performances when EPC data were identical. This pattern can be seen in Figure 4.17,

where Accumulative Bits Length of the Joined Q-ary Tree approach is displayed. There

are no or little difference between SP1 to SP4 on all data sets, however, number of Bits

Length started to increase rapidly once it reaches SP5 and SP6.

Out of the three approaches, the Joined Q-ary Tree has the slowest incremental rate

between SP4 and SP6 for all data sets. The incremental Bits Length also slows down once

number of tags within the interrogation zone gets bigger. Therefore, the performance of

Joined Q-ary Tree will increase for larger set of tags compared with the Naive approaches.

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Since the Joined Q-ary Tree accumulated least Bits Length out of the three approaches,

it can be concluded that the best performing approach, using GID-96 bits encoding scheme,

is the Joined Q-ary Tree. Also, the number of tags within the interrogation zone has no

impact on Identical bits of EPC data. Additionally, by applying the right Q-ary tree on

specific bits of EPC, performance of Joined Q-ary Tree is improved.

4.5.5.2 Experiment Two Results

We observed three types of EPC encoding scheme in this experiment. Table 4.22 and

Figure 4.18 demonstrate results of the Joined Q-ary Tree using the three encoding schemes

on different number of tags versus the two Naive Q-ary Trees. From both Figure and

Table, it can be seen that the Joined Q-ary Tree always performed the best out of the

three approaches considered, while the 4-ary tree has the worst performance for GID-96

bits (Figure 4.18a) and SGTIN-96 bits (Figure 4.18b) encoding schemes. For GIAI-96 bits

encoding (Figure 4.18c), the Naive 2-ary tree has the worst performance compared with

the Naive 4-ary tree and the Joined Q-ary Tree.

Table 4.22: Number of bits length of three approaches using different Encoding Scheme:a) GID-96 bits, b) SGTIN-96 bits, and c) GIAI 96 bits

GID-96 bits288 tags 576 tags 864 tags

2-ary 25642 43690 617344-ary 25912 44096 62280

Joined 22638 38284 53892SGTIN-96 bits

288 tags 576 tags 864 tags2-ary 27490 47396 670504-ary 27736 47584 67264

Joined 24556 41694 58494GIAI-96 bits

288 tags 576 tags 864 tags2-ary 10424 11592 127104-ary 10392 11448 12440

Joined 9300 10356 11348

The differences in the results for GIAI-96 bits against the other two encoding schemes

can be explained by the number of fields involved in the EPC pattern. From all 96 bits of

the EPC data, the GIAI-96 bits scheme only has the IAR (Item-Level) field that involved

both Identical and Unique bits of EPC. The other four fields engaged only Identical bits

(See Table 4.19). This is different from the other two encoding schemes where there are

two partitions that engaged Unique bits of EPC data; 1) OC and 2) SN in GID-96 bits

and 1) IR and 2) SN in SGTIN-96 bits. Therefore, the Unique part of EPC in GIAI-96

bits encoding scheme is in the latter stage of EPC data, which results in higher number

of queries being issued by both 2-ary and 4-ary trees.

Since the 4-ary tree performs better in Unique bits, it needs less queries for IAR field

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Figure 4.18: Performances comparison between Naive approaches and Joined Q-ary ap-proach using different Encoding Scheme: a) GID-96 bits, b) SGTIN-96 bits, and c) GIAI96 bits

compared with the 2-ary tree. The nature of GIAI-96 bits encoding scheme also resulted

in minimal number of queries issued, compared with the other two encoding schemes, as

it involves less partitions. Nevertheless, the Joined Q-ary Tree still outperformed both

Naive 2-ary and Naive 4-ary trees regardless of the encoding scheme used. This is because

the Joined Q-ary Tree adapts the best tree to suit the circumstance of each part of the

EPC data, which resulted in the lowest number of queries issued by the RFID readers.

85


Table 4.23: Percentage of improvement of Joined Q-ary Tree versus two Naive approachesusing different Encoding Scheme: a) GID-96 bits, b) SGTIN-96 bits, and c) GIAI 96 bits

GID-96 bits288 tags 576 tags 864 tags

Improved from 2-ary 11.72 12.37 12.70Improved from 4-ary 12.64 13.18 13.47

SGTIN-96 bits288 tags 576 tags 864 tags


GIAI-96 bits288 tags 576 tags 864 tags


Table 4.23 demonstrates that the Joined Q-ary Tree performs better than the 2-ary

tree by about 10 to 13 percent, and achieve around 8 to 13 percent better than the 4-ary

tree. These all depend on different number of tags within the interrogation zone and

the chosen encoding scheme. Figure 4.19 illustrates the improvement in percentage of

Joined Q-ary Tree compared with the 2-ary tree and the 4-ary tree. The percentage of

improvement increases more slowly for GID-96 bits and SGTIN-96 bits schemes, once the

number of tags within the interrogation zone gets higher. On the other hand, for GIAI-96

bits encoding, there is no decrease in system efficiency in the Joined Q-ary Tree, but the

performance of the naive trees increase once the number of tags increase. Since the Joined

Q-ary Tree accumulated least Bits Length out of the three approaches, regardless of the

encoding scheme and the number of tags within the interrogation zone, it can now be

concluded that the best performing approach is Joined Q-ary Tree.

4.6 Overall Analysis

A total of five experiments were conducted for the tree-based anti-collision approaches.

The first three experimental evaluations were to verify the performance and capability

of our proposed Unified Q-ary Tree, and the remaining two experiments were to proof

the concept of the Joined Q-ary Tree. From experiment one to three, we determined

that out of twelve combinations of Unified Q-ary Trees, the Unified 2-ary & 4-ary tree

performed the best overall, in terms of system robustness that preserves memories usage

during identification process. We then verified that by using a 2-ary tree for Identical bits

of EPC, and by using a 4-ary tree for Unique bits of EPC, the Total number of bits for

querying can be decreased.

In addition to the first three experiments, the fourth and fifth demonstrate that the best

performing approach is the Joined Q-ary Tree, using GID-96 bits encoding scheme. The

results acquired have shown that the Joined Q-ary Tree has far more superior performance

compared with our proposed Unified Q-ary Tree and existing Naive Q-ary Trees. Moreover,

86

4.6. OVERALL ANALYSIS

Figure 4.19: Percentage of improvement of Joined Q-ary Tree compared with Naive 2-aryTree and Naive 4-ary Tree, using different Encoding Scheme: a) GID-96 bits, b) SGTIN-96bits, and c) GIAI 96 bits

we also discovered that the Joined Q-ary Tree achieves the best performance, regardless

of the type of encoding scheme.

From the analysis of all experiments, we recognised certain properties of importance

for tree-based anti-collision methods, which are: 1) similarity of EPC pattern, 2) number

of tags within one group of the EPC pattern, and 3) overall number of tags within the

interrogation zone.

87


4.7 Summary

In this chapter, we have investigated the problems on existing deterministic anti-collision

schemes, and we proposed two new tree-based anti-collision methods in order to eliminate

shortcomings of existing techniques. The main contributions and findings of this Chapter

are as follows:

• We have proposed a Unified Q-ary Tree (Pupunwiwat and Stantic, 2009a), (Pupunwi-

wat and Stantic, 2009b), which is a combination of two Q-ary trees; and we identified

the best Q-ary tree for specific circumstances. We found that a 2-ary tree suits best

when the EPC bits are identical, and the use of 4-ary tree can be optimised when

the EPC bits are unique. The best combination of 2-ary and 4-ary trees are then

used for construction of a Joined Q-ary Tree.

• Based on findings from the Unified Q-ary Tree experiment, we then proposed a

Joined Q-ary Tree (Pupunwiwat and Stantic, 2010c) to further minimised the total

memories usage during the identification process. We discover that the Joined Q-ary

Tree performed the best compared with existing tree-based anti-collision techniques,

regardless of number of tags within the reader zone and the encoding scheme used.

• We have confirmed that the similarity of EPC pattern, the number of tags within

one group of the EPC pattern, and the overall number of tags within the inter-

rogation zone, have impacted on the performance of any tree-based anti-collision

schemes. Nevertheless, the best performing technique, in terms of memory usage

and robustness of the RFID system, are our proposed deterministic anti-collision

techniques.

88

5Probabilistic Anti-Collision Approaches

In this chapter, we tackle issues of existing probabilistic anti-collision schemes, such as the

amount of slots and frames produced during each identification process, and the perfor-

mance efficiency. Firstly, we introduce a Precise Tag Estimation Scheme (PTES) (Pupun-

wiwat and Stantic, 2010a), (Pupunwiwat and Stantic, 2010b) to minimise the number

of slots and frames queried by the RFID reader, and to maximise the system efficiency.

Secondly, we introduce the Probabilistic Cluster-Based Technique (PCT) (Pupunwiwat

and Stantic, 2010d) anti-collision method to improve the performance of tag recognition

process and provide a sufficient performance over existing methodologies. Finally, the

remaining of this chapter comprise the mathematic fundamental for probabilistic anti-

collision schemes, the foundations of the proposed PTES and PCT methods, and the

experimental evaluation.

5.1 Mathematic Fundamental for ALOHA-based Tag Estimation

In the Framed-Slotted ALOHA based probabilistic scheme, to estimate the number of

present tags, Binomial distribution is a good fundamental method. For a given initial

Q in a frame with F slots and n tags, the expected value of the number of slots with

occupancy number x is as follows:

ax = n× Cxn(

1

F)x(1− 1

F)n−x

89

CHAPTER 5. PROBABILISTIC ANTI-COLLISION APPROACHES

Therefore, the expected number of Empty slot e, Successful slot s, and Collision slot c

is given by the following equations:

e = a0 = F (1− 1

F )n

s = a1 = n(1− 1F )n−1

c = ak = F − a0 − a1

Thus, the system efficiency (E) is defined as the ratio between the number of Successful

slot and the frame-size, as per the following equations:

E =s

F=

n(1− 1F )n−1

F= n

1

F(1− 1

F)n−1

It has been proven that the highest efficiency can be obtained if the frame-size F is

equal to the number of tags n, provided that all slots have the same fixed length:

F (optimal) = n

Therefore, we make the assumption that by keeping the number of tags close to the

available frame-size, the optimal performance efficiency can be obtained. According to

literatures, it is possible to achieve the theoretically optimal efficiency of 36.8 percent in

ALOHA-based systems.

5.2 Precise Tag Estimation Scheme

A frame-size prediction stage is one of the most crucial processes that determine the

performance of the probabilistic anti-collision technique. In order to overcome shortcom-

ings of existing methods for frame-size prediction, we propose a Precise Tag Estimation

Scheme (PTES) that is compatible with any ALOHA-based anti-collision protocols. The

aims of PTES are to obtain optimal tunable parameters that produce minimum number of

frames and slots; and to find the impact of collision slots and empty slots toward Backlog

estimation. After obtaining initial results for PTES, the optimal parameters found will

be incorporated within our proposed PCT, in order to further improve the performance

efficiency.

This section will describe the newly proposed PTES; the specific requirements for tag

estimation; initial Q value; suggest frame-size; and sample tag estimation and allocation.

90

5.2. PRECISE TAG ESTIMATION SCHEME

5.2.1 Slot Observation and Initial Q Value

For general probabilistic anti-collision algorithm, the reader picks tag within an interroga-

tion zone by the command “Select”; then issues “Query”, which contains a ‘Q’ parameter

to specify the frame-size, [F = 2Q - 1]. For our PTES methodology, we have chosen initial

Q value to be any number between 1 and 15, giving enough maximum number of slots

of 32768 per frame. After the first round of identification, collision slots and empty slots

will be observed and used, to estimate remaining number of tags. After the number of

tags has been estimated, frame-size for the next identification round can be configured in

accordance to the suggested frame-size threshold.

5.2.2 Suggested Threshold for Frame-Size

The suggested frame-size threshold to be used in our methodology is set according to

estimated number of tags. For example, if the estimated number of tags is 100 tags, the

suggested frame-size would have a Q value of 7. Since the frame-size is calculated by 2Q

- 1, the frame-size where Q = 7 will allow at most 128 tags (boundary 0 to 27 - 1) to

be identified. Therefore, if the estimated number of tags is between 65 and 128 tags, the

suggested initial Q would equal to 7.

Table 5.1 shows Minimum and Maximum number of tags allowed per suggested frame-

size, and Minimum and Maximum boundary of random numbers generated per frame-size.

Maximum number of tags allowed in each frame-size is calculated by 2Q and minimum

number of tags allowed is calculated by 2Q−1 + 1. The maximum frame-size boundary

is calculated by 2Q - 1, while the minimum frame-size boundary is always 0. The Table

only demonstrates up to Q = 15.

Table 5.1: Suggested frame-size boundary (B) and minimum and maximum number oftags (NT) for specific estimated number of tags

Q = 1 Q = 2 Q = 3 Q = 4 Q = 5NT B NT B NT B NT B NT B

Min 1 0 3 0 5 0 9 0 17 0Max 2 1 4 3 8 7 16 15 32 31


Min 33 0 65 0 129 0 257 0 513 0Max 64 63 128 127 256 255 512 511 1024 1023


Min 1025 0 2049 0 4097 0 8193 0 16385 0Max 2048 2047 4096 4095 8192 8191 16384 16383 32768 32767

91


5.2.3 PTES approaches

In this chapter, we propose three tag estimation methods for a Precise Tags Estimation

Scheme (PTES). In method one - PTES[C], we use a fixed parameter to calculate collision

slot and use a variable to predict empty slot for the next round of identification. In method

two - PTES[CE], we use two variables to predict collision slot and empty slot for the new

identification round. Finally, in method three - PTES[CCE], we use a fixed parameter to

calculate collision slot for the first round of identification, then we use two variables to

predict collision slot and empty slot from the second identification round onward.

The aims of all PTES methods are to clarify that both collision slots and empty slots

have an impact on Backlog prediction; and that more than one variable can be used to

predict frame-size for an upcoming round effectively, depending on the chosen Initial Q.

The three methods are explained within this sub-section.

5.2.3.1 PTES[C]

A PTES[C] uses various parameters to predict collision slots for the new identification

round. PTES[C] method aims to obtain the optimal parameter in order to calculate and

predict the closest number of remaining tags for the next round of identification. We

assume that for the current identification round, each collision slot has at least two tags

collided. However, it is impossible to distinguish certain number of tags that actually

caused the collision. There is exactly one tag per successful slot, therefore, we do not take

successful slots into consideration. In addition, an empty slot does not engage any tag.

Accordingly, we also do not take empty slots into consideration in this method. PTES[C]

focuses on finding optimal parameters to calculate and predict the number of collision

slots for the next identification round.

The PTES[C] method uses different parameter between 2.0 and 3.0 to predict the

number of collision slots. Since a collision slot engages at least two tags, we assume that

the parameter for collision slots calculation falls between 2.0 and 3.0 (more than two tags

but possibly less than three tags). However, in reality, the number of tags per collision

slot can be more than three tags. According to Schoute’s method, which is a simple and

accurate Backlog estimation technique, the parameter 2.39 for collision slots prediction is

used. Therefore, we select our collision slots variable to be between 2.0 and 3.0.

Equation 5.1 shows Backlog estimation using variable 2.0 <= V1 <= 3.0 for collision

slots prediction. The variable V1 is the tunable parameters to predict the number of

remaining tags, using information of collision slots from the previous frame.

Backlog = d(V1 × c)e (5.1)

92


Where c is the number of Collision Slot ; and V1 is variable between 2.0 and 3.0 with

increments of 0.1.

Backlog = d( 2.0 ∗ c )e


.......


Therefore, there are eleven possible optimal V1 for PTES[C] method. The estimated

number of slots is rounded-up to the nearest integer.

Algorithm 1 demonstrates the PTES[C] algorithm applied for the number of Backlog

estimation, and either keep the current frame-size or re-adjust the frame-size for the the

next identification cycle.

Input: c = Collision Slots, BacklogOutput: Q = Frame− Size Adjustmentfor (Frame-Size prediction procedure) do

Backlog = (V1 * c);while Looking up Suggested Threshold for Frame-Size Table do

if Found Matched Q for specific Backlog thenRe-adjust Q Value;

end

endOutput Q Adjust Value;

endAlgorithm 1: PTES(C) Algorithm

5.2.3.2 PTES[CE]

Similar to PTES[C] method, the PTES[CE] method aims to obtain the optimal parameter

in order to calculate and predict the closest Backlog for the next identification round. We

assume that for the current identification round, each collision slot has at least two tags

collided. However, we cannot know for sure how many tags actually caused the collision.

There is exactly one tag per successful slot, thus, we do not take successful slots into

consideration. On the other hand, we assume that empty slots will continuously occur

during the next rounds of identification despite the frame-size. Thus, PTES[CE] method

is created to find the optimal parameter and to predict the number of remaining tags

for upcoming round, using information from both collision slots and empty slots of the

current frame.

