rfid object localization
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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
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