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Every Bit Counts – Fast and Scalable RFID Estimation

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Presentation on theme: "Every Bit Counts – Fast and Scalable RFID Estimation"— Presentation transcript:

1 Every Bit Counts – Fast and Scalable RFID Estimation
Title: <catchy word play> - <what the paper is about> Every Bit Counts – Fast and Scalable RFID Estimation Muhammad Shahzad and Alex X. Liu Michigan State University, USA MobiCom 2012 Course: Advanced Topics on the Internet of Things Presented by Jeroen Delvaux (student ID )

2 Problem: RFID Cardinality Estimation
= counting tags ID = …01 We assume 1 stationary reader RFID tag range The overhead of an exact count is fundamentally high RFID reader Goal: Estimate population size as fast as possible Jeroen Delvaux RFID Cardinality Estimation - Introduction Slide 02/12 (+1)

3 Why Counting RFID Tags? Example use cases
Assumption: no need to identity individual tags Supermarket – product inventory & restocking Logistics & Distribution – monitor manufactured, packaged and shipped quantities Music festival – crowd management Jeroen Delvaux RFID Cardinality Estimation - Introduction Slide 03/12 (+1)

4 Presentation Outline Problem + Motivation Proposed solution + Review
Anti-Collision Protocols (preliminaries) Tags should not send data simultaneously Problem + Motivation Proposed solution + Review “i am 95% sure that the number of tags is between 410 and 430” Related work + Conclusion Jeroen Delvaux RFID Cardinality Estimation – Anti-Collision Slide 04/12 (+1)

5 Anti-Collision Protocols
We are not supposed to all speak at the same time frequency-based time-based location-based ALOHA + variants Tree search + variants Our scope Narrow down IDs until only one tag replies Hawaiian: ‘peace’, ‘affection’ English mutation: ‘hello’, ‘goodbye’ ID = 01****** = 011***** = 0110**** Randomize the waiting time for tags to reply Jeroen Delvaux RFID Cardinality Estimation – Anti-Collision Slide 05/12 (+1)

6 Framed Slotted ALOHA Randomized waiting time ∈ {1,2,3,4} for each tag to send 1 data packet Standardized by EPCGlobal (Class 1 Generation 2 tags) slot Electronic Product Code frame frame A A data data rand = 3 B B data data rand = 1 C data data D D data data rand = 4 1 ≥2 1 1 1 ≥2 all rand = 3 time C Reader accepts data if exactly one tag replies; reject in case of a collision Jeroen Delvaux RFID Cardinality Estimation – Anti-Collision Slide 06/12 (+1)

7 Framed Slotted ALOHA (continued)
Random number derivation, at the start of each frame: rand = Hash(seed, ID) frame frame 1 ≥2 1 1 1 ≥2 A rand = 3 B Reader-driven protocol (synchronous) rand = 1 D ‘Democratic’: each tag has an equal probability of getting its data accepted by the reader broadcast seed rand = 4 rand = 3 Main parameter: frame size (#slots) C Jeroen Delvaux RFID Cardinality Estimation – Anti-Collision Slide 07/12 (+1)

8 Presentation Outline Problem + Motivation
Anti-Collision Protocols (preliminaries) Tags should not send data simultaneously Problem + Motivation ALOHA Proposed solution (builds upon ALOHA) + Review “i am 95% sure that the number of tags is between 410 and 430” Related work + Conclusion Jeroen Delvaux RFID Cardinality Estimation – Proposed Solution Slide 08/12 (+1)

9 Proposal for Counting RFID Tags
Averaged over 10 frames. In paper, 100 frames (smoother). ALOHA Frame with 16 slots run length 0 tags saturation 25 tags 1 2+ 1 2+ 1 2+ 2+ 1 2+ 1 2+ 2+ 2+ error 100 tags 2+ 2+ 2+ 2+ 1 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ inverse curve = job done! The more tags, the more non-empty slots Estimator: the averaged length of sequences of non-empty slots (runs) error MATLAB simulation 2+ 1 2+ 2+ 2+ Number of tags Jeroen Delvaux RFID Cardinality Estimation – Proposed Solution Slide 09/12 (+1)

10 Estimator seems Crude Does not distinguish ‘1’ and ‘2+’
Not discussed in the paper Does not distinguish ‘1’ and ‘2+’ Authors provide formal mathematical analysis of their estimator (30 equations) 2+ 2+ 2+ 2+ 1 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ 2+ more tags 2+ 1 2+ 2+ 1 2+ 2+ 1 1 1 2+ 1 2+ 2+ 1 1 less tags 1 run having 16 slots => same number of tags Overlooks that slots are independent events However, the proposed estimator does not reach full potential and seems ‘arbitrarily’ chosen Average run length = 5 => more tags 1 1 1 1 1 same number of tags 1 1 1 1 1 Average run length = 1 => less tags Jeroen Delvaux RFID Cardinality Estimation – Proposed Solution Review Slide 10/12 (+1)

11 Why Not a More Informative Estimator?
averaged over 10 frames Inversion seems more reliable Rather shaky curve Run length (discussed in paper) Slot type fractions (not in paper) ≥2 Smaller ripple 1 Number of tags Number of tags Jeroen Delvaux RFID Cardinality Estimation – Proposed Solution Review Slide 11/12 (+1)

12 Conclusion & Related Work
[Chen et al., “Understanding RFID Counting Protocols”, MobiCom 2013] Many RFID counting protocols have been proposed (10+) Not necessarily build upon ALOHA nor having a stationary reader The proposed solution does not reach full potential (yet) The chosen estimator (run length) is rather crude More attention needed regarding two-phase approach: coarse + refinement Initial parameter values (frame size, tag reply probability) More optimal parameter values Rough estimate # tags More accurate estimate # tags Thank you! Questions? Jeroen Delvaux RFID Cardinality Estimation – Conclusion & Related Work Slide 12/12 (+1)

13 Scalability Trick Measure large populations with a small frame size (maintain speed) 16 slots per frame 10 frames averaging At the expense of accuracy loss (curves become less smooth) Probability that a tag competes in a given frame ≠1 e.g., 1/4 Count up to 100 tags Count up to 400 tags Jeroen Delvaux RFID Cardinality Estimation – Proposed Solution Slide (+1)


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