The PTES[CE] method uses any variable between 2.0 and 3.0 to predict the number

of collision slots. Variables between 0.1 and 0.9 are also used to predict the number of

93


2.0c, 0.1e 2.0c, 0.2e 2.0c, 0.3e 2.0c, 0.4e 2.0c, 0.5e 2.0c, 0.6e 2.0c, 0.7e 2.0c, 0.8e 2.0c, 0.9e2.1c, 0.1e 2.1c, 0.2e 2.1c, 0.3e 2.1c, 0.4e 2.1c, 0.5e 2.1c, 0.6e 2.1c, 0.7e 2.1c, 0.8e 2.1c, 0.9e2.2c, 0.1e 2.2c, 0.2e 2.2c, 0.3e 2.2c, 0.4e 2.2c, 0.5e 2.2c, 0.6e 2.2c, 0.7e 2.2c, 0.8e 2.2c, 0.9e2.3c, 0.1e 2.3c, 0.2e 2.3c, 0.3e 2.3c, 0.4e 2.3c, 0.5e 2.3c, 0.6e 2.3c, 0.7e 2.3c, 0.8e 2.3c, 0.9e2.4c, 0.1e 2.4c, 0.2e 2.4c, 0.3e 2.4c, 0.4e 2.4c, 0.5e 2.4c, 0.6e 2.4c, 0.7e 2.4c, 0.8e 2.4c, 0.9e2.5c, 0.1e 2.5c, 0.2e 2.5c, 0.3e 2.5c, 0.4e 2.5c, 0.5e 2.5c, 0.6e 2.5c, 0.7e 2.5c, 0.8e 2.5c, 0.9e2.6c, 0.1e 2.6c, 0.2e 2.6c, 0.3e 2.6c, 0.4e 2.6c, 0.5e 2.6c, 0.6e 2.6c, 0.7e 2.6c, 0.8e 2.6c, 0.9e2.7c, 0.1e 2.7c, 0.2e 2.7c, 0.3e 2.7c, 0.4e 2.7c, 0.5e 2.7c, 0.6e 2.7c, 0.7e 2.7c, 0.8e 2.7c, 0.9e2.8c, 0.1e 2.8c, 0.2e 2.8c, 0.3e 2.8c, 0.4e 2.8c, 0.5e 2.8c, 0.6e 2.8c, 0.7e 2.8c, 0.8e 2.8c, 0.9e2.0c, 0.1e 2.9c, 0.2e 2.9c, 0.3e 2.9c, 0.4e 2.9c, 0.5e 2.9c, 0.6e 2.9c, 0.7e 2.9c, 0.8e 2.9c, 0.9e3.0c, 0.1e 3.0c, 0.2e 3.0c, 0.3e 3.0c, 0.4e 3.0c, 0.5e 3.0c, 0.6e 3.0c, 0.7e 3.0c, 0.8e 3.0c, 0.9e

Figure 5.1: Variable V1 and V2 for Collision slot and Empty slot calculation for PTES[CE]method. There are ninety-nine possible combinations of V1 and V2, in order to find optimalparameters for c and e prediction

empty slots for the upcoming round. Since an empty slot does not engage any tag, we

assume that the parameter for empty slots calculation will fall between 0.1 and 0.9 (no

more than one tag). Equation 5.2 shows Backlog estimation using variable V1 for collision

slots prediction and variable V2 for empty slots prediction. Both variables V1 and V2 are

tunable parameters to predict the number of remaining tags, using information of collision

slots and empty slots from the previous frame.

Backlog = d(V1 × c+ V2 × e)e (5.2)

where c is the number of Collision Slot ; e is the number of Empty slot ; V1 is variable

between 2.0 and 3.0; and V2 is variable between 0.1 and 0.9 with increments of 0.1.

Backlog = d( 2.0 ∗ c + 0.1 ∗ e )e

.......

Backlog = d( 2.0 ∗ c + 0.9 ∗ e )e

Backlog = d( 2.1 ∗ c + 0.1 ∗ e )e

.......

.......

Backlog = d( 3.0 ∗ c + 0.9 ∗ e )e

Therefore, there are ninety-nine possible optimal V1 and V2 variables for this method.

The estimated number of slots is rounded-up to the nearest integer.

Figure 5.1 shows ninety-nine possible optimal V1 and V2 variables for c and e.

94


Algorithm 2 demonstrates the PTES[CE] algorithm applied for the number of Backlog

estimation; and either keep the current frame-size or re-adjust the frame-size for the the


Input: c = Collision Slots, e = Empty Slots, BacklogOutput: Q = Frame− Size Adjustmentfor (Frame-Size prediction procedure) do

Backlog = (V1 * c + V2 * e);while Looking up Suggested Threshold for Frame-Size Table do


end


endAlgorithm 2: PTES(CE) Algorithm

5.2.3.3 PTES[CCE]

The PTES[CCE] method uses parameter 2.0 to predict the number of collision slots after

the first round of identification. Parameter 2.0 is chosen according to the assumption

that at least two tags collided per collision slot. Since the number of tags is supposedly

unknown at the beginning, a simple frame-size prediction using variable 2.0 is chosen for

the next Q adjust value. Equation 5.1 with variable V1 = 2.0 is applied for collision slots

prediction.

Backlog = d(2.0× c)e

Where c is the number Collision Slot.

The PTES[CCE] uses Equation 5.2 to predict the number of collision slots and empty

slots from second round onward. Equation 5.2 shows Backlog estimation using variable

V1 for collision slots prediction and variable V2 for empty slots prediction.

Backlog = d(V1 × c+ V2 × e)e

where c is the number of Collision Slot ; e is the number of Empty slot ; V1 is variable

between 2.0 and 3.0; and V2 is variable between 0.1 and 0.9 with increments of 0.1.

Algorithm 3 demonstrates the PTES[CCE] algorithm applied for the number of Backlog

estimation; and either keep the current frame-size or re-adjust the frame-size for the the


95


Input: c = Collision Slots, e = Empty Slots, Backlog, RoundcountOutput: Q = Frame− Size Adjustmentfor (Frame-Size prediction procedure) do

if Roundcount == 0 thenBacklog = (2.0 * c);

endelse

Backlog = (V1 * c + V2 * e);endRoundcount = Roundcount + 1;while Looking up Suggested Threshold for Frame-Size Table do


end


endAlgorithm 3: PTES(CCE) Algorithm

Table 5.2 displays the comparison between our three proposed PTES methods. From

the Table, the PTES[C] only uses variable V1 for tag estimation while the other two

PTES use both V1 and V2. The difference between PTES[CE] and PTES[CCE] is in the

first round of tag prediction where PTES[CCE] introduces discrete estimation, using only

variable V1 for the first round of identification; then from the second round all procedures

are the same as in PTES[CE].

Table 5.2: PTES methods comparisonMethod Round Variable V1 Variable V2

PTES[C] All 2.0 - 3.0 N/APTES[CE] All 2.0 - 3.0 0.1 - 0.9

PTES[CCE] First 2.0 N/ASecond onward 2.0 - 3.0 0.1 - 0.9

5.2.4 Sample Tag Estimation and Allocation

This section describes a sample tag allocation and estimation for all three PTES methods.

For instance, there are twenty tags to be identified. However, while performing the prob-

abilistic anti-collision algorithm, the number of tags is supposedly unknown. The initial

Q value for this example is set to 4; thus, the number of available slots for the first round

of identification is equal to 16 (0 to 24 - 1).

Figure 5.2 shows a sample of first round tag allocation, where seven collision slots,

four empty slots, and five successful slots occurred. For each collision slot, two or more

tags collided while an empty slot engaged no tag. Each successful slot holds exactly one

tag per slot. After the first round of tag allocation, PTES equations are applied, in order

to find an estimated frame-size for the next round.

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Figure 5.2: A sample first round of tag allocation with Initial Q of 4. Collision slot c = 7,Empty slot e = 4, and Successful slot s = 5

5.2.4.1 Sample Tag Estimation - PTES[C]

After the first round of identification shown in Figure 5.2, we applied PTES[C] method

with variable V1 between 2.0 to 3.0, to estimate collision slots for the upcoming round.

The actual remaining tags from this round are fifteen tags. For instance, after applying

Equation 5.1 using V1 = 2.2, number of estimated tags for the next round can be calculated

as follows:

Backlog = d( 2.2 ∗ 7 )e = 15

Therefore, the estimated number of tags for the next round is equal to fifteen tags.

Hence, the new Q adjust is equal to 4 (see Table 5.1 for suggested frame-size).

Nevertheless, if different variable V1 is used, number of estimated tags and the new Q

adjust would be different, as shown in Table 5.3.

Table 5.3: Sample tag estimation and frame-size (Q) adjustment after the first round ofidentification, using PTES[C] method

Round one (c = 7)Variable (V1) Tag Estimation Q Adjust

2.0 14 42.1 15 42.2 15 42.3 16 42.4 17 52.5 18 52.6 18 52.7 19 52.8 20 52.9 20 53.0 21 5

Subsequent to the first round of identification, according to Table 5.3, the adjustment

of Q value for parameters V1 = 2.0 - 2.3 is equal to 4, while the Q value for V1 = 2.4 -

3.0 is equal to 5. In order to identify all tags within the interrogation zone, PTES[C] with

variable V1 is applied until no more collision occurs. Corresponding to Figure 5.3, we can

97


see that the second round of identification split into two using Q adjust of 4 and 5.

Figure 5.3: A sample of Q-adjust in each round of identification until all tags are identified

Table 5.4: Sample tag estimation and frame-size (Q) adjustment after the second roundof identification, using PTES[C] method

Round two (c = 4)Variable (V1) Tag Estimation Q Adjust

2.0 8 32.1 8 32.2 9 42.3 9 4

Round two (c = 3)Variable (V1) Tag Estimation Q Adjust

2.4 7 32.5 8 32.6 8 32.7 8 32.8 8 32.9 9 43.0 9 4

Table 5.4 shows the second round of identification. After the second round, the Q

adjust for variable V1 = 2.0, 2.1, 2.4, 2.5, 2.6, 2.7, and 2.8 is equal to 3, while the Q adjust

for variable V1 = 2.2, 2.3, 2.9, and 3.0 is equal to 4. Corresponding to Figure 5.3, we can

see that the third round of identification split further into four Q adjust.

Similar to the first two rounds of identification, Table 5.5 shows the third round of

identification. In this round, some tags identification are completed using variable V1 =

2.9 and 3.0. However, identification using variable V1 between 2.0 to 2.8 required further

recognition. Q adjust for all variable except V1 = 2.2 is equal to 3. Figure 5.3 shows that

the fourth round of identification split further into four Q adjust.

More identification is completed within the fourth round as shown in Table 5.6. Only

variable V1 between 2.0 to 2.2 required further identification. Figure 5.3 shows that the

fifth round only split into two Q adjust.

In the last round of identification, all tags can be recognised using variable V1 = 2.0,

2.1, and 2.2; as shown in Table 5.7.

98


Table 5.5: Sample tag estimation and frame-size (Q) adjustment after the third round ofidentification, using PTES[C] method

Round three (c = 3)Variable (V1) Tag Estimation Q Adjust

2.0 6 32.1 6 3


2.2 4 22.3 5 32.4 5 32.5 5 32.6 5 32.7 5 32.8 6 3


2.9 0 Completed3.0 0 Completed

Table 5.6: Sample tag estimation and frame-size (Q) adjustment after the fourth roundof identification, using PTES[C] method

Round four (c = 1)Variable (V1) Tag Estimation Q Adjust

2.0 2 12.1 2 12.2 2 1

Round four (c = 0)Variable (V1) Tag Estimation Q Adjust

2.3 0 Completed2.4 0 Completed2.5 0 Completed2.6 0 Completed2.7 0 Completed2.8 0 Completed

Table 5.7: Sample tag estimation and frame-size (Q) adjustment after the fifth round ofidentification, using PTES[C] method

Round five (c = 0)Variable (V1) Tag Estimation Q Adjust

2.0 0 Completed2.1 0 Completed2.2 0 Completed

Since samples tag identification using PTES[C] mostly explain how our proposed meth-

ods estimated number of tags in each round, we will show the actual tag allocation using

PTES[CE] in the next sub-section.

99


5.2.4.2 Sample Tag Allocation - PTES[CE]

After the first round of identification shown in Figure 5.2, PTES[CE] method uses variable

V1 between 2.0 to 3.0, to estimate collision slots; and variable V2 between 0.1 to 0.9, to

estimate empty slots for the next round. The actual remaining tags from this round are

fifteen tags. For example, after applying Equation 5.2 using V1 = 2.0 and V2 = 0.5,

number of estimated tags for the next round can be calculated as follows:

Backlog = d( 2.0 ∗ 7 + 0.5 ∗ 4 )e = 16

Therefore, the estimated number of tags for the next round is equal to sixteen tags.

Hence, the new Q adjust is equal to 4 (see Table 5.1 for suggested frame-size).

Following the first round, the adjustment of Q value for estimated tag between 12 and

16 is equal to 4, while the Q value for estimated tag between 17 and 25 is equal to 5. In

order to identify all tags within the interrogation zone, PTES[CE] with variable V1 and

V2 is applied for each identification round until no more collision occurs and all tags are

identified.

Figure 5.4: A sample second round of tag allocation with Initial Q of 4, V1 = 2.0, and V2

= 0.5. Collision slot c = 2, Empty slot e = 5, and Successful slot s = 9

Figures 5.4, 5.5, and 5.6 show examples of further identification process using variables

V1 = 2.0 and V2 = 0.5. Figure 5.4 shows a sample of second round tag allocation where two

collision slots, five empty slots, and nine successful slots, occurred. The actual remaining

tags from this round are six tags. The number of estimated tag for round three can be

calculated as follows:

Backlog = d( 2.0 ∗ 2 + 0.5 ∗ 5 )e = 7

Therefore, the estimated number of tags for the next round is equal to seven tags. The

new Q adjust is equal to 3.

Figure 5.5 shows a sample of third round tag allocation where one collision slot, three

empty slots, and four successful slots, occurred. The actual remaining tags from this round

are two tags. The number of estimated tag for round three can be calculated as follows:

100


Figure 5.5: A sample third round of tag allocation with Initial Q of 3, V1 = 2.0, and V2

= 0.5. Collision slot c = 1, Empty slot e = 3, and Successful slot s = 4

Backlog = d( 2.0 ∗ 1 + 0.5 ∗ 3 )e = 4

The estimated number of tags for the next round is equal to four tags. Therefore, the

new Q adjust is equal to 2.

Figure 5.6: A sample fourth (final) round of tag allocation with Initial Q of 2, V1 = 2.0,and V2 = 0.5. Collision slot c = 0, Empty slot e = 2, and Successful slot s = 2

Figure 5.6 shows a sample of final round tag allocation where no collision slot, two

empty slots, and two successful slots, occurred. There are no more tag remaining since no

collision occurred; thus, the identification process using probabilistic anti-collision algo-

rithm terminated after this round.


In order to show the significance of our proposed PTES methods, we conducted two

experimental evaluations and compared our methods with existing techniques.

5.2.5.1 Preliminary

To study the Precise Tag Estimation Scheme, all experiments are assumed to be set up

in a well-controlled environment where there is no metal or water nearby. We randomly

generated all data sets with assumptions that a UHF RFID reader is used and passive

RFID tags are attached to each item. At this stage, we assume that all items are static

and no other type of interference beside collision itself is presented. It is also assumed

that other type of data stream errors, such as data duplication, have been filtered at the

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earlier stage. Different tag sets are simulated for each experiment. While performing each

anti-collision algorithm, number of tags is supposedly unknown. We performed ten runs

on each test case and presented the average results.

5.2.5.2 Experiment One Data Set

The aim of the first experiment is to find the impact on system efficiency of different initial

Q used by PTES method versus existing methods. There are 200 and 300 tags utilised

in the experiment. Different initial Q of 6, 7, 8 and 9 are applied on each tag set. The

data sets and initial Q parameters are selected, based on the supposition of the capability

of the UHF RFID reader and passive tags read rates. Table 5.8 demonstrates the type

of Backlog prediction applied to different number of tags, different initial Q, and tunable

parameters. All methods are applied separately to different randomly generated data sets,

giving a total of 1688 test cases within this experiment.

