Object Localization Using RFIDKirti Chawla

Department of Computer ScienceUniversity of Virginia

• The Problem of Locating Objects• Research Milestones• Background• Motivation• Proposed Approach• Experimental Evaluation• Conclusion

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Outline

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Locate

Environments

Goal: Find positions of objects in an environment

Hypothesis: Standard RFID is sufficient and effective

Key-factors: Performance, applicability and shortcomings

Locating ObjectsProblem

Objects

Research DeliverablesMilestonesJournal Publication: •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 Publications:•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•Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Object Localization using RFID, Proceedings of IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306Patent:•Kirti Chawla, and Gabriel Robins, Object Localization with RFID Infrastructure, US Patent Application Number: PCT/US2011/053067, filed with WIPO/USPTO September 2011Copyright:•Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, US Copyright Case Number: 1-633487801, 2011Startup Venture:•Co-founded Diorama Technologies LLC, in partnership with private investors•Raised venture capital funding and negotiated licensing terms•Other investors have shown strong interest in commercializing our ideas

WIPO/USPTOPatent

Object Localization with RFID Infrastructure

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Technologies

Mismatched Solutions

Limiting Constraints

Techniques

Current State of the ArtBackground

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RFID Reader RFID Tag

Near-field PropagationFar-field Propagation

Readers: Variety of form-factors and frequencies

Tags: Flexible power source, frequency, and form factors

RFID PrimerBackground

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Motivation

Dark Environment

No Line of Sight

Why locate objects using RFID ?

Cost EffectiveSolid

Obstacles

Natural Fit Adaptive

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Power-Distance RelationshipApproach

DistanceReader Power

Tag Power

Problem: Radio variability renders Friis equation practically useless

Insight: Utilize empirical power-distance relationship

NTag Power Wavelength

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

Comparison

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Antenna

Insight: Similarly behaving tags are close to each other

Radio WaveShared Region

Empirical Power-Distance Relationship

Approach

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Tag Sensitivity CharacterizationApproach

13%

25% 54% 8%

High Sensitive Average Sensitive Low Sensitive

Pile of Tags

Problem: Tags have variable sensitivities / performance

Insight: Bin tags based on their sensitivity

ResultsKey Challenges

RFID Tag

Vertical Horizontal

RFID Reader

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Reliability through Multi-TagsApproach

Problem: Optimal tag reads occur at certain orientations

Insight: Multi-tags provide orientation redundancy

Results

Platform Side View

Parallel Orthogonal

RFID TagPlatform Top View

Setup Phase

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Tag Localization ApproachApproach

Localization Phase

Signal Strength Metric: MTDP

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Tag Localization AlgorithmsApproach

Linear Search Binary Search Parallel Search

O(#Tags Log#Power-Levels)

O(#Tags #Power-Levels)

O(#Power-Levels)

Reader Output Power Range

0 MAXMID

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Reader Localization ApproachApproach

Setup PhaseLocalization Phase

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Localization ErrorApproach

Problem: Assumption that target and reference tag locations coincide leads to localization error

Insight: Consider other nearby reference tags

in order to minimize the localization error

Heuristics

Reference Tags

Target Tag

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Experimental SetupEvaluation

Robot DesignTrack Design

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Empirical Power-Distance Relationship

Evaluation

Insight: Only empirical power-distance relationship can provide high localization performance

Theoretical (N = 2)

Theoretical (N = 3)

Theoretical (N = 6)

Empirical

Back

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Localization AccuracyEvaluation

Insight: Performance can be improved by denser

reference tag deployment

Actual Position

Inferred Position

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Localization TimeEvaluation

Insight: Faster algorithms provide lower tag detectability

Linear Search (HL)

Parallel Search

Linear Search (LH)

Binary Search

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Localization Performance Vs #Tags

Evaluation

Insight: Localization performance varies with tag density

Diminishing returns

Approach

Average Time (minutes)Test Area

(m2)Localization

Error (m)Notes

Setup Phase Localization Phase

Ni et al., 2003 - - -2

Pure RFID

Alippi et al., 2006 - -20 0.68

Bekkali et al., 2007 - -9 0.5 – 1.0

Senta et al., 2007 - -2 0.2

Wang et al., 2007 - - -0.1 – 0.9

Zhang et al., 2007 - - -1 Hybrid

Seo and Lee, 2008 - -5 0.2 – 1.6 Pure RFID

Choi and Lee, 2009 - -14.4 0.02 Hybrid

Choi et al., 2009 - - -0.21

Pure RFID

Joho et al., 2009 27 - -0.38

Linear Search (LH)Linear Search (HL)Binary SearchParallel SearchMeasure and ReportCombined Approach

161.2329.7847.241.67

0161.23

5.281.421.951.67

010.32

8

0.270.290.310.350.250.18

Pure RFID

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Comparative EvaluationEvaluation

Preliminary new experiments show:

1) Reference tags optional

2) Instantaneous localization

3) High accuracy

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Applications

Warehouses

Hospitals

Supply Chains

Monitor life-critical events

Smart Carts

Provide ground truth

Tier-II Applications

Assisted Living

Location-Aware Services

Energy Saving in Buildings

Locating Objects

Tier-I Applications

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Object Location VisualizationInterface

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Future DirectionsConclusion

RFID-only Object Localization Framework:- Showed that pure RFID can be used for object localization

- Introduced a power-distance relationship metric

- Proposed tag binning to mitigate tag sensitivity variability

- Devised effective localization algorithms and heuristics

- Identified / mitigated key localization challenges

Future Research Directions:- Scalability

- Technology Evolution

- Localization performance

- Visualization tools

- Field testing and Commercialization

Research DeliverablesMilestonesJournal Publication: •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 Publications:•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.•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.Patent:•Kirti Chawla, and Gabriel Robins, Object Localization with RFID Infrastructure, US Patent Application Number: PCT/US2011/053067, filed with WIPO/USPTO September 2011.Copyright:•Kirti Chawla, and Gabriel Robins, An RFID-Based Object Localization Framework, US Copyright Case Number: 1-633487801, 2011.Startup Venture:•Co-founded Diorama Technologies LLC, in partnership with private investors•Raised venture capital funding and negotiated licensing terms•Other investors have shown strong interest in commercializing our ideas 23/23

WIPO/USPTOPatent

Object Localization with RFID Infrastructure

Backup Slides

Localization Solutions

Localization TypeLocalization Technique

Signal Strength

Signal Phase

Arrival Time

Environmental

Self

Organizing Localization SpaceBackground

Localization ChallengesApproach

Radio Interference Occlusions Tag Sensitivity

Tag Spatiality Tag Orientation

Reader Locality

Back

Tag Sensitivity – Single TagEvaluation

Constant Distance/Variable Power

Variable Distance/Constant Power

Back

Tag Sensitivity – Multi-Tag (Proximity)

Evaluation

Constant Distance/Variable Power

Variable Distance/Constant Power

Back

Tag Sensitivity – Multi-Tag (Rotation-1)

Evaluation

Constant Distance/Variable PowerBack

Tag Sensitivity – Multi-Tag (Rotation-2)

Evaluation

Variable Distance/Constant PowerBack

Error-Reducing HeuristicsApproach

Root Sum Square

Minimum Power

Selection

Absolute Difference

Localization Error

Meta Heuristic

Problem: There can be multiple nearby reference tags

Insight: Select nearby reference tags using different schemes

Back