RFID Object Localization

Gabriel Robins and Kirti ChawlaDepartment of Computer Science

University of Virginia

robins@cs.virginia.edu kirti@cs.virginia.edu

Outline

• What is Object Localization ?• Background • Motivation• Localizing Objects using RFID• Experimental Evaluation• Conclusion

02/33

What is Object Localization ?

Goal: Find positions of objects in the environment

Problem: Devise an object localization approach with good performance and wide applicability

03/33

Objects Environments

Current Situation04/33

Lots of approaches and applications lead to vast disorganized research space

• Inapplicable

• Not general

• Mismatched

• Identify limitations

• Determine suitability

Techniques

Signal arrival angle

Signal strength

Signal arrival time

Signal phase

Technologies

Satellites

Lasers

Ultrasound sensors

Cameras

Applications

Outdoor localization

Indoor localization

Mobile object localization

Stationary object localization

Localization Type05/33

Self Environmental

• Self-aware of position• Processing capability

• Not aware of position• Optional processing

capability

Localization Technique06/33

• Signal arrival time• Signal arrival difference time• Signal strength• Signal arrival phase• Signal arrival angle• Landmarks• Analytics (combines above techniques with analytical

methods)

RFID Technology Primer07/33

RFID reader RFID tag

• Passive• Semi-passive• Active

• Interact at various RF frequencies

Inductive CouplingBackscatter Coupling

Motivating RFID-based Localization08/33

• Low-visibility environments• Not direct line of sight• Beyond solid obstacles• Cost-effective• Adaptive to flexible application requirements• Good localization performance

State-of-the-art in RFID Localization09/33

Pure

RFID –based localization approaches

Hybrid

Contributions10/33

• Pure RFID-based environmental localization framework with good performance and wide applicability

• Key localization challenges that impact performance and applicability

Power-Distance Relationship11/33

Reader power Distance Tag power

NReader Power Wavelength

Reader Gain × Tag Gain ×Tag Power 4 × π × Distance

• Cannot determine tag position

• Empirical power-distance relationship

Empirical Power-Distance Relationship12/33

Insight: Tags with very similar behaviors are very close to each other

Tag Sensitivity13/33

• Variable sensitivities

• Bin tags on sensitivity

Average sensitiveHigh sensitive Low sensitive

Pile of tags

Key Challenges Results

25 % 54 % 8 %

13 %

Reliability through Multi-tags14/33

Platform design

Results

Insight: Multi-tags have better detectabilities (Bolotnyy and Robins, 2007) due to orientation and redundancy

Tag Localization Approach15/33

Setup phase Localization phase

Algorithm: Linear Search16/33

• Linearly increments the reader power from lowest to highest (LH) or highest to lowest (HL)

• Reports the first power level at which a tag is detected as the minimum tag detection power level

• Localizes the tags in a serial manner• Time-complexity is: O(# tags power levels)

Algorithm: Binary Search17/33

• Exponentially converges to the minimum tag detection power level

• Localizes the tags in a serial manner• Time-complexity is: O(# tags log(power levels))

Algorithm: Parallel Search18/33

• Linearly decrements the reader power from highest to lowest power level

• Reports the first power level at which a tag is detected as the minimum tag detection power level

• Localizes the tags in a parallel manner• Time-complexity is: O(power levels)

Reader Localization Approach19/33

Setup phase Localization phase

Algorithm: Measure and Report20/33

• Reports a 2-tuple TagID, Timestamp after reading a neighborhood tag

• Sorted timestamps identify object’s motion path• Time-complexity is: O(1)

Localization Error21/33

• Reference tag’s location as object’s location leads to error

• Number of selection criteria

Error-reducing Heuristics

Experimental Setup22/33

1

4

2

3

Y-axis

X-axis

Track design Mobile robot design

Experimental Evaluation23/33

• Empirical power-distance relationship• Localization performance• Impact of number of tags on localization performance

Empirical Power-Distance Relationship24/33

Localization Accuracy25/33

Algorithmic Variability26/33

Localization Time27/33

Performance Vs Number of Tags28/33

Diminishing returns

Comparison with Existing Approaches29/33

Hybrid

Hybrid

Visualization30/33

Accuracy

Work area

Antenna control

Heuristics

Deliverables31/33

Patent(s):1. Kirti Chawla, and Gabriel Robins, Method, System and Computer Program Product for Low-

Cost Power-Provident Object Localization using Ubiquitous RFID Infrastructure, UVA Patent Foundation, University of Virginia, 2010, US Patent Application Number: 61/386,646.

Journal Publication(s): 2. Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework,

International Journal of Radio Frequency Identification Technology and Applications, Inderscience Publishers, 2011, Vol. 3, Nos. 1/2, pp. 2-30.

Conference Publication(s):3. Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Efficient RFID-Based Mobile Object

Localization, Proceedings of IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, 2010, Canada, pp. 683-690.

4. Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Object Localization using RFID, Proceedings of IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306.

Grant(s): 5. Gabriel Robins (PI), NSF Grant on RFID Pending

Conclusion32/33

• Pure RFID-based object localization framework• Key localization challenges• Power-distance relationship is a reliable indicator• Extendible to other scenarios

33/33

Thank You

34

Backup Slides

Key Localization Challenges35

RF interference Occlusions

Reader localityTag spatiality

Tag sensitivity

Tag orientation

Back

Single Tag Calibration36

Constant distance/Variable power

Variable distance/Constant power

Back

Multi-Tag Calibration: Proximity37

Constant distance/Variable power

Variable distance/Constant power

Back

Multi-Tag Calibration: Rotation 138

Constant distance/Variable power

Back

Multi-Tag Calibration: Rotation 239

Variable distance/Constant power

Back

Error-Reducing Heuristics40

Heuristics: Absolute differenceM

1 I JJI=1

M M

2 I J I KJ,K I=1 I=1J K

M M

3 I J I KJ,KI=1 I=1J K

M M M M

4 I J I K I J I KJ,KI=1 I=1 I=1 I=1J K

J, K are neighbors

J, K are neig

H : Min( Δ (R ))

H : Min( Δ (R ) + Δ (R ))

H : Min( Δ (R ) + Δ (R ))

H : Min( Δ (R ) + Δ (R )) such that Δ (R ) < Δ (R )

hbors

Back

Error-Reducing Heuristics41

Heuristics: Minimum power reader selection

5 J KJ,K,S,QJ KS Q

6 J KJ,K,S,QJ KS Q

J, K are planar orthogonally oriented

S, Q are neighbors

H : Min (Δ (T) + Δ (T))

H : Min (Δ (T) + Δ (T))

Back

Error-Reducing Heuristics42

Heuristics: Root sum square absolute difference

M2

7 I JJ I=1

M M2 2

8 I J I KJ,K I=1 I=1J K

M M2 2

9 I J I KJ,KI=1 I=1J K

M M M2 2 2 2

10 I J I K I J I KJ,KI=1 I=1 I=1 I=J K

J, K are neighbors

H : Min( Δ (R ) )

H : Min( Δ (R ) + Δ (R ) )

H : Min( Δ (R ) + Δ (R ) )

H : Min( Δ (R ) + Δ (R ) ) such that Δ (R ) < Δ (R )

M

1

J, K are neighbors

Back

Error-Reducing Heuristics43

Localization error

Root sum square absolute difference

Meta-Heuristic

Minimum power reader selection

Absolute difference

Other heuristics

Back