Table 5.8: Chosen Parameters for Experiment OneBacklog Prediction No. Tags V1 V2 Initial Q Total Test Case

PTES[C] 200 - 300 2.0 - 3.0 - 6 - 9 88PTES[CE] 200 - 300 2.0 - 3.0 0.1 - 0.9 6 - 9 792

PTES[CCE] 200 - 300 2.0 - 3.0 0.1 - 0.9 6 - 9 792Shoute 200 - 300 2.39 - 6 - 9 8

Lowerbound 200 - 300 2.0 - 6 - 9 8

5.2.5.3 Experiment Two Data Set

The second experiment is to find the optimal parameters of PTES that produce the min-

imal number of slots and frames, and generate the highest system efficiency, compared

with the existing methods. There are five tag sets comprising 100, 200, 300, 400, and 500

tags. The initial Q of this experiment is fixed to 8. Table 5.9 displays the type of Backlog

prediction applied to different number of tags, fixed initial Q, and tunable parameters. All

methods are applied separately to different randomly generated data sets, giving a total

of 1055 test cases within this experiment.

Table 5.9: Chosen Parameters for Experiment TwoBacklog Prediction Initial Q V1 V2 No. Tags Total Test Case

PTES[C] 8 2.0 - 3.0 - 100 - 500 55PTES[CE] 8 2.0 - 3.0 0.1 - 0.9 100 - 500 495

PTES[CCE] 8 2.0 - 3.0 0.1 - 0.9 100 - 500 495Schoute 8 2.39 - 100 - 500 5

Lowerbound 8 2.0 - 100 - 500 5

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5.2.6 Results

This section presents the evaluation of impacts of different initial Q on chosen frame-size

prediction methods, and demonstrates the impacts of different number of tags within an

interrogation zone toward anti-collision approaches. These results are displayed as follows:

5.2.6.1 Results - Impacts of Different Initial Q

In the first experiment, we compared our PTES algorithm with Schoute (Sch) and Lower-

bound (LB) methods. In accordance to our surveys, the two methods are simple and give

accurate Backlog prediction. We divide our results into two parts. In the first part, we

present results on PTES[C] method; and for the second part, we demonstrate results on

PTES[CE] and PTES[CCE] methods. We present results separately for PTES[C] because

it is the only method that involved single parameter (V1), while the other two PTES

methods occupied both parameters V1 and V2.

Figure 5.7: Performance efficiency of PTES[C], Sch, and LB methods, using differentInitial Q: a) PTES[C] 200 tags and b) PTES[C] 300 tags

Part I Experiment results, as shown in Figure 5.7, illustrate that different initial Q have

individual impact on system efficiency for PTES[C] method. All PTES[C]’s parameters

(V1 2.0 to 3.0) are displayed in the figure. Considering different number of tags (See Figure

5.7a and 5.7b), the results show that initial Q of 8 is the optimal Q for all approaches.

According to both figures, the maximum performance efficiency using different methods,

including Sch, LB, and PTES[C] methods, can be achieved when the initial Q is set to 8.

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Theoretically, the optimal performance efficiency can be achieved when the number of

tags is equal to the size of the frame length. This is proven to be true in Figure 5.7a),

where the optimal performance was reached with initial Q of 8 regardless of any method

applied. When the number of tags is equal to 200 tags, Q = 8 is the best candidate since

it is the nearest frame length available. Similarly, when the number of tags increased to

300 tags, initial Q of 8 is still the best option as it has the closest frame length.

Figure 5.8: Performance efficiency of PTES[CE], PTES[CCE], Sch, and LB methods, usingdifferent Initial Q: a) PTES[CE] 200 tags, b) PTES[CE] 300 tags, c) PTES[CCE] 200 tagsand d) PTES[CCE] 300 tags

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Part II From Figure 5.8, it can be seen that different initial Q also have individual

impact on system efficiency for both PTES[CE] and PTES[CCE] methods. The best

parameters V1 between 2.0 to 2.5 and V2 between 0.1 to 0.2 are displayed in the figure.

Similar to PTES[C], Figure 5.8 illustrates that the maximum performance efficiency using

different methods (Sch, LB, PTES[CE], and PTES[CCE]) can be achieved when the initial

Q is 8. In addition, it is noticeable that most variables of PTES[CCE] perform more

stably compared with the PTES[CE] approach. For instance, Figure 5.8c) shows more

lines overlap than Figure 5.8a), which means that all parameters of PTES[CCE] generate

more constant results than the PTES[CE].

The challenge of choosing the initial Q is to take into consideration the amount of

actual tags, which will be presented to the reader at the beginning of the read cycle.

From results of both parts of the first experiment, we verified that the initial Q of 8 is

the most suitable Q on average for our proposed PTES methods. Nevertheless, the

selected initial Q is mainly appropriate for PTES method that is incorporated with any

probabilistic anti-collision approach, without grouping strategy. For other probabilistic

anti-collision methods that involved grouping rules, such as EDFSA and PCT, different

initial Q may be more suitable for specific tag groups.

5.2.6.2 Results - Impacts of Different Number of Tags

The second experiment verifies that different number of tags also have impacts on perfor-

mances of each anti-collision techniques. Initial Q of 8 is used in this experiment since

it gives maximum performance efficiency according to the first experiment. We compared

our PTES algorithm with Sch method since it is simple and gives accurate Backlog predic-

tion. The LB method is not considered in this experiment due to the excessive number of

frames required from initial test case compared with Schoute method. In this experiment,

we also divide our results into two parts. In the first part, we present results on PTES[C]

method; and for the second part, we demonstrate results on PTES[CE] and PTES[CCE]

methods.

Part I To measure the performance efficiency of PTES[C] approach, we performed test-

ing on data sets of 100 to 500 tags, and compared the results against Sch method. From

Table 5.10, it can be seen that when the number of tags is equal to 200 tags, every anti-

collision methods achieved highest system efficiency. Relatively to the first experiment,

this result also validates the Binomial distribution fundamental where the optimal effi-

ciency can be obtained if the frame-size is equal to the number of tags. Nevertheless,

for 200 to 500 tags, all methods maintain their system efficiency above 30 percent. The

Table also demonstrates that the optimal parameters V1 for PTES[C] is 2.5, where these

parameters give highest system efficiency compared with other variables. In addition, pa-

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Table 5.10: Performance efficiency of PTES[C], Sch, and LB methods, using Initial of 8on different sets of tags

EfficiencyAnti-Collision Approaches 100 tags 200 tags 300 tags 400 tags 500 tags

PTES[C]: 2.0 0.28409 0.37037 0.33557 0.35273 0.35461PTES[C]: 2.1 0.28409 0.36496 0.33557 0.33167 0.35461PTES[C]: 2.2 0.27174 0.36765 0.33259 0.33167 0.35511

PTES[C]: 2.3 0.26882 0.36765 0.33186 0.32051 0.35511PTES[C]: 2.4 0.26882 0.36765 0.33186 0.32051 0.35511PTES[C]: 2.5 0.26882 0.37313 0.33186 0.32051 0.35511PTES[C]: 2.6 0.26882 0.37313 0.34247 0.32051 0.32468PTES[C]: 2.7 0.26882 0.37313 0.34247 0.32051 0.32468PTES[C]: 2.8 0.27473 0.37313 0.34247 0.31847 0.32468PTES[C]: 2.9 0.27473 0.37313 0.34091 0.31847 0.32468PTES[C]: 3.0 0.27473 0.36765 0.34091 0.31847 0.30788

Schoute 0.26882 0.36765 0.33186 0.32051 0.35511

rameters V1 of 2.3 and 2.4 give equivalent efficiency for all tag sets compared with Sch

method. Since Sch method uses 2.39 for variable V1, it is rational for parameters 2.3 and

2.4 to perform correspondingly to Sch method. However, our results show that 2.5 is the

optimal V1 parameter, which contradict with Sch method that claims 2.39 as its optimal

value. We verify this result as dependent upon the chosen initial Q in our experiment.

However, it can be assumed that Sch method considers its performance toward all Qs,

while we only consider optimal Q in our case. Since initial Q is tunable to the user’s

favour, we decided that it is only necessary to find optimal V1 for specific initial Q.

Figure 5.9: Performance efficiency (a) and Number of frames (b) of PTES[C] (V1 = 2.3to 2.5) versus Sch methods, using Initial Q of 8 on different tag sets

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After considering performance efficiency, we also look into total number of frames

produced by each method. The number of frames determines the initiative time in each

identification cycle; the higher the number of frame means the longer identification delay.

According to Figure 5.9b), it can be seen that PTES[C] with the optimal parameter

V1 of 2.5 also used the lowest number of frames for all number of tags. Judging from

both performance efficiency (Figure 5.9a) and number of frames (Figure 5.9b) queried, we

conclude that PTES[C] with optimal parameters V1 = 2.5 and optimal initial Q of 8 can

achieve the best system efficiency compared with Sch method.

Table 5.11: Performance efficiency of PTES[CE], PTES[CCE], Sch, and LB methods,using Initial of 8 on different sets of tags

EfficiencyAnti-Collision Approaches 100 tags 200 tags 300 tags 400 tags 500 tags

PTES[CE]: 2.0, 0.1 0.27174 0.36765 0.33186 0.35211 0.35511PTES[CE]: 2.0, 0.2 0.24038 0.36765 0.33186 0.33333 0.34530PTES[CE]: 2.1, 0.1 0.26882 0.36765 0.33186 0.31847 0.35511PTES[CE]: 2.1, 0.2 0.24038 0.37879 0.31780 0.31646 0.34530PTES[CE]: 2.2, 0.1 0.27473 0.36765 0.33186 0.31847 0.34626PTES[CE]: 2.2, 0.2 0.24038 0.36765 0.31780 0.31646 0.30637

PTES[CE]: 2.3, 0.1 0.27473 0.37313 0.34091 0.31847 0.36550PTES[CE]: 2.3, 0.2 0.24038 0.36765 0.32328 0.31646 0.29481PTES[CE]: 2.4, 0.1 0.27473 0.37313 0.34091 0.31847 0.30788PTES[CE]: 2.4, 0.2 0.24038 0.36765 0.32328 0.31646 0.29481PTES[CE]: 2.5, 0.1 0.27473 0.37313 0.32328 0.31847 0.30788PTES[CE]: 2.5, 0.2 0.24038 0.34722 0.32328 0.31646 0.29481

PTES[CCE]: 2.0, 0.1 0.27174 0.36765 0.33186 0.35211 0.35511PTES[CCE]: 2.0, 0.2 0.27473 0.36765 0.33186 0.33333 0.34530PTES[CCE]: 2.1, 0.1 0.26882 0.36765 0.33186 0.33333 0.35511PTES[CCE]: 2.1, 0.2 0.27174 0.37879 0.32328 0.32895 0.34530PTES[CCE]: 2.2, 0.1 0.27473 0.36765 0.33186 0.33333 0.34626PTES[CCE]: 2.2, 0.2 0.27174 0.36765 0.32328 0.32895 0.30637

PTES[CCE]: 2.3, 0.1 0.27473 0.37313 0.34091 0.33333 0.36550PTES[CCE]: 2.3, 0.2 0.27174 0.36765 0.32328 0.32895 0.29481PTES[CCE]: 2.4, 0.1 0.27473 0.37313 0.34091 0.33333 0.30788PTES[CCE]: 2.4, 0.2 0.27174 0.36765 0.32328 0.32895 0.29481PTES[CCE]: 2.5, 0.1 0.27473 0.37313 0.32328 0.33333 0.30788PTES[CCE]: 2.5, 0.2 0.27174 0.36765 0.32328 0.32895 0.29481

Schoute 0.26882 0.36765 0.33186 0.32051 0.35311

Part II From Table 5.11, it can be seen that for 200 to 500 tags, all methods including

Sch, PTES[CE], and PTES[CCE], maintain their performance efficiency above 30

percent. The Table also demonstrates that both PTES[CE] and PTES[CCE] with

parameters V1 and V2 equal to 2.3 and 0.1 respectively, give the highest system efficiency.

The two PTES methods perform equivalently for all data sets except for 400 tag sets.

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Specifically, looking closely at Figure 5.10a), we can see that PTES[CE] method

obtained lower efficiency for 400 tags compared with Sch method, despite the optimal

parameters used. On the other hand, we can see from Figure 5.10c) that the PTES[CCE]

still maintains its performance against Sch method regardless of number of tags. From

these results, we now summarise that for both PTES[CE] and PTES[CCE], parameters

V1 = 2.3 and V2 = 0.1 can achieve the best system efficiency. Nevertheless, the

PTES[CCE] has the most stable performance compared with other approaches.

Figure 5.10: Results of PTES[CE] and PTES[CCE] (V1 = 2.3, V2 = 0.1) versus Schmethods using Initial Q of 8 on different tag sets: Performance efficiency (a: PTES[CE],c: PTES[CCE]) and Number of frames (b: PTES[CE], d: PTES[CCE])

After considering performance efficiency, we are now looking into total number of

frames produced by each method. According to Figure 5.10b) and Figure 5.10d), it can

be seen that PTES[CE] and PTES[CCE] with optimal parameters, also used the lowest

number of frames for all number of tags. These results are far more superior, in terms of

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5.3. PROBABILISTIC CLUSTER-BASED TECHNIQUE

performance and number of frames required, compared with the performance of PTES[C]

discussed in Part I. This is due to the fact that the PTES[C] only used one variable

that only consider the impact of collision slots, while the other two PTES methods also

take into consideration the impact of empty slots from previous frames. Justified by both

system efficiency and number of frames, we conclude than PTES[CCE], with parameters

V1 = 2.3 and V2 = 0.1 and optimal initial Q of 8, is the best approach out of the three

proposed PTES. Therefore, PTES[CCE] approach is chosen as a frame-size prediction for

DFSA, EDFSA, and PCT methods for our PCT experiments.

5.3 Probabilistic Cluster-Based Technique

The PCT method employs a dynamic probabilistic algorithm concept, and uses group-

splitting rule, to split Backlog into group if the number of unread tags is higher than the

maximum frame-size.

The PCT approach first estimates the number of Backlog, or the remaining tags, within

the interrogation zone. If the number of Backlog is larger than the specific frame-size, it

splits the number of Backlog into a number of groups and allows only one group of tags to

respond. The reader then issues a “Query”, which contains a ‘Q’ parameter to specify the

frame-size (frame-size F(min) = 0; F(max) = 2Q - 1). Each selected tag in the group will

pick a random number between 0 to 2Q - 1 and put it into its slot counter. Only the tag

that picks zero as its slot counter responds to the request. When the number of estimated

Backlog is below the threshold, the reader adjusts the frame-size without grouping the

unread tags. After each read cycle, the reader estimates the number of Backlog using the

PTES algorithm and adjusts its frame-size.

5.3.1 Probabilistic Anti-Collision Algorithm using PTES

PCT approach first estimates the number of unread tags, then it decides if the number

of tags needs to be spliced or not. The probabilistic anti-collision algorithm using PTES

frame-size prediction is then applied to each selected group of tag.

Algorithm 4 demonstrates the probabilistic anti-collision algorithm applied to each

selected group of tags, where only one group of tags responds to the reader. There are

three kinds of slot:

1. Successful slot: Where there is only one tag reply, the reader sends ACK(RN16)

to a tag. The tag then backscatters its EPC to the reader and the reader issues

QueryRep for the next slot.

2. Empty slot: Where there is no tag reply, the reader then issues QueryRep for the

next slot.

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Reader sends Queryfor (Identification procedure) do

Every tags generate RN16 and slot counter;for (Current frame) do

if (Slot counter == 0) thenTag replies its RN16;if (A single tag replies) then

Reader sends ACK(RN16) to a tag;if (RN16 received by tag == RN16 tag saved data) then

Tag sends (EPC+PC+CRC) to reader;endReader sends QueryRep;

endelse if (Multiple tags reply) then

Reader sends QueryRep;endelse if (No tag replies) then

Reader sends QueryRep;end

endif (Tag receives QueryRep) then

slot counter = slot counter - 1;end

endReader uses PTES algorithm to adjust the size of the new frame;Reader sends QueryAdjust;

endAlgorithm 4: Probabilistic anti-collision algorithm with PTES Frame-Size Prediction

3. Collision slot: Where there is more than one tag reply, the reader then issues

QueryRep for the next slot.

After “QueryRep” command is received, each tag decreases its slot counter by 1. At

the end of each frame, the reader checks if all tags have been identified. Then, the reader

estimates the number of Backlog using PTES algorithm, and adjust its frame-size.

5.3.2 PCT Preliminary

Instead of splitting tags into group randomly, the PCT approach derived new rules using

particular equations, according to the optimal system efficiency obtained for specific num-

ber of tags. We first conducted an experiment to acquire optimal frame-size for specific

number of tags as shown in Figure 5.11. It can be seen that the optimal system efficiency

achieved by the probabilistic ALOHA method is approximately 38 percent and the opti-

mal number of tags is close to the maximum frame-size. Efficiency is calculated as shown

in Equation 5.3:

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Figure 5.11: Performance efficiency of different frame-size on different number of tags

Efficiency = (S

S + C + E) (5.3)

Where S is the number of Successful slots, C is the number of Collision slots, and E is

the number of Empty slots.

