Physical-layer Identification of UHF RFID Tags Authors: Davide Zanetti, Boris Danev and Srdjan Capkun Presented by Zhitao Yang 1

Outline 1)Main idea 2)Experimental setup 3)Collected data 4)Feature extraction and matching 5)Performance results 6)Conclusion 2

1. Main idea Goal: study the feasibility and the accuracy of physical-layer identification and classification of passive UHF RFID tags. Identification: based on accept/reject decisions, the fingerprint of tag is verified against a reference fingerprint (1:1 comparison). Classification: the tag is assigned to one and only one class. 3

The main clue 1)The reader sends the acquisition to the tag with out of specification commands; 2)The tag is challenged and response RN 16 as backscatter data; 3)The reader extracts timing and spectral features from the collected data. 4

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What is RN16? Answer: 16 bits random number. 6

2. Experimental setup Experiment configuration: Sample: 70 EPC class-1 generation (C1G2) RFID tags Carrier frequency: MHz Baseband: pulse-interval encoding (PIE) Modulation: phase-reversal amplitude shift keying (PR-ASK) Reader ADC sample rate: 1 GS/s Data width: 8 bits. 7

Reader’s hardware 8

The communication process between reader and tags 1)The reader send a “select” command to a particular tag; 2)The reader initiate an inventory round (Query); 3)The tag replies an RN16 with fixed preamble; 4)The reader responses an “ACK”. 9

Operation frequency band: out of specification band Why? The backscatter link frequency (BLF) tolerance and variation during backscatter are higher at an out-of-specification frequency. The manufacturers mainly focus on tag responses within the specified frequency range. 10

TRcal: a parameter to specify the backscatter link frequency 11

Parameter configuration 12

3. Collected data Dataset 1: to evaluate the identification and classification accuracies; Dataset 2: to estimate the stability of proposed techniques considering different configurations of tag position, orientation, and transmission power; Dataset 3,4,5: to analyze the classification accuracy between different tag models and within each model, and also validate the accuracy of the proposed techniques for different TRcal values. 13

4. Feature extraction and matching Time domain feature Time interval error (TIE) is to measure how far each active edge of the clock varies from its ideal position. It is used to measure precise and stable behavior. 14

Time domain feature - TIE TIE is linear, Y = a * x +b So, the calculated coefficient a is, which is defined as a feature for fingerprinting UHF RFID tags. The preamble part of the tag response is used to compute. Because the fixed preamble can help avoid any data-dependent bias in evaluation 15

Time domain feature – Average baseband power 16

Spectral features 1)Separate the RN 16 preamble of each collected sampled signal into single clock cycles 2)Compute the spectral feature of each of those clock cycles 3)Computer the average of the spectral features of all single clock cycles. The fingerprint of spectral feature is the average value. 17

5. Performance results promising worst better best Classification 18

Identification Identification accuracy of the feature of different number of RN16 preambles When the false acceptance rate and false rejection rate are equal, the common value is equal error rate. The spectral feature for different number of RN16 preambles and subspace dimensions 19

Stability 20

The influence of TRcal Conclusion: Although in some case out-of- specification Trcal times lead to a more accurate classification, no generalization can be done on the relationship between classification accuracy and TRcal length. 21

Implementation in cloning detection in RFID-enabled supply chains The requirement: high computational speed and accuracy. The proposed method can reach high accuracy (EER=0%) and computational speed (100 tags/s) using spectral features. Not suitable for pallet-scenario, because it sensitive to tag’s position Suitable for conveyor scenario, because tags are identified at a time and a fixed position. 22

6. Conclusion Using 70 UHF RFID tags under an experiment to prove that these tags can be classified and identified independently of the location and distance to the reader. 23