From the results acquired for performance efficiency evaluation, we have developed

equations 5.4, 5.5, 5.6, 5.7 and 5.8 to find a minimum and maximum number of tags

suitable for particular frame-size. These minimum and maximum numbers of tags are

derived to acquire the optimal performance efficiencies, as in Figure 5.11. Each equation

is then used to exploit rules for PCT.

To show in detail, the derivation of equations 5.4, 5.5, 5.6, 5.7 and 5.8, Table 5.12

demonstrates given information found from Figure 5.11, and all missing fields. From

Table 5.12, it is visible that at optimal system efficiency of 38 percent, the number of

tags is equal to the available frame-size calculated by 2Q. We have set the minimum and

maximum boundary efficiencies at 33 percent. The information on maximum number of

tags at 33 percent is also available from Figure 5.11. For example, when Q is equal to

8, the optimal percentage efficiency can be obtain at 256 tags and the number of tags at

maximum boundary is equal to 352 tags.

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max = 2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)

min = (2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4)) + 1 (5.4)

max = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−2) + 2(Q−3) − 2(Q−4) + 2(Q−5))

min = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + 1 (5.5)

max = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4))

min = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + 1 (5.6)

max = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4))

min = [(2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−2) + 2(Q−3) − 2(Q−4) + 2(Q−5))] + 1 (5.7)

max = (2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4))+

(2(Q−2) + 2(Q−3) − 2(Q−4) + 2(Q−5))

min = [(2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)) + (2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4))] + 1 (5.8)

Figure 5.12 illustrates the minimum and maximum boundaries and their correlated

percentage of efficiency for frame-size of 256. The figure shows that when the number of

tags equal 177 and 352, the percentage of efficiency is equal to 33 percent.

Table 5.12: Available Information and Missing fields on System Efficiency. MinB = Min-imum point of occurrence, MaxB = Maximum point of occurrence

Q MinB at 33% MaxB at 33% Optimal at 38%9 Unknown 704 5128 Unknown 352 2567 Unknown 176 1286 Unknown 88 645 Unknown 44 324 Unknown Unknown 163 Unknown Unknown 82 Unknown Unknown 41 Unknown Unknown 2

Table 5.13 demonstrates the derived answers for missing fields from Table 5.12. The

minimum boundary with 33 percent efficiency is calculated by the maximum boundary of

the previous frame plus 1. Thus, for Q8, the minimum boundary is equal to 177 (176 +

1). After finding all information needed, the reverse engineered equations for maximum

and minimum boundaries are derived for each Q. After we found all outcomes for each Q,

it is now possible to find the equation for two or more type of Qs.

In order to simplify the derived equations, we employ the use of β (Beta), κ (Kappa),

and µ (Mu), and assigned these three icons to express each rule. In this research, we

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Figure 5.12: The minimum and maximum boundaries and their correlated percentage ofefficiency for frame-size of 256

proposed three rules for PCT: PCT256, PCT128, and PCT-E (PCT-Extended). All rules

split the number of Backlog into groups then used one of initial Q8 (frame-size 256), Q7

(frame-size 128), or Q6 (frame-size 64), to identify a current set of tags. Equation 5.9

shows the conversion of all three key sets, from equation 5.4 to 5.8, into β, κ, and µ.

β = 2Q + 2(Q−1) − 2(Q−2) + 2(Q−3)

κ = 2(Q−1) + 2(Q−2) − 2(Q−3) + 2(Q−4)

µ = 2(Q−2) + 2(Q−3) − 2(Q−4) + 2(Q−5) (5.9)

From equation 5.4, 5.5, 5.6, 5.7 and 5.8, we derived three key sets within these equa-

tions. These key sets are converted into β, κ, and µ and applied into each PCT rule, as

shown in Table 5.14. Table 5.14 displays the conversion of equations 5.4 to 5.8, with the

minimum and maximum boundaries for each rule. For instance, Equation 5.4 is applied

to all three rules: PCT256, PCT128, and PCT-E. However, Equation 5.5 only apply to

PCT-E.

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Table 5.13: Derived Equations for Missing fields on System Efficiency. MinB = Minimumpoint of occurrence, MaxB = Maximum point of occurrence

MinB Reverse Engineered Calculation of the DerivedQ at 33% Equation for MinB Equation for MinB

9 353 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (256 + 128 - 64 + 32) + 1 = 3538 177 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (128 + 64 - 32 + 16) + 1 = 1777 89 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (64 + 32 - 16 + 8) + 1 = 896 45 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (32 + 16 - 8 + 4) + 1 = 455 23 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (16 + 8 - 4 + 2) + 1 = 234 12 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (8 + 4 - 2 + 1) + 1 = 123 6 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (4 + 2 - 1 + 0) + 1 = 62 4 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (2 + 1 - 0 + 0) + 1 = 41 2 (2(Q−1) + 2(Q−2) - 2(Q−3) + 2(Q−4)) + 1 (1 + 0 - 0 + 0) + 1 = 2

MaxB Reverse Engineered Calculation of the DerivedQ at 33% Equation for MaxB Equation for MaxB

9 704 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 512 + 256 - 128 + 64 = 7048 352 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 256 + 128 - 64 + 32 = 3527 176 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 128 + 64 - 32 + 16 = 1766 88 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 64 + 32 - 16 + 8 = 885 44 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 32 + 16 - 8 + 4 = 444 22 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 16 + 8 - 4 + 2 = 223 11 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 8 + 4 - 2 + 1 = 112 5 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 4 + 2 - 1 + 0 = 51 3 2Q + 2(Q−1) - 2(Q−2) + 2(Q−3) 2 + 1 - 0 + 0 = 3

5.3.3 Sample Boundary Computation

To demonstrate the computation of each PCT rule, we initiate sample calculation of each

rule that utilise equation 5.4 to 5.8 from Table 5.14.

Table 5.14: The conversion of PCT rules to β Beta, κ Kappa, and µ MuPCT256 PCT128 PCT-E

Max βMin κ + 1 (5.4)

β + µβ + 1 (5.5)

Max β + κ β + κMin β + 1 (5.6) [β + µ] + 1 (5.7)

β + κ + µ[β + κ] + 1 (5.8)

5.3.3.1 Equation 5.4 computation

In the case that only one type of ‘Q’, either 7 or 8 is applied during the identification cycle,

Equation 5.4 is used to calculate a minimum and maximum number of tags. Therefore, for

PCT256, PCT128 and PCT-E rules, we obtained the maximum and the minimum number

of tags by rewriting Equation 5.4, as shown in Table 5.15, 5.17 and 5.19. For instance, for

PCT256 where Q = 8, Equation 5.4 can be rewritten as follows:

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max = β

= 28 + 2(8−1) − 2(8−2) + 2(8−3)

= 352

min = κ+ 1

= (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4)) + 1

= 177

Therefore, for PCT256 after applying Equation 5.4, we obtained the maximum number of

tags of 352 and the minimum number of tags of 177.

Based on the same principle as PCT256, for PCT128 where Q = 7, Equation 5.4 can

be rewritten as follows:

max = β

= 27 + 2(7−1) − 2(7−2) + 2(7−3)

= 176

min = κ+ 1

= (2(7−1) + 2(7−2) − 2(7−3) + 2(7−4)) + 1

= 89

Thus, for PCT128 after applying Equation 5.4, we obtained the maximum number of tags

of 176 and the minimum number of tags of 89.

5.3.3.2 Equation 5.5 and 5.7 computations

Equations 5.5 and 5.7 are used to calculate a minimum and maximum number of tags for

PCT rules, in the case that two types of ‘Q’, either 8 & 7 or 8 & 6 are used during the

identification cycle. This rule only applies to PCT-E (See Table 5.14). After rewriting

Equation 5.5 or 5.7, we obtained the maximum and the minimum number of tags as shown

in Table 5.19. For example, for PCT-E where Q = 8, Equation 5.5 can be rewritten as

follows:

115


max = β + µ

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−2) + 2(8−3) − 2(8−4) + 2(8−5))

= 440

min = β + 1

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + 1

= 353

Thus, for PCT-E after applying Equation 5.5, we obtained the maximum number of tags

of 440 and the minimum number of tags of 353.

In addition, for PCT-E where Q = 8, Equation 5.7 can be rewritten as follows:

max = β + κ

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4))

= 528

min = β + µ+ 1

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−2) + 2(8−3) − 2(8−4) + 2(8−5)) + 1

= 441

Therefore, for PCT-E after applying Equation 5.7, we obtained the maximum number of



For the case where two types of ‘Q’, either 8 & 7 or 7 & 6 are used during the identification

cycle, Equation 5.6 is used to calculate a minimum and maximum number of tags for PCT

rules. This rule only applies to PCT256 and PCT128 but does not apply to PCT-E (See

Table 5.14). After rewriting Equation 5.6, we obtained the maximum and the minimum

number of tags as shown in Table 5.15 and 5.17. For example, for PCT256 where Q = 8,

Equation 5.6 can be rewritten as follows:

116


max = β + κ

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4))

= 528

min = β + 1

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + 1

= 353



Also, for PCT128 where Q = 7, Equation 5.6 can be rewritten as follows:

max = β + κ

= (27 + 2(7−1) − 2(7−2) + 2(7−3)) + (2(7−1) + 2(7−2) − 2(7−3) + 2(7−4)

= 264

min = β + 1

= (27 + 2(7−1) − 2(7−2) + 2(7−3)) + 1

= 177




Equation 5.8 is used to calculate a minimum and maximum number of tags, in the case

of three types of ‘Q’, 6, 7, and 8 are applied in PCT-E rule. After rewriting Equation 5.8,

we obtained the maximum and the minimum number of tags as shown in Table 5.19. For

instance, for PCT-E where Q = 8, Equation 5.8 can be rewritten as follows:

117


max = β + κ+ µ

= (28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4))+

(2(8−2) + 2(8−3) − 2(8−4) + 2(8−5))

= 616

min = [β + κ] + 1

= [(28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4))] + 1

= 529

Therefore, for PCT-E after applying Equation 5.8, we obtained the maximum number of


For both PCT256 and PCT-E rules, if the number of unread tags is larger than 352

and in order to achieve the optimal system efficiency, we must divide the tags into two or

more groups. For the number of unread tags smaller than 352, we must let every unread

tag responds.

Similarly, for PCT128 rule, if the number of unread tags is larger than 176 and in

order to achieve the optimal system efficiency, we must divide the tags into two or more

groups. By doing this, we can always obtain the expected system efficiency as displayed

in Figure 5.11.

5.3.4 PCT Rules

PCT approach derived new rules using particular equations expressed by β Beta, κ Kappa,

and µ Mu. All rules split the number of Backlog into groups then used one of Q8 (frame-

size 256), Q7 (frame-size 128), or Q6 (frame-size 64), to identify a current set of tags. We

make the assumption that the performance efficiency can be improved by dividing tags

into accurate number of groups, and then performing the tag identification separately

for each group. In this research, we have chosen the frame-size of 256, 128, and 64 for

our PCT rules since the initial Q of 8, 7 and 6 provide the most appropriate range for

the current RFID reader and passive tags specification. Generally, the UHF reader is

capable of capturing variety numbers of passive tags, depending on the reader type and

tag class (e.g. Class 0: Read-only tag). Thus, selected initial Qs are the most suitable

for our proposed rules. Each PCT rule, with the minimum and maximum boundaries, is

explained as follows:

118


5.3.4.1 PCT256

PCT256 uses either frame-size of 256 (Q = 8) or frame-size of 128 (Q = 7) for tag identifi-

cation. We assume that the identification time and performance efficiency of our proposed

PCT256 will advance from the existing probabilistic approaches. From the preliminary

for all PCT rules, we obtained specific equations to calculate minimum and maximum

boundaries for the PCT256 rule. These equations are applied as shown in Table 5.15.

Table 5.15: PCT256 Boundary Computation - number of group (Frame-Size 256 and 128),and minimum and maximum boundaries

PCT256 Boundary ComputationFS 256 FS 128 Minimum Bound Maximum Bound

.... .... .... ....4 - [3β + κ] + 1 4β3 1 3β + 1 3β + κ3 - [2β + κ] + 1 3β2 1 2β + 1 2β + κ2 - [β + κ] + 1 2β1 1 β + 1 β + κ1 - κ + 1 β

.... .... .... ....

Table 5.15 displays the relevant equations for minimum and maximum boundaries

calculation for the PCT256 rule. From the Table, we can see that there are two frame-

size, 256 and 128, for grouping division. For example, the minimum boundary is calculated

by 3β + 1 when the number of group division comprises three groups of 256 and one group

of 128, and the maximum boundary is calculated by 3β + κ. Following the computation,

the minimum and maximum boundaries are 1057 and 1232 respectively, as show in Table

5.16. The detailed calculation is present as follows:

max = 3β + κ

= 3(28 + 2(8−1) − 2(8−2) + 2(8−3)) + (2(8−1) + 2(8−2) − 2(8−3) + 2(8−4))

= 1232

min = 3β + 1

= 3(28 + 2(8−1) − 2(8−2) + 2(8−3)) + 1

= 1057

After applying specific equations for each group division, Table 5.16 shows the final

PCT rule for PCT256. For instance, if the number of Backlog equals to 900 tags, the

PCT256 algorithm will split the unread tags into three groups of Q8 (256).

Algorithm 5 demonstrates the group splitting algorithm using PCT256 rule, and either

keep tag in a single group or split tag into number of groups according to PCT256 rule.

119


Table 5.16: PCT256 Rule - The number of unread tags, optimal frame-size (A and B),and number of group (A and B)

PCT256 RuleBacklogs FS A Group A FS B Group B

.... .... .... .... ....1233 to 1408 256 4 - -1057 to 1232 256 3 128 1881 to 1056 256 3 - -705 to 880 256 2 128 1529 to 704 256 2 - -353 to 528 256 1 128 1177 to 352 256 1 - -89 to 176 128 1 - -45 to 88 64 1 - -23 to 44 32 1 - -12 to 22 16 1 - -6 to 11 8 1 - -

.... .... .... .... ....

Input: TagcountOutput: Number of Groupfor (Group Splitting procedure) do

if Tagcount less than 353 tags thenKeep tag into a single group;

endelse

while Looking up PCT256 Rule Table doif Found Matched rule for specific Backlog then

Split tags into groups;end

end

endOutput number of groups;

endAlgorithm 5: Group Splitting Algorithm using PCT256 Rule

5.3.4.2 PCT128

PCT128 uses either frame-size of 128 (Q = 7) or frame-size of 64 (Q = 6) for tag identifi-

cation. The PCT128 contains higher number of groups in some cases, compared with the

PCT256 method, which may result in worse performance efficiency for specific number of

tags. We calculate minimum and maximum boundaries for the PCT128 rule according to

specific equations. These equations are applied, as shown in Table 5.17.

Table 5.17 displays the relevant equations for minimum and maximum boundaries

calculation for the PCT128 rule. From the Table, it can be seen that there are two frame-

size, 128 and 64, for grouping division. For example, the minimum boundary is calculated

by 5β + 1 when the number of group division comprises five groups of 128 and one group

120


Table 5.17: PCT128 Boundary Computation - number of group (Frame-Size 128 and 64),and minimum and maximum boundaries

PCT128 Boundary ComputationFS 128 FS 64 Minimum Bound Maximum Bound

.... .... .... ....8 - [7β + κ] + 1 8β7 1 7β + 1 7β + κ7 - [6β + κ] + 1 7β6 1 6β + 1 6β + κ6 - [5β + κ] + 1 6β5 1 5β + 1 5β + κ5 - [4β + κ] + 1 5β4 1 4β + 1 4β + κ4 - [3β + κ] + 1 4β3 1 3β + 1 3β + κ3 - [2β + κ] + 1 3β2 1 2β + 1 2β + κ2 - [β + κ] + 1 2β1 1 β + 1 β + κ1 - κ + 1 β

.... .... .... ....

of 64, and the maximum boundary is calculated by 5β + κ. Following the computation,

the minimum and maximum boundaries are 881 and 968 respectively, as show in Table

5.18.

Table 5.18: PCT128 Rule - The number of unread tags, optimal frame-size (A and B),and number of group (A and B)

PCT128 RuleBacklogs FS A Group A FS B Group B

.... .... .... .... ....1321 to 1408 128 8 - -1233 to 1320 128 7 64 11145 to 1232 128 7 - -1057 to 1144 128 6 64 1969 to 1056 128 6 - -881 to 968 128 5 64 1793 to 880 128 5 - -705 to 792 128 4 64 1617 to 704 128 4 - -529 to 616 128 3 64 1441 to 528 128 3 - -353 to 440 128 2 64 1265 to 352 128 2 - -177 to 264 128 1 64 189 to 176 128 1 - -45 to 88 64 1 - -23 to 44 32 1 - -12 to 22 16 1 - -6 to 11 8 1 - -

.... .... .... .... ....

121


Table 5.18 shows the PCT rule for PCT128. For instance, if the number of Backlog

equals to 900 tags, the PCT128 algorithm will split the unread tags into five groups of Q7

(128) and one group of Q6 (64).

Algorithm 6 demonstrates the group splitting algorithm using PCT128 rule, and either

keep tag in a single group or split tag into number of groups according to PCT128 rule.



endelse

while Looking up PCT128 Rule Table doif Found Matched rule for specific Backlog then


end


endAlgorithm 6: Group Splitting Algorithm using PCT128 Rule

5.3.4.3 PCT-Extended

The rules of PCT-Extended (PCT-E) are more complex than the PCT256 and PCT128.

This is because the PCT-E identifies tags using three different frame-size of 256 (Q = 8),

128 (Q = 7), and 64 (Q = 6) instead of two. We assume that the performance efficiency

of PCT-E can improve further from the PCT256. However, the identification time may

increase due to the higher number of group applied in each identification round. From the

preliminary for all PCT rules, we obtained specific equations to calculate minimum and

maximum boundaries for the PCT-E rule. These equations are applied as shown in Table

5.19.

Table 5.19 presents the relevant equations for minimum and maximum boundaries

calculation for the PCT-E rule. From the Table, it is shown that there are three frame-

size, 256, 128 and 64, for grouping division. For instance, the minimum boundary is

calculated by [2β + κ] + 1 when the number of group division comprises two groups of

256, one group of 128, and one group of 64; and the maximum boundary is calculated by

2β + κ + µ. Following the computation, the maximum and minimum boundaries are 881

and 968 respectively, as shown in Table 5.20.

Table 5.20 displays the PCT-E rule. For instance, if the number of Backlog equals to

900 tags, the PCT-E algorithm will split the unread tags into two groups of Q8 (256), one

group of Q7 (128), and one group of Q6 (64).

122


Table 5.19: PCT-E Boundary Computation - number of group (Frame-Size 256, 128 and64), and minimum and maximum boundaries

PCT-E Boundary ComputationFS 256 FS 128 FS 64 Minimum Bound Maximum Bound

.... .... .... .... ....4 - - [3β + κ + µ] + 1 4β3 1 1 [3β + κ] + 1 3β + κ + µ3 1 - [3β + µ] + 1 3β + κ3 - 1 3β +1 3β + µ3 - - [2β + κ + µ] + 1 3β2 1 1 [2β + κ] + 1 2β + κ + µ2 1 - [2β + µ] + 1 2β + κ2 - 1 2β +1 2β + µ2 - - [β + κ + µ] + 1 2β1 1 1 [β + κ] + 1 β + κ + µ1 1 - [β + µ] + 1 β + κ1 - 1 β + 1 β + µ1 - - κ + 1 β

.... .... .... .... ....

Table 5.20: PCT-E Rule - The number of unread tags, optimal frame-size (A, B, C), andnumber of group (A, B, C)

PCT-E RuleBacklogs FS A Group A FS B Group B FS C Group C

.... .... .... .... .... .... ....1321 to 1408 256 4 - - - -1233 to 1320 256 3 128 1 64 11145 to 1232 256 3 128 1 - -1057 to 1144 256 3 - - 64 1969 to 1056 256 3 - - - -881 to 968 256 2 128 1 64 1793 to 880 256 2 128 1 - -705 to 792 256 2 - - 64 1617 to 704 256 2 - - - -529 to 616 256 1 128 1 64 1441 to 528 256 1 128 1 - -353 to 440 256 1 - - 64 1177 to 352 256 1 - - - -89 to 176 128 1 - - - -45 to 88 64 1 - - - -23 to 44 32 1 - - - -12 to 22 16 1 - - - -6 to 11 8 1 - - - -

.... .... .... .... .... .... ....

Algorithm 7 demonstrates the group splitting algorithm using PCT-E rule, and either

keep tag in a single group or split tag into number of groups according to PCT-E rule.

123




endelse

while Looking up PCT-E Rule Table doif Found Matched rule for specific Backlog then


end


endAlgorithm 7: Group Splitting Algorithm using PCT-E Rule


In order to show the significance of our proposed PCT method, we conducted an experi-

mental evaluation and compared our methods to existing techniques.

5.3.5.1 Preliminary

To study the Probabilistic Cluster-Based Technique, the experiment is assumed to be

under the same environment as for the experimental evaluation of the PTES (Section

5.2).

5.3.5.2 Experiment Data Set

The aim of the experiment is to compare the performance of our proposed PCT method to

the existing probabilistic DFSA and EDFSA anti-collision approaches. In this experiment,

we considered different number of tags, from 100 to 1400, within the interrogation zone.

The number of simulated tags are assumed to be no more than 1400 tags, due to maximum

range of UHF reader and passive tags. For each identification round, optimal tunable

parameters of PTES[CCE] is applied on different Initial Q. For instance, when PCT initial

a new frame with Initial Q of 8, the tunable parameter V1 is set to 2.3 and parameter V2

is set to 0.1. Table 5.21 displays the type of ALOHA-based anti-collision methods applied

to different number of tags and tunable initial Qs. All methods are applied separately to

different randomly generated data sets, giving a total of 70 test cases (14 for each method)

within this experiment.

124


Table 5.21: Chosen Parameters for Experiment ThreeInitial Q Backlog Prediction No. Tags

DFSA 8 PTES[CCE] 100 to 1400EDFSA 8 PTES[CCE] 100 to 1400PCT256 tunable between 7, 8 PTES[CCE] 100 to 1400PCT128 tunable between 6, 7 PTES[CCE] 100 to 1400PCT-E tunable between 6, 7, 8 PTES[CCE] 100 to 1400

5.3.6 Results

Our experiment evaluates the performance of our proposed PCT method to existing DFSA

and EDFSA approaches. Corresponding between Table 5.22 and Figure 5.13a), it can be

seen that both PCT256 and PCT-E produced minimal number of slots during identifica-

tion process, compared with other methods. Specifically, PCT256 and PCT-E technique

minimised the number of slots from EDFSA approach when the number of tags is between

400 and 500, and between 800 and 1200 tags. This is because the number of group sets for

EDFSA will be doubled when the number of Backlog reached the specific threshold; while

PCT increased number of group slowly, according to the estimated number of unread tags.

As a result, the number of slots are minimised for PCT256. On the other hand, PCT128

performed better than DFSA but did not outperform the EDFSA. According to optimal

efficiency displayed in Figure 5.11, the initial Q of 8 (frame-size = 256) has a wider range

of optimal efficiency compared with the initial Q of 7. Therefore, PCT256 with initial

frame-size of 256, has a better performance than the PCT128 with initial frame-size of

128.

Table 5.22: Number of slots comparison and Performance efficiency for DFSA, EDFSA,PCT128, PCT256, and PCT-E methods on different number of tags

Number of Slots EfficiencyTags D ED P128 P256 P-E D ED P128 P256 P-E100 364 364 304 364 364 0.2747 0.2747 0.3289 0.2747 0.2747200 536 536 588 536 536 0.3731 0.3731 0.3401 0.3731 0.3731300 880 880 880 880 880 0.3409 0.3409 0.3409 0.3409 0.3409400 1200 1224 1186 1094 1098 0.3333 0.3268 0.3373 0.3656 0.3643500 1368 1408 1438 1350 1350 0.3655 0.3551 0.3477 0.3704 0.3704600 1776 1672 1758 1672 1670 0.3378 0.3589 0.3413 0.3589 0.3593700 2224 1880 2056 1880 1880 0.3147 0.3723 0.3405 0.3723 0.3723800 2552 2484 2302 2126 2126 0.3135 0.3221 0.3475 0.3763 0.3763900 2812 2672 2644 2416 2448 0.3201 0.3368 0.3404 0.3725 0.36761000 3504 3016 2936 2760 2760 0.2854 0.3316 0.3406 0.3623 0.36231100 3516 3104 3242 2974 2978 0.3129 0.3544 0.3393 0.3699 0.36941200 3800 3288 3494 3230 3230 0.3158 0.3650 0.3434 0.3715 0.37151300 4308 3552 3814 3552 3550 0.3018 0.3660 0.3408 0.3660 0.36621400 4688 3760 4112 3760 3760 0.2986 0.3723 0.3405 0.3723 0.3723

Table 5.22 shows that there is no improvement to our proposed methods compared

with existing methods when the number of tags are low (up to around 300 tags). This

is because PCT methods start dividing tags into groups only when the number of tags

125


Figure 5.13: Number of slots comparison (a) and Performance efficiency (b) for DFSA,EDFSA, PCT128, PCT256, and PCT-E methods on different number of tags

reaches the specific threshold. As a result, for certain tag sizes, the number of slots

and performance efficiency remained unchanged due to the same identification procedure,

compared with DFSA and EDFSA methods. Moreover, Table 5.22 also demonstrates that

the PCT128 is the only method that has different results when the number of tags are

100 and 200 tags. This is due to the fact that PCT128 is the only method that uses

initial frame-size of 128 to predict Backlog. Therefore, even when the number of tags is

still low, the PCT128 starts splitting tags into group, resulting in different tag outcomes.

Furthermore, when the number of tags is 100 tags, PCT128 shows the minimal number of

slots issues. The reason for the outcome is because PCT128 uses frame-size of 7 instead

126


of 8, as used in other methods. Thus, when the number of tags are as low as 100 tags, the

PCT128 performs the best. However, we can see from Figure 5.13b) that the performance

efficiency of PCT128 stabilises and does not improve any further when the number of tags

increased.

Table 5.22 and Figure 5.13b) show that both PCT256 and PCT-E maintained their

system efficiency above other methods and have the most stable performance. Never-

theless, the PCT-E required additional number of group sets from the PCT256 method

throughout the identification process (see Table 5.16 versus Table 5.20). As a result, the

PCT-E required extra time to initiate a new group compared with the PCT256 method.

On the other hand, the DFSA’s efficiency dropped dramatically when the number of tags

increase, while the EDFSA’s efficiency become unstable during the time when number of

groups doubled-up from 1 to 2 and from 2 to 4. The PCT128 has steady performance but

does not perform as good as PCT256.

Table 5.23: Percentage improvement of the proposed PCT128, PCT256, and PCT-E versusexisting EDFSA (ED) and DFSA (D) techniques

PCT128 PCT256 PCT-Eimproved improved improved improved improved improvedfrom ED from D from ED from D from ED from D

100 16.48 16.48 0.00 0.00 0.00 0.00200 -9.70 -9.70 0.00 0.00 0.00 0.00300 0.00 0.00 0.00 0.00 0.00 0.00400 3.10 1.17 10.62 8.83 10.29 8.50500 -2.13 -5.12 4.12 1.32 4.12 1.32600 -5.14 1.01 0.00 5.86 0.12 5.97700 -9.36 7.55 0.00 15.47 0.00 15.47800 7.33 9.80 14.41 16.69 14.41 16.69900 1.05 5.97 9.58 14.08 8.38 12.941000 2.65 16.21 8.49 21.23 8.49 21.231100 -4.45 7.79 4.19 15.42 4.06 15.301200 -6.27 8.05 1.76 15.00 1.76 15.001300 -7.38 11.47 0.00 17.55 0.06 17.601400 -9.36 12.29 0.00 19.80 0.00 19.80

Average -1.66 5.93 3.80 10.80 3.69 10.70

Table 5.23 demonstrates the percentage of improvement of the proposed PCT method

versus EDFSA and DFSA methods. It can be seen that when the number of tags are

low and the PCT methods have not divided these tags into groups, there are no difference

between the outcome of our methods and existing methods, and the percentage of improve-

ment remain unchanged. However, during the time when number of groups doubled-up

from 1 to 2 (400 tags) and from 2 to 4 (800 tags), PCT256 and PCT-E show the highest

percentage of improvement compared with the EDFSA method. On the other hand, the

percentage of improvement increased more stably compared with the DFSA method, since

the DFSA method does not imply group splitting rules.

The PCT256 has a better performance than the EDFSA by about 4 percent on average,

while it is approximately 11 percent better than the DFSA approach as demonstrated in

127


Figure 5.14: Percentage of improvement of PCT compared with DFSA and EDFSA meth-ods

Figure 5.14. The optimal percentage of improvement of PCT256 method can achieve

up to 14 percent and 21 percent compared with the EDFSA and DFSA respectively,

depending on the number of tags within the interrogation zone. Nevertheless, the PCT-E

method required additional number of groups from the PCT method, and acquired slightly

lower percentage of improvement, compared with the PCT method. On the other hand,

the PCT128 has a better performance than the DFSA method by around 6 percent on

average, but does not show any improvement from the EDFSA technique, as displayed in

Figure 5.14. However, the PCT128 still shows some improvement in some cases and is able

to achieve up to 16 percent compared with the EDFSA and DFSA methods. Therefore,

we conclude that our proposed PCT256 method is the most effective method, in terms of

system efficiency and number of slots minimisation.

5.4 Overall Analysis

A total of three experiments were conducted for the ALOHA-based anti-collision ap-

proaches. The first two experimental evaluations are to verify the performance of our

proposed Precise Tag Estimation Scheme and to identify the best tunable parameters for

the method. The third experiment is to compare the performance of our proposed Prob-

abilistic Cluster-Based Technique to existing ALOHA-based methods. From the first two

experiments, we determined that the initial Q of 8 is the most suitable initial Q on average

for our proposed PTES methods. Nevertheless, the selected initial Q is mainly appropriate

for PTES method, incorporated with the probabilistic approach with no grouping rules.

Additionally, we verified that PTES[CCE], with parameters V1 = 2.3 and V2 = 0.1 and

128

5.5. SUMMARY

optimal initial Q of 8, is the best approach out of the three proposed PTES methods.

In addition to the first two experiments, the final experiment demonstrated that our

proposed PCT method is the most effective method in terms of system efficiency and

number of slots minimisation. Specifically, the PCT256 gives the highest performance

efficiency and outperforms existing ALOHA-based anti-collision methods.

From the analysis of all experiments, we recognised certain properties of importance for

ALOHA-based anti-collision methods, which are: 1) initial Q (frame-size) initialisation;

2) accuracy of Backlog prediction techniques; and 3) overall number of tags within the

interrogation zone.

5.5 Summary

In this chapter, we have investigated the problems on existing probabilistic anti-collision

approaches and proposed a new frame-size estimation scheme, in order to predict a more

precise frame-size to be used and incorporated with a probabilistic anti-collision technique.

We also proposed a new probabilistic anti-collision method, to eliminate shortcomings of

existing approaches. The main contributions and findings of this Chapter are as follows:

• We have proposed a Precise Tag Estimation Scheme (PTES) (Pupunwiwat and Stan-

tic, 2010a), (Pupunwiwat and Stantic, 2010b), which is a method that estimates pre-

cise number of remaining Backlog by using information of collision slots and empty

slots from the previous frame. We found that PTES with correctly tuned Q param-

eter and variable V1 and V2 can achieved optimal results and outperformed existing

frame-size estimation techniques.

• We also proposed a Probabilistic Cluster-Based technique (PCT) (Pupunwiwat and

Stantic, 2010d) to maximise efficiency of the tag identification process and to be

incorporated with our proposed PTES. We discovered that PCT performed the best

compared with existing ALOHA-based anti-collision techniques, regardless of num-

ber of tags within the interrogation zone.

• We have confirmed that the tunable initial Q, the accuracy of Backlog prediction

techniques with correct variables, and the overall number of tags within the interro-

gation zone, have impacted on the performance of any ALOHA-based anti-collision

schemes. Nevertheless, the best performing approach in terms of system efficiency

and robustness of the RFID system, are our proposed probabilistic anti-collision

techniques.

129

6Conceptual Selective Technique Management

In this chapter, we analyse our proposed deterministic and probabilistic anti-collision

approaches and determine the best-fit method for specific circumstances. Firstly, we com-

pared the Joined Q-ary Tree and the Probabilistic Cluster-Based Technique, and identified

the performance and certain properties of importance for both anti-collision methods in

general. Secondly, we proposed two new selective techniques management: 1) a Novel

Decision Tree Strategy; and 2) a Six Thinking Hats Strategy (Pupunwiwat et al., 2011).

We applied each selective technique toward the anti-collision method selection process, in

order to find the optimal method for specific scenarios. The benefit from choosing correct

anti-collision methods is that the Chain Reaction impact toward long-term RFID data

management can be reduced. Finally, we formed a new concept and applicability of each

type of anti-collision approach, then applied them to a sample real world scenario. The re-

maining of this chapter comprise the definition of Chain Reaction and why it is important

in RFID data management; the comparative analysis of Joined Q-ary Tree versus Proba-

bilistic Cluster-Based Technique; the foundation of two selective technique management;

and the sample applicability of each anti-collision approach toward real world scenario.

6.1 Chain Reaction from Data Collection Process

A chain reaction is a sequence of reactions where a reactive product or by-product causes

additional reactions to take place. In a chain reaction, positive feedback leads to a self-

amplifying chain of events. As for chain reaction toward RFID data management, the

most important step that will have the largest impact toward data is the RFID data

131

CHAPTER 6. CONCEPTUAL SELECTIVE TECHNIQUE MANAGEMENT

collection process. If any error occurs at the data collection level, the impact will increase

all subsequent steps, such as data integration and aggregation; data query model and

event processing; and data warehousing and data mining.

Our main goal in this chapter is to identify which anti-collision method is the most

suitable for particular circumstances. In order to do so, we need a precise selective tech-

nique management to classify which anti-collision scheme is the most suitable for specific

scenario. After being able to optimise the anti-collision method selection process, the

Chain Reaction from data collection process can be reduced toward long-term RFID data

management.

6.2 Comparative Analysis of Deterministic and Probabilistic

Techniques

To compare and analyse our proposed anti-collision methods, we identified certain

general properties of importance for anti-collision methods, they are:

Tree-based methods

• Similarity of EPC pattern

• Number of tags within one group of the EPC pattern

• Overall number of tags within the interrogation zone

ALOHA-based methods

• Initial Frame-size (Q) specification

• Accuracy of Backlog prediction techniques

• Overall number of tags within the interrogation zone

In this study, we have empirically compared the performance of the Joined Q-ary

Tree against the PCT anti-collision approach because our deterministic and probabilistic

methods have outperformed existing techniques in their own grounds (Pupunwiwat and

Stantic, 2010c,d). The Joined Q-ary Tree uses less resource, has no complexity in imple-

mentation, and needs low reader power and memory consumption, because it does not need

to keep memory during identification. On the other hand, the Probabilistic Cluster-Based

Technique works well in arbitrary situation, minimise resource used, and increase system

efficiency, without the need for complex implementation. We believe that this comparative

analysis is necessary to identify the best overall method for specific circumstances.

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6.2. COMPARATIVE ANALYSIS OF DETERMINISTIC AND PROBABILISTICTECHNIQUES

6.2.1 Data sets

There are two major test cases involved in our empirical evaluation, and these test cases

have been generated separately. The first test case considers specific EPC patterns (same

product) with 50 and 100 tags per pallet. The second test case, which has been used for

probabilistic approaches, had no specific EPC pattern (different products) nor a specific

number of tags per pallet. These two cases represent a typical situation in a warehouse

environment.

Our three main anti-collision schemes chosen for comparison are: 1) Joined Q-ary

Tree, 2) PCT256 no group, and 3) PCT256. The PCT256 no group does not imply any

group-splitting rule but still employ our proposed PTES as a tag estimation method. Since

it is verified previously that our methods perform better than existing approaches, it is

necessary to know which of our methods is the most suitable under particular condition

and environment. The data sets are explained as follows:

6.2.1.1 Joined Q-ary Tree method

For Joined Q-ary Tree anti-collision approach, there are ten pallets of inventories in test

case A, with each pallet contains 100 cases/tags, giving a total of 1000 tags. Similarly,

test case B also contains 1000 tags but each pallet only holds 50 cases/tags. The GID-96

bits EPC encoding scheme is used with the EPC pattern from Table 6.1.

• Test case A: Joined Q-ary Tree with 100 tags per pallet (Joined(100)) - 10 pallets,

100 cases each, total 1000 tags

• Test case B: Joined Q-ary Tree with 50 tags per pallet (Joined(50)) - 20 pallets,

50 cases each, total 1000 tags

Table 6.1: Chosen EPC Pattern of Tree-based anti-collision methods for ComparativeAnalysis

EPC Pattern (GID-96)H 0011 0101

GMN 104,426,055OC [9,872,273 - 9,872,292]SN [26,292,755,245 - 26,292,755,344]

6.2.1.2 PCT256 no group and PCT256 methods with PTES tag estimation

For both probabilistic anti-collision approaches, we considered different number of tags,

from 100 to 1000 tags. For each identification round, optimal tunable parameters of

PTES[CCE] is applied on different Initial Q. Table 6.2 demonstrates the type of ALOHA-

based anti-collision methods applied to different number of tags and tunable initial Qs.

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All methods are applied separately to different randomly generated data sets, giving a

total of 20 test cases (20 for each method) within this test case.

Table 6.2: Chosen Parameters of ALOHA-based anti-collision methods for ComparativeAnalysis

Initial Q Backlog Prediction No. TagsPCT256 no group 8 PTES[CCE] 100 to 1000

PCT256 tunable between 7, 8 PTES[CCE] 100 to 1000

6.2.2 Comparative Analysis

From the empirical study, we have investigated the performance of our proposed Joined

Q-ary Tree and PCT256 (group and no group). From Table 6.3 and Figure 6.1a), it is

shown that the number of slots of Joined Q-ary Tree increased linearly, depending on the

number of tags within the interrogation zone. On the other hand, the number of slots of

PCT256 methods increased with no specific pattern. This is due to the random nature of

slots generation in ALOHA-based approaches.

Figure 6.1a) illustrates that the difference in performance between each method in-

creased with the increased number of tags, and this has particularly become visible when

examining 1000 tags. The overall number of slot results have shown that the Joined Q-ary

Tree with 100 tags per pallet (Joined(100)) has obtained the minimal number of slots

throughout the whole experiment, which also obtained the shortest identification time re-

quired. In contrast, the Joined Q-ary Tree with 50 tags per pallet (Joined(50)) performed

poorly compared with the Joined(100)and PCT256. These results have proven that the

selection of the EPC pattern has a large impact on the performance of the Joined Q-ary

Tree. When the chosen EPC pattern involved has a very small group of tags (such as

50 tags per pallet), the performance of Joined Q-ary Tree cannot be optimised. Figure

6.1a) also demonstrates that the PCT256 performed better than both PCT256 no group

and Joined(50), but does not outperformed Joined(100). However, the PCT256 does not

rely on the restriction of EPC pattern and can be applied to any set of tags with different

encoding scheme. Moreover, the PCT256 no group also performs better than Joined(50)

when the number of tags is lower than 500 tags, but began to worsen when the number

of tags gets higher.

Table 6.3 and Figure 6.1b) show the performance efficiency of all methods. It can

be seen that the Joined(100) achieved close to 47 percent efficiency once the number

of tags reach 1000. Additionally, we can see than the performance efficiency of both the

Joined(100) and Joined(50) methods keep increasing, in accordance to the number of tags.

In contrast, the PCT256 cannot achieve a performance efficiency higher than 38 percent.

By examining Figure 6.1b), it can be assumed that the efficiency of the Joined Q-ary Tree

will increase slowly once the number of tags within the interrogation zone becomes very

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6.2. COMPARATIVE ANALYSIS OF DETERMINISTIC AND PROBABILISTICTECHNIQUES

Figure 6.1: Comparative analysis of deterministic versus probabilistic anti-collision meth-ods: a) Number of slots comparison and b) Performance efficiency

high. For the Joined(50), if the number of tags keeps increasing, it is possible that the

performance efficiency will achieve the same level as PCT256.

From the comparative analysis, we have identified certain properties of importance for

anti-collision methods in general. For deterministic methods, we have discovered that

there are impacts from similar EPC patterns; the number of tags within one group of the

EPC pattern; and the overall number of tags within the interrogation zone. For proba-

bilistic methods, we have determined that the performance of the anti-collision technique

depends on the Initial frame-size (or the Q value) specification; the accuracy of Backlog

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Table 6.3: Number of slots and performance analysis for Joined Q-ary Tree (100 tags),Joined Q-ary Tree (50 tags), PCT256 no group, and PCT256 on different number of tags

Number of Slots EfficiencyTags J100 J50 P256(N) P256 J100 J50 P256(N) P256100 320 392 348 348 0.3125 0.2551 0.2874 0.2874200 520 668 536 536 0.3846 0.2994 0.3731 0.3731300 720 940 880 880 0.4167 0.3191 0.3409 0.3409400 924 1216 1200 1094 0.4329 0.3289 0.3333 0.3656500 1124 1488 1368 1350 0.4448 0.3360 0.3655 0.3704600 1324 1760 1776 1672 0.4532 0.3409 0.3378 0.3589700 1524 2036 2224 1880 0.4593 0.3438 0.3147 0.3723800 1728 2308 2552 2126 0.4630 0.3466 0.3135 0.3763900 1928 2588 2812 2416 0.4668 0.3478 0.3201 0.37251000 2128 2860 3504 2760 0.4699 0.3497 0.2854 0.3623

prediction techniques; and the overall number of tags within the interrogation zone. We

conclude that the Joined Q-ary Tree method can achieve higher efficiency if the right EPC

pattern is configured. However, for arbitrary situations where EPC pattern cannot be

found, it is more preferable to use probabilistic approach rather than the deterministic

method.

6.3 Strategies for Choosing Suitable Anti-Collision Techniques

In this section, we clarify the importance of data collection process and why anti-collision

method selection process is a very important step for real world applications. We intro-

duce two novel strategies to choose the correct type of anti-collision algorithm for the

right situation. Most past literature only focus on improving specific type of anti-collision

technique, either deterministic or probabilistic; and also attempt to combine both schemes

together. While several literature focus heavily on improving anti-collision method alone,

there is no research done on how the data collection process can be optimised by employing

the correct anti-collision method for the right business. In another word, we have been

trying to improve something very eagerly without knowing how these improvements can

benefit real life scenarios. Thus, we propose two novel strategies for optimal anti-collision

method selection, which utilises Decision Tree (Investopedia, 2011) and the Six Thinking

Hats Strategy (Bono, 2000). By selecting a correct anti-collision method for the business,

the data collection procedure, which is the first and most important step in data manage-

ment, can be optimised. Thus, the resources requirement, cost, and complexity of RFID

system’s implementation for data transformation, data security, and data organisation,

can be minimised. Also, by selecting the correct anti-collision algorithm for a specific

scenario, we do not need the most complex and expensive algorithm, to be able to get the

most efficient collection of data.

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6.3. STRATEGIES FOR CHOOSING SUITABLE ANTI-COLLISION TECHNIQUES

6.3.1 Novel Decision Tree for Anti-Collision Methods Selection

There are always many factors that contribute to the outcome of the solution. A Decision

Tree can be used to clarify and find an answer to a non-complex problem. The structure of

Decision Tree allows users to take a problem with multiple possible solutions and displays

it in a simple format that shows the relationship between different events or decisions.

From literatures, a good Decision Tree can reach toward the same solution as complex

Fuzzy Logic. For scenario where not many RFID locations and constraint are involved, it

is wise to apply the Decision Tree to decide between either deterministic or probabilistic

anti-collision protocols.

6.3.1.1 Novel Decision Tree Architecture

In this study, we introduce the Novel Decision Tree Strategy for selective anti-collision

technique management, where either Joined Q-ary Tree, PCT no group, or PCT group

is applicable. PCT no group does not split tags into group as the number of tags may

not be high enough to require the splitting. Certain properties of importance for anti-

collision methods discovered from comparative analysis are to be integrated with the

decision-making progress.

Figure 6.2: Novel Decision Tree for Anti-Collision Methods Selection

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Figure 6.2 illustrates the steps of the decision-making process of the proposed Novel

Decision Tree. By taking certain properties found from our empirical study into consid-

eration, we have constructed a decision tree that reflected on the size of the company, the

number of tags per pallet, the total number of tags, the EPC pattern, and the relationship

between the suppliers and consumers. For instance, A Local Pen Maker Company is a

small company that produces any type of pens and exports them locally. The company

packs a group of pens into a box, and then allocates them into pallets. Each box contains

x pens and each pallet contains y boxes. We can use these provided information and travel

through each step of the decision tree, to reach the final outcome.

6.3.1.2 Sample Scenarios using Decision Tree Selection

Local Pen Maker Company (SME) A Local Pen Maker Company is a small company

that produces any type of pens and exports them locally. The company packs a group

of pens into a box, and then allocated them into pallets. Each box contains 30 pens and

each pallet contains 5 boxes. Each pen is tagged with individual RFID passive tag. Total

number of tags within a single interrogation zone equal to 600 tags (4 pallets).

By using Decision Tree from Figure 6.2 for the final outcome, a suitable anti-collision

method for a Local Pen Maker Company is a “Deterministic Joined Q-ary Tree”. A given

threshold t for this scenario equals to 100 tags, and the procedures are as follows:

• Question: Is this a SME or a large Enterprise? Answer: SME

• Question: Is this an international corporation? Answer: No

• Question: Are all items from different sources? Answer: No

• Question: Is the number of tag in a single pallet exceeding 100 tags? Answer:

Yes

• Outcome: The suitable anti-collision method is a “Deterministic Joined Q-ary

Tree”

Therefore, according to the decision tree outcome, a Local Pen Maker SME Company

should employ a deterministic Joined Q-ary Tree, as its anti-collision method (Figure 6.3).

Local Notebook Manufacturer (SME) A Local Notebook Manufacturer is a medium

size company that produces any type of notebooks, note pads, writing pads; and then

exports them locally. The company packs a group of notebooks into a box, and then

allocates them into pallets. Each box contains 10 notebooks and each pallet contains 5

boxes. Each notebook is tagged with individual RFID passive tag. Total number of tags

within a single interrogation zone equal to 250 tags (5 pallets).

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Figure 6.3: Novel Decision Tree for Local Pen Maker Company (SME) Anti-CollisionMethods Selection


method for a Local Notebook Manufacturer is a “Probabilistic Cluster-Based Technique

with no grouping strategy”. A given threshold t for this scenario equals to 100 tags and

x equal to 300 tags. The procedures are as follows:

• Question: Is this a SME or a large Enterprise? Answer: SME

• Question: Is this an international corporation? Answer: No

• Question: Are all items from different sources? Answer: No

• Question: Is the number of tag in a single pallet exceeding 100 tags? Answer: No

• Question: Is the total number of tag in an interrogation zone exceeding 300 tags?

Answer: No

• Outcome: The suitable anti-collision method is a “Probabilistic Cluster-Based

Technique without grouping strategy”.

Thus, according to the decision tree outcome, a Local Notebook Manufacturer should

employ a PCT no group, as its anti-collision method (Figure 6.4).

Figure 6.4: Novel Decision Tree for Local Notebook Manufacturer (SME) Anti-CollisionMethods Selection

International Stationery Enterprise An International Stationery Enterprise is a

large business that imports any type of notebooks, pens, pencils, pencil cases; and then

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exports them internationally. The company packs a group of stationeries into a box, and

then allocates them into pallets. Each box contains different types of supplies and each

pallet contains several boxes. Each stationery is tagged with individual RFID passive tag.

Total number of tags within a single interrogation zone equals to 400 tags.


method for an International Stationery Enterprise is a “Probabilistic Cluster-Based Tech-

nique”. A given x for this scenario equals to 300 tags, and the procedures are as follows:

• Question: Is this a SME or a large Enterprise? Answer: Large Enterprise

• Question: Does the corporation involve trading? Answer: Yes

• Question: Is the Supplier to Consumer or Consumer to Supplier relationship a 1

to M relationship? Answer: No

• Question: Is the total number of tag in an interrogation zone exceeding 300 tags?

Answer: Yes


Technique”.

Therefore, according to the decision tree outcome, an International Stationery Enter-

prise should employ a PCT as its anti-collision method (Figure 6.5).

Figure 6.5: Novel Decision Tree for International Stationery Enterprise Anti-CollisionMethods Selection

International A-Grade Filing and Storage Group An International A-Grade Filing

and Storage Group is a large business that imports any type of filing and storage materials,

and then exports them to a single local business. The company packs a group of materials

into a box, and then allocates them into pallets. Each box contains specific type of supplies

with 20 items and each pallet contains 6 boxes. Each product is tagged with individual

RFID passive tag. Total number of tags within a single interrogation zone equals to 350

tags.


method for an International Stationery Enterprise, is an integration of both “Deterministic

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Joined Q-ary Tree” and “Probabilistic Cluster-Based Technique”. A given threshold t for

this scenario equal to 100 tags and x equals to 300 tags. The procedures are as follows:


• Question: Does the corporation involve trading? Answer: Yes


to M relationship? Answer: Yes

• Question: Is the number of tag in a single pallet exceeding 100 tags; and is the

total number of tag in an interrogation zone less than 300 tags? Answer: No

• Question: Is the number of tag in a single pallet exceeding 100 tags; and is the

total number of tag in an interrogation zone exceeding 300 tags? Answer: Yes

• Outcome: The suitable anti-collision methods are “Deterministic Joined Q-ary

Tree” and “Probabilistic Cluster-Based Technique”.

Therefore, according to the decision tree outcome, an International A-Grade Filing

and Storage Group should employed both Joined Q-ary Tree and PCT as its anti-collision

methods (Figure 6.6).

Figure 6.6: Novel Decision Tree for International A-Grade Filing and Storage Group Anti-Collision Methods Selection

6.3.2 Extended Solution for Complex Anti-Collision Methods Selection

This section introduces an alternative technique to be used, instead of the Novel Decision

Tree. It is possible that the Novel Decision Tree may not be the best for some complex

cases; and the complex decision-making process, which involves more than fact and num-

bers, will be required in order to obtain the best anti-collision selection. There are several

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everyday decision-making techniques available in modern day. However, we must select

the best technique that will allow the selective decision to be made precisely, and provide

the best solution based on information, feeling, and experiences.

6.3.2.1 Everyday decision making techniques

There are several existing everyday decision-making techniques available, which could pos-

sibly be applied to RFID anti-collision selection process. These techniques are described

as follows:

• Pros and Cons: This technique lists the advantages and disadvantages of each option.

• Simple Prioritisation: This method selects the alternative with the highest

probability-weighted utility for each alternative.

• Satisfaction: This technique accepts the first option that seems like it might achieve

the desired result. The decision is made according to a person in authority or an

expert.

• Flipism: Flipism includes decision making based on flipping a coin, cutting a deck

of playing cards, and other random or coincidental methods.

It is important to recognise the importance of anti-collision method selection. Thus,

any existing techniques in everyday decision-making should be considered. However, each

technique mentioned earlier only involves specific criteria in making the right decision.

Therefore, we must provide alternative methods that combine all decision-making tech-

niques together, in order to derive the best solution in anti-collision selecting process.

From past literature, we found the “Six Thinking Hats strategies” to be a useful decision-

making technique that include most decision-making process, and can be applied for ef-

fective complex anti-collision methods selection.

6.3.2.2 Six Thinking Hats Strategies

In this concept, there are six metaphorical hats; and the thinker can put on or take off one

of these hats to indicate the type of thinking used. Bono (2000) stated that putting on and

taking off these hats is essential. The hats must never be used to categorise individuals,

even though their behavior may seem to invite this (Bono, 2005, 2008). When done in

groups, everybody wears the same hat at the same time.

Figure 6.7 illustrates the Six Thinking Hats framework. The explanation of each hat

is as follows:

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Figure 6.7: Six Thinking Hats Framework

• White Hat: The White Hat takes care of facts and numbers by thinking neutral

and objective, and by focusing on the data and information that are available or

needed.

• Red Hat: This covers intuition, emotions, feelings, and hunches. The red hat allows

the thinker to put forward an intuition without having to qualify or justify it.

• Black Hat: This is the hat of judgment and caution; and is a most valuable hat.

The black hat covers negative aspects, for example, why something cannot be done.

The black hat must always be logical.

• Yellow Hat: This is the logical positive, for example, why something will work

and why it will offer benefits. It can be used when looking forward to the results of

some proposed action, but can also be used to find something of value in what has

already happened.

• Green Hat: This is the hat of creativity, alternatives, proposals, what is interesting,

provocations, and changes.

• Blue Hat : This is the overview or process control hat. It looks not at the subject

itself but at the thinking about the subject. The blue hat takes care of the control

and the organisation of the process of thought. Also of the use of the other hats.

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To simplify the benefit of Six Thinking Hats strategies over existing everyday decision-

making methods, the following points demonstrate the applicability of each hat toward

existing everyday decision-making technique:

• Pros and Cons: Pros can be viewed through Yellow hat ; and Cons can be viewed

through Black hat.

• Simple Prioritisation: Simple Prioritisation can be examined through White hat,

which only represents fact and decision that is made according to the verification of

that fact.

• Satisfaction: This technique can be observed through White hat and Red hat, as the

decision is made according to the fact from an expert, and the likelihood of the first

option that seems like it might achieve the desired result.

• Flipism: Flipism solely based on luck and chances, thus, we classified this method

under the Red hat category.

6.3.3 Six Thinking Hats for Complex Anti-Collision Methods Selection

It is crucial that the RFID system must employ anti-collision protocols in readers, in order

to enhance the integrity of the captured data. However, the step of choosing the right anti-

collision protocol is also very important, since we cannot depend solely on the capability

of anti-collision protocol itself, but also on the suitability of each selected technique for

the specific scenario. This is why the Novel Decision Tree alone is not sufficient because it

is based solely on data figures. Therefore, we propose the Six Thinking Hats strategy for

complex selective technique management to clarify the choice from the decision tree. The

novelty of using Six Thinking Hats strategy and applying it for anti-collision selection is

that, we will get the optimal and more precise outcome of anti-collision method selection

for the specific scenario.

6.3.3.1 Preliminary

The one great enemy of the thought is the complexity because it leads to confusion. When

the thought is clear and simple, it is more pleasing and effective. Therefore, the concept

of the Six Thinking Hats is to think simply. First, the thinker must simplify the thought

by treating one thing later, rather than at the same time. The thinker can then face them

by separating the emotions, the logic, the information, the hope, and the creativity. The

intention of the Six Thinking Hats is to disassembly the thought, so that the thinker can

use a way to think on one thing at a time, instead of doing all at the same time.

For anti-collision selective process, each thinker must make decision separately. The

Blue hat thinker, who control the selective process, will make final decision for best anti-

collision techniques for the specific scenario.

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The following shows how the six hats can be applied toward complex anti-collision

selective process:

• White hat - Facts & Information: When a thinker is wearing a White hat,

he/she is neutral and objective. The thinker does not make interpretations nor gives

opinions but instead imitate the computer that gives the facts and numbers. The

thinker must obtain information and complete the emptiness of existing information.

For anti-collision selective process, the thinker who wears White hat will rely solely

on information given from various sources, such as a Novel Decision Tree; and will

decide which anti-collision is most suitable for the current scenario.

• Red hat - Feelings & Emotions: The use of Red hat allows the thinker to feel

visible so that they can become partly of map, and also of the system of values, that

chooses the route in the map. The Red hat provides the thinker with an advisable

method to enter and to leave the way emotionally. Thus, it allows the thinker to

explore the feelings and never to make the attempt to justify the feelings or to base

them on the logic. For anti-collision selective process, the thinker who wears Red

hat will make decision based on hunch and guts feeling. Thus, it is important that

the person who wear Red hat has strong relationship with the enterprise.

• Black hat - Being Cautious & Pessimistic: Thinking with Black hat takes

care specifically of the negative judgment. The thinker of Black hat indicates what

is bad, incorrect, and erroneous. The thinker indicates that something does not

comply to the experience or to the knowledge accepted; and why something is not

going to work. For anti-collision selective process, the thinker who wears Black hat

points out the disadvantage of the selected anti-collision approach; and why it may

be necessary to change to a different technique.

• Yellow hat - Being Positive & Optimistic: - Thought of Yellow hat is positive

and constructive. The thinker of Yellow hat takes care of the positive evaluation,

the same way that the thought of Black hat takes care of the negative evaluation.

The thinker investigates and explores, in search of value and benefit and then find

logical endorsement for this value and benefit. For anti-collision selective process, the

thinker who wears Yellow hat point out the advantage of the selected anti-collision

technique, and why it is necessary to keep current decision.

• Green hat - New Ideas & Alternatives: - The Green hat is for the creative

thought. The search of alternatives is a fundamental aspect of the thought of Green

hat. For anti-collision selective process, the thinker of Green hat will provide al-

ternatives in the case of both deterministic and probabilistic algorithms, which have

the same weight of positive and negative impacts.

• Blue hat - The Big Picture: - The thinker of Blue hat will be thinking about

thinking, and set objective for each section. The Blue hat is the hat of the control.

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The thinker of Blue hat must organises the same thought, and to think about the

thought necessary to investigate the subject. The thinker of Blue hat is like a

conductor, who proposes and makes use of the other hats. For anti-collision selective

process, the thinker of Blue hat will decide who to put on each hat, and what are

the main scope of the overall selective process.

6.3.3.2 Global Trading Enterprise (GTE) Scenario

Global Trading Enterprise (GTE) is a large international business, with Many-to-Many

relationship between suppliers and consumers. GTE imports products from different coun-

tries then repackaged and exported them internationally and locally to different compa-

nies. The company involves large amount of inventories, which are stocked into special

warehouse with four different zones as shown in Figure 6.8.

Figure 6.8: Six Thinking Hats: Global Trading Enterprise (GTE) Scenario

After analysing information given from Figure 6.8, Table 6.4 displays the preferred

algorithm for each location that will provide the optimal quality of collected data from

GTE scenario.

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Table 6.4: Preferred Anti-Collision Method for Each Location (Zone 1 - 4) in GTE scenarioLocation Joined Q-ary Tree PCT Group PCT no GroupZone One X X XZone Two X X X

Zone Three X X XZone Four X X X

6.3.3.3 Decision Making Phase

When applied Novel Decision Tree Strategy and Six Thinking Hats Strategy for GTE

scenario, different conclusions for anti-collision methods deployment were acquired. The

steps of decision-making processes are as follows:


• Question: Does the corporation involve trading? Answer: Yes, GTE is a global

trading company


to M relationship? Answer: No, GTE have more than one supplier and consumer

all over the world

• Question: Is the total number of tag in an interrogation zone exceeding x tags?

Answer: Yes, GTE’s warehouse stored numerous numbers of goods in storages and

used RFID system to monitor and control inventories.


Technique”.

According to the decision tree outcome, GTE should employ a PCT group as its anti-

collision method for all locations.

Six Thinking Hats

• White Hat: For GTE scenario, the thinker who wears the White hat goes for

realistic data and stays with the Novel Decision Tree assessment, which is to select

the PCT deployment for all four zones.

• Red Hat: The Red hat is put on by local warehouse staff who knows the environ-

ment better than the board of directors. Thus, the Red hat wearer has decided that

different anti-collision techniques should be deployed for the different zones.

• Yellow Hat: In this scenario, the thinker who wears the Yellow hat points out the

advantage of the selected anti-collision technique and why it is necessary to keep

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the current decision. The thinker has decided on deploying only PCT, since it is

simple to order one lot of hardware and software from the same supplier, and to

avoid unnecessary procedures and time frames for implementation.

• Black Hat: Logically, at the unloading zone (zone one), trucks usually arrive from

the same company/suplier. In addition, at zone three where tagged items are moved

along the conveyer belt, realistically it is impossible to have more than one hundred

cases of alcohol sitting on the belt. Thus, the Black hat thinker decided that different

anti-collision algorithm must be deployed at both zones one and three.

• Green Hat: The Green hat wearer agrees with the Black and Red hat wearers

since the Green hat takes care of the old ideas and presents alternatives. However,

because the options are strictly limited to either deterministic or probabilistic for

each zone, Green hat decides on applying Joined Q-ary Tree to zone one instead

of PCT; and also suggests PCT no group for zone three, as not many tags will be

present on the conveyer belt.

• Blue Hat: The thinker of the Blue hat will be thinking about thinking and set

objectives for each section. For the anti-collision selective process, the thinker of

the Blue hat is to deicide who to put on each hat and what is the main scope of

the overall selective process. From the overall analysis, the Blue hat has decided to

employ both types of anti-collisions and to apply them to different zones.

According to the Six Thinking Hats Strategy, GTE should employ a PCT group at

zone two and zone four only, since these two zones are involved with arbitrary goods.

The Six Thinking Hats strategy has recommended that the Joined Q-ary Tree is deployed

instead of the PCT group at zone one because arriving items from supplier are usually

delivered from the same supplier. At zone three, it is recommended that the PCT no group

is implemented since this location is involved with arbitrary goods, but does not involve

a numerous number of tags.

6.3.3.4 Solution Phase

For a complex scenario such as GTE, complex kinds of thinking are needed, in order to

obtain the optimal result from each anti-collision algorithm. The Six Thinking Hats can

correctly identify the best algorithms for all four zones, as shown in Table 6.5. The Novel

Decision Tree, however, can only obtain correct algorithms for zones two and four. This

is because the Novel Decision Tree only takes into consideration the facts and figures

without any concern for special circumstances unforeseen, or for specific environmental

requirements. Thus, for zone one and zone three where the information provided is am-

biguous, the Novel Decision Tree cannot correctly identify the suitable algorithm.

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6.4. APPLICABILITY OF ANTI-COLLISION TECHNIQUES IN REAL WORLDSCENARIO

Table 6.5: Selected Anti-Collision Method using Decision Tree and Six Thinking HatsStrategies. Joined Q-ary Tree = JQT; PCT Group = PCT-G; PCT no Group = PCT-NG

Novel Decision Tree Six Thinking HatsLocation JQT PCT-G PCT-NG JQT PCT-G PCT-NGZone One X X X X X XZone Two X X X X X X

Zone Three X X X X X XZone Four X X X X X X

From the investigation, we have discovered that different anti-collision method has

advantage over the other in some cases. We found that by correctly identifying the most

suitable anti-collision technique, using our proposed Novel Decision Tree Strategy and

Six Thinking Hats Strategy, the data collection process can be improved; and the chain

reaction toward the next level of data transformation, aggregation, and event processing

can be decreased. Thus, it is important that the correct type of anti-collision algorithm

is applicable to different scenarios.

The next two sections explain the applicabilities of both Joined Q-ary Tree and Prob-

abilistic Cluster-Based Technique, in accordance with sample real world scenarios.

6.4 Applicability of Anti-Collision Techniques in Real World Sce-

nario

This section demonstrates sample scenarios, in which our proposed Joined Q-ary Tree

and Probabilistic Cluster-Based Technique, can be applied. The first scenario is a Wine

Warehouse Tag-and-Ship Scenario, where a deterministic Joined Q-ary Tree can be utilised

as an adequate anti-collision scheme. The Joined Q-ary Tree is deployed within the RFID

reader device and communicates with RFID passive tags presented within the interrogation

zone. The second scenario is a Document Warehouse Scenario, where a Probabilistic

Cluster-Based Technique is exploited as an anti-collision approach. Both scenarios are

explained in detail, in the following subsections.

6.4.1 Wine Warehouse Tag-and-Ship Scenario

The Wine Warehouse Tag-and-Ship Scenario is where a business decided to select its

own encoding scheme, reader type, tag type, EPC pattern, and middleware vendor. The

warehouse involves huge amount of inventory with many pallets of goods traveling trough

the supply chain. The tags must be printed according to selected EPC pattern within

the organisation. Then, after all tagged items are deployed around the warehouse, they

can be tracked and traced easily. The benefit of utilising a RFID system toward this

scenario is that, inventories within the warehouse can be tracked in real-time automatically,

which minimised cost of manual labour and maximised the visibility of items. In the

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Wine Warehouse Tag-and-Ship Scenario, the Joined Q-ary Tree is employed as an anti-

collision, because the scenario involves RFID data from the same source and has massive

tag movement.

Figure 6.9: Wine Warehouse Tag-and-Ship Scenario

Figure 6.9 illustrates a sample Wine Warehouse Tag-and-Ship Scenario, and the appli-

cability of Joined Q-ary Tree, as an anti-collision approach. The figure shows sample set

up including the environment area, items, readers, tags, middleware, and the operation

process. The detail of Figure 6.9 is explained as follows:

• Complex Event Processing - Complex Event Processing (CEP) consists of many

events that happen across all layers of a RFID organisation. It identifies the most

meaningful events within massive amount of events; analyses their impact; and takes

subsequent action in real time. Complex Event Processing refers to processed states,

the changes of state, or time. An event may be observed as a change of state with

any physical or logical, or otherwise other condition in a RFID system.

150


• Device Service Provider Interface (DSPI) - A device service provider interface

(DSPI) component can provide a uniform manner to communicate and manage a

RFID device. The DSPI component can include a receiver component that receives

one or more RFID server data, and RFID device data. The interface can be defined

to handle discovery, configuration, communication, and connection management.

• RFID Printer - A printing device used to write data to a RFID tag that can also

print any graphics, barcodes and text onto the label.

• RFID Reader - A transmitter or receiver that reads the contents of RFID tags

in the proximity. The maximum distance between the reader’s antenna and the tag

varies, depending on the type of reader and tag, and the RFID application.

• Middleware - RFID middleware is placed between the reader and the enterprise

applications and database systems. According to EPC specifications, middleware ap-

plications handle the tag and reader data originating from different sources. RFID

middleware fulfills and supports the unique EPC data of the items that are being

tagged and aggregate massive amount of data before it reaches the enterprise appli-

cations. Data is routed and converted into formats, as per the requirements of the

various applications.

• Database - A database is a collection of information that is organised and stored

in a computer system so that it can easily be accessed, managed, and updated. In

one view, databases can be classified according to types of content: bibliographic,

full-text, numeric, and images.

• Application - An application is a computer software designed to perform a specific

function, singular or multiple related specific tasks, directly for end user. Exam-

ples of application programs include database programs, development tools, and

communication programs. Application programs use the services of the computer’s

operating system and other supporting programs.

• Untagged Item - Item newly arrived and has not yet been tagged.

• Tagged Item - Item that has been tagged by a RFID printer.

The detailed process of the Wine Warehouse Tag-and-Ship Scenario are as follows:

After the cases have been picked and are ready to be tagged, an operator uses a Tag

Printing application and an RFID Printer to print and apply tags onto each case of wine.

The Tag Printing application stores the information about the tags that it prints in the

CasesTagged database. When the user clicks “Print Tag” in the Tag Printing application,

the “Print Tag” command is processed in the following way:

• The Print Tag command with the tag information communicates with the event

processing engine.

151


• The event processing engine sends the “Print Tag” command to the Printer Device’s

provider, and the “Print Tag” command is queued in the Command Processing

Thread of the provider.

• The Command Processing Thread converts the tag information from the DSPI stan-

dard format to the proprietary tag format of the Printer Device’s provider.

• The tag information including EPC number is transferred to the RFID Printer, using

the Printer Device Host protocol.

• The RFID Printer prints the tag, and the tag is applied to the case of wine, either

manually or by using an automated system.

• The Tag Printing application puts the information about the tag that was printed

in the CasesTagged database.

The tagged cases move along a conveyor belt toward the exit where a RFID Reader

is located. The Reader reads the tag on each case and sends all tag information through

middleware vendor. The middleware then stores each received tag in the CasesShipped

database. The information in this database is used for business analyses, such as comparing

information in the CasesTagged database and CasesShipped database. This ensures that

all cases of wine being shipped to the customer have been tagged. The tag information is

sent to the reader’s provider and is processed in the following way:

• The provider has a translating mechanism that converts the tag information from

the Reader Device’s provider proprietary format, into the DSPI standard format.

• During the data capturing process, the Reader Device performs the Joined Q-ary

Tree anti-collision protocol to reduce tag collision transmission, and also perform

other filtering algorithms to eliminate data errors.

• The tag-read event travels to the shipping process in RFID services, where the event

handler is pre-coded to send the tag information to the CasesShipped database.

• The CasesShipped database and CasesTagged database can be compared, to check

if all wine cases that entered the warehouse have reached the exit and have been

forwarded for shipping.

6.4.2 Document Warehouse Scenario

The Document Warehouse Scenario shows an example of an enterprise that receives items

from various vendors. The document warehouse involves huge amount of inventory where

data source can be from various locations and each tag requires specific encoding scheme,

but the enterprise does not produce its own printed tags. The benefit of utilising a RFID

152


system toward this scenario is that inventories within the warehouse can be tracked in real-

time automatically, which minimise cost of manual labour and maximise the visibility of

items. In the Document Warehouse Scenario, the Probabilistic Cluster-Based Technique is

employed as an anti-collision, since the scenario involves RFID data from different sources

and has massive tag movement.

Figure 6.10: Document Warehouse Scenario

Figure 6.10 illustrates a sample Document Warehouse Scenario and the applicability

of Probabilistic Cluster-Based Technique as an anti-collision method. The figure shows

sample set up, including the environment area, items, readers, tags, middleware, and the

operation process. The definition of Complex Event Processing, DSPI, RFID Reader,

Middleware, and Database, were explained from the previous subsection. The detailed

process of the Document Warehouse Scenario are as follows:

The Document Warehouse Scenario does not involve printing process as in the Wine

Warehouse Tag-and-Ship Scenario. Several inventories, which have already been tagged,

153


are received from different vendors. After the tagged cases have been picked from several

locations, each document case moves along a conveyor belt toward the exit where a RFID

Reader is located. The Reader reads the tag on each case and sends all tag information

through middleware vendor. The middleware then stores each received tag in the Inventory

database. The information in this database is used for business analyses, such as document

tracking and tracing. This ensures that all document have been received and ready to be

sent out to other locations. The tag information is sent to the reader’s provider, and is

processed in the following way:

• The provider has a translating mechanism that converts the tag information from

the Reader Device’s provider proprietary format, into the DSPI standard format.

• During the data capturing process, the Reader Device performs the PCT anti-

collision protocol incorporates with PTES tag estimation scheme, to reduce tag

collision transmission and also perform other filtering algorithms to eliminate data

errors.

• The tag-read event travels to the inventory checking in RFID services, where the

event handler is pre-coded, to send the tag information to the Inventory database.

6.5 Summary

In this chapter, we have conducted a comparative analysis of our proposed deterministic

and probabilistic anti-collision approaches. Additionally, we introduced the novel con-

ceptual selective technique management for precise anti-collision methods selection. The

main contributions and findings of the chapter are as follows:

• We have performed a comparative analysis between our proposed Joined Q-ary Tree

and Probabilistic Cluster-Based Technique. We found that both methods have ad-

vantages and disadvantages over one another, depending on each specific case. The

Joined Q-ary Tree is more suitable for tags within specific EPC pattern, while Prob-

abilistic Cluster-Based Technique is more suitable for arbitrary tags.

• We proposed two novel strategies, 1) the Novel Decision Tree and 2) the Six Thinking

Hats, for anti-collision method selection process (Pupunwiwat et al., 2011). We de-

termine that correct anti-collision method for specific cases reduced Chain Reaction

impact toward long-term RFID data management.

• We also demonstrated the applicability of our proposed techniques toward real world

scenarios.

154

7Conclusions

In this chapter, we concluded the thesis with a summary of our contributions and future

research directions.

7.1 Summary of Contributions

In this study, we address a question on making anti-collision methods for RFID data

streams collision more efficient than current available approaches. The thesis first discusses

the background information on RFID, why it is important, and motivation for investigating

suitable anti-collision techniques toward different scenarios. We assess relevant literature

and provide information on existing state-of-the-art anti-collision methods.

The thesis focuses on three main research problems: firstly, designing and developing

a new deterministic anti-collision technique; secondly, improving frame-size estimation

method and introducing new group based technique for probabilistic anti-collision; and

finally, initiating two new strategies for selective technique management.

The contributions of each chapter are summarised as follows:

In Chapter 2, we presented an overview of RFID technology. Particular attentions

were given to the structure of the RFID system including various types of antennas,

readers, and tags; and the issues surrounding the deployment of RFID. We identified the

characteristic of RFID data, which is fundamental for RFID data management. We also

provided sample of main RFID commercial applications and explained the importance of

RFID technology in supply chain. We highlighted that for RFID data management, the

155

CHAPTER 7. CONCLUSIONS

data should be absorbed closer to the source, which caters to the need of pre-processing

data, so that only relevant and meaningful information is passed to the next stage. Thus,

it is important that the raw data must be filtered before being passed into the applications.

In Chapter 3, we reviewed the literature and classified existing RFID data streams

filtering methods into general data stream filtering and anti-collision. We then identified

and analysed the benefit and detriment of each existing method. We discovered that for

deterministic anti-collision method, major limitations are due to memories and compu-

tational complexity requirements in memory anti-collision algorithms. The memoryless

QT algorithms also perform poorly in the case where there are a large number of tags

within an interrogation zone. Several improved version of QT enhance the performance

and reduce identification delay but comes with huge implementation cost. For probabilis-

tic anti-collision method, the earlier type of ALOHA anti-collision performs poorly, while

the more advanced Framed-Slotted ALOHA has better performance. However, literature

review demonstrated that existing methods from Framed-Slotted ALOHA category are

either inefficient or too complex, with high overhead cost of implementation.

Also in Chapter 3, we defined the research questions in relation to RFID anti-collision.

Given that both deterministic and probabilistic anti-collision algorithms have different ad-

vantages and disadvantages toward development of real world applications, it is important

to construct anti-collision methods that are simple, with low overhead computation, and

perform effectively, compared with existing techniques. We concluded that the basic Query

Tree is the most effective deterministic anti-collision approach, and the Framed-Slotted

ALOHA is arguably the best approach in probabilistic anti-collision. Thus, our research

focused on the development of new methods that perform better than these existing tech-

niques.

In Chapter 4, we investigated the different representation of RFID data encoding, in

order to better understand the importance of each data encoding scheme. By constructing

complex anti-collision algorithms, high memory capacity and power sources are needed,

which is impractical in RFID system. Furthermore, no study has been undertaken to

construct a basic deterministic anti-collision, under the assumption that we have a limited

power source from RFID reader and limited memory in both readers and tags. Thus, we

proposed a Unified Q-ary Tree, which is a basic combination of two Q-ary trees, in order

to identify the best performing Q-ary tree for particular circumstance. From our empirical

study, we discovered that the best performing tree out of the four Q-ary trees is 4-ary tree,

while number of tags within the interrogation zone has no impact on the performance.

Overall, we also found that a combination of 2-ary tree and 4-ary tree performed the best,

by reducing the total memory usage required, and by minimising the identification delay.

In general, by using a lower level 2-ary tree for Identical bits of EPC data, and by using a

higher level 4-ary tree for Unique bits of EPC data, the total number of bits for querying

can be decreased.

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7.1. SUMMARY OF CONTRIBUTIONS

To further overcome the limitation of current tree-based anti-collision techniques, in

Chapter 4 we proposed a Joined Q-ary Tree based on findings from the evaluation of the

Unified Q-ary Tree. The Joined Q-ary Tree is the enhanced Q-ary Tree, which adaptively

adjusted its breaches between the two Q-ary Trees, according to the EPC pattern. From

experimental evaluation, the Joined Q-ary Tree outperformed the existing approaches in

terms of memory usage, which resulted in minimal identification time. We also confirmed

that different encoding schemes have impacted on the performance of all tree-based meth-

ods. Nevertheless, the Joined Q-ary Tree always perform the best, compared with naive

approaches, regardless of encoding scheme applied. We have also identified and confirmed

certain properties of importance for the deterministic anti-collision methods.

In Chapter 5, we described our new probabilistic anti-collision approaches. In proba-

bilistic anti-collision, two key features for the most efficient performance of identification

procedure are frame-size estimation technique and the probabilistic anti-collision method

itself. To overcome the inaccuracy of the current frame-size estimation techniques, we

proposed a Precise Tag Estimation Scheme for a more accurate Backlog prediction. The

Precise Tag Estimation Scheme uses various variables and number of slots from previous

identification round to predict the number of remaining tags. Empirical study has shown

that by limiting initial Q to a specific value and by using particular variables for slots pre-

diction, our method provided more accurate Backlog estimation compared with existing

frame-size prediction techniques.

In addition to our proposed frame-size estimation technique, in Chapter 5, we also

proposed a Probabilistic Cluster-Based Technique, in order to improve the performance ef-

ficiency from current probabilistic anti-collision methods. The Probabilistic Cluster-Based

Technique utilised group splitting rules derived by using particular equations, according

to the optimal system efficiency obtained for specific number of tags. We first conducted

an experiment to acquire optimal frame-size for specific number of tags. Subsequently,

from the results acquired for performance efficiency evaluation, we have developed sev-

eral equations to calculate boundaries and exploit rules. We then integrated the Precise

Tag Estimation Scheme as an accurate frame-size prediction for our Probabilistic Cluster-

Based Technique. The experimental evaluation demonstrated that our proposed method

is the most effective method, in terms of system efficiency and number of slots minimisa-

tion. From the analysis of all experiments, we have also recognised certain properties of

importance for probabilistic anti-collision methods.

In Chapter 6, we analysed and compared our proposed Deterministic Joined Q-ary

Tree and Probabilistic Cluster-Based Technique, and determined the best-fit method for

specific circumstances. Empirical analysis shows that the Joined Q-ary Tree method can

achieve higher efficiency if the right EPC pattern is configured. However, for arbitrary

situations where EPC pattern cannot be found, it is more preferable to use probabilistic

approach rather than the deterministic method. We then introduced two new strategies

for selective anti-collision technique management. These two novel techniques focused

157


on accurately selecting suitable anti-collision methods for particular scenarios, instead of

employing complex all-rounder anti-collision. Empirical evaluation demonstrates that a

more accurate anti-collision method is selected using our proposed selective techniques,

and the correct anti-collision method for specific cases can then reduce the Chain Reaction

impact toward long term RFID data management.

To demonstrate the use of our proposed anti-collision methods in real world

scenarios, in Chapter 6, we also discussed the applicability of both Joined Q-ary Tree

and Probabilistic Cluster-Based Technique. The sample scenarios show the conceptual

applications of RFID technology within the supply chain management. Our

anti-collision methods are applicable at the earliest stage of the RFID architecture,

where RFID readers and tags are communicated at the physical layer.

More specifically, the thesis makes the following contributions:

• We investigated different existing approaches on RFID data stream filtering, specif-

ically the state-of-the-art anti-collision methods.

• We proposed deterministic anti-collision methods (Pupunwiwat and Stantic, 2009a),

(Pupunwiwat and Stantic, 2009b), (Pupunwiwat and Stantic, 2010c), which over-

come limitation of previous studies and proved that our methods have the most

superior result, regardless of encoding schemes applied.

• We proposed probabilistic anti-collision methods, including the frame-size prediction

technique (Pupunwiwat and Stantic, 2010a), (Pupunwiwat and Stantic, 2010b) and

the novel group based anti-collision technique (Pupunwiwat and Stantic, 2010d),

which can efficiently improve the system performance, compared with existing ap-

proaches.

• We performed comparative analysis of our two proposed deterministic and proba-

bilistic anti-collision methods, and identified the benefits and disadvantages of each

approach.

• We introduced two novel strategies for selective anti-collision technique management

(Pupunwiwat et al., 2011), and confirmed that by correctly identifying the most

suitable anti-collision method for specific scenario, the Chain Reaction toward long

term RFID data management can be reduced.

• We demonstrated sample scenarios of how our proposed anti-collision methods can

be applied in the supply chain management.

158

7.2. FUTURE WORKS

7.2 Future Works

In this thesis, we have addressed issues directly related to anti-collision, which is a part

of major data streams filtering in RFID. However, the implementation and integration of

efficient anti-collision techniques is only the first step to improve data quality, and further

process of data is required in order to successfully complete all stages of RFID data

stream management. Although anti-collision is the most important step to determine

the quality of RFID data, it is also important to consider other potential issues in data

management. There are several open research questions regarding RFID data streams

filtering and management, which should be addressed in the future. These open research

questions are described as follows:

• In the context of RFID anti-collision, it would be interesting to evaluate Reader-to-

Reader or Reader-to-Tag anti-collision techniques. Despite the fact that Tag-to-Tag

collision is the most critical problem for RFID data management, it is necessary to

look into other prospective collisions in order to optimise the quality of captured

data.

• As for RFID Tag-to-Tag anti-collision, it is possible to target other anti-collision

methods constructed in SDMA, FDMA, or CDMA. Even though TDMA is the

largest group of anti-collision procedures, it is essential to take into consideration,

algorithms from other divisions in order to offer wider range of anti-collision ap-

proaches for specialised applications.

• It is also feasible to perform a comparative cost analysis for both Tree-based and

ALOHA-based anti-collision techniques. While performance studies have been done

on identification time, memory usage, and performance efficiency, there is no evi-

dence confirming the cost analysis for RFID anti-collision.

• For both deterministic and probabilistic anti-collision methods, there is a need for

a new measurement model that will determine the capability of each anti-collision

approach, based on every constraints including identification time, memory usage,

performance efficiency, and cost consumption. Currently, different types of analysis

have been done separately, thus results may be bias.

• As our main research goal concentrates on the implementation of simple anti-collision

algorithms, we have not looked into the Hybrid anti-collision techniques, which com-

bine both deterministic and probabilistic anti-collision together. Hybrid approaches

may be a good solution for specific real-world applications.

• It would be interesting to construct an actual fully automated data management

engine for specific organisation. The structure should involve the most suitable

anti-collision techniques and appropriate filtering methods for noises, missed reads,

unreliable reads, and duplications.

159


• While general data stream filtering methods for noises, missed reads, unreliable reads,

and duplications are adequate, it still remains an open problem to find the strategy

that can select the most suitable technique for each specific scenario. There is a

possibility that our two proposed selective anti-collision technique management can

be used as selective filtering technique management in general.

• Another open area of research is to look into other available strategies that can

be applied as a selective technique management. Beside Decision Tree and Six

Thinking Hats strategies, different approaches from other research domains should

be considered.

• Data management, which is the most crucial project in RFID system, involves many

procedures beside the data filtering process. These include data transformation, data

aggregation, event management, data warehousing, and data mining. Each stage of

data management must be integrated together before they can be deployed to the

actual organisation. Thus, more research must be developed case-by-case to verify

that all necessary stages of data management are compatible with each other and

that they can be integrated and deployed to the chosen organisation.

160

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