An RFID-Based Object Localization Framework and System

Kirti ChawlaDepartment of Computer Science

University of Virginia

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Goal: Locate objects in an environmentAttributes: -Reliable-Accurate (sub-meter) and Fast (seconds)

Location, Location, LocationIntroduction

Locate

Objects Environments

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RFID Tag

Near-field CommunicationFar-field Communication

Tags and Readers: - Form Factors - Operating Frequency- Power Source

RFID PrimerBackground

RFID Reader

IntellectualContributions

Resilient to environmental conditions / noise

Accommodates numerous scenarios

Tag orientation and vendor hardware –agnostic

Adaptability

Signal strength as a reliable metric

Tag sensitivity influences performance

Tag selection & sorting ensures uniformity

Heuristics enhance accuracy

Reliability

Tag selection optimizes range & cost

Improved performance by matching tags to readers

Reference tags are unnecessary

Scalability

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Technologies

Mismatched Solutions

Limiting Constraints

Techniques

Current State of the ArtBackground

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Pros/ConsMotivation

Dark Environmen

t

No Line of Sight

Cost Effective

Solid Obstacles

Adaptive

Susceptible

Invasive

Entry Barrier

Targeted

Unintended Use

Use-Case: WarehouseMotivation

Warehouse-Store30 Min./Day

Avg. Search Time

100, 000 Ft2

4000 Stores

Floor space and Nos.

100 People$ 12/Hour275 Days/Year

Workforce Cost

Potential New Savings = $ 600 Million / Year

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Other Use-CasesMotivation

Hospitals

Airports

Locate:- Guests / Travelers- Freight- Baggage

Locate:- Medical Supplies- Surgical Instruments- Caregivers- Patients

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Thesis StatementResearch

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Reliable High-Performance

RFID-based Object Localization

Framework and System

Performance Enhancing Heuristics

Empirical Power-Distance

Relationships

Uniformly Sensitive Tags

Localization FrameworkResearch

Tag Selection

Tag Binning

Empirical Power-Distance Relationship

Performance-Enhancing Heuristics

Collection of Tags

Improved Location Estimates

Candidate Tags

Uniformly Sensitive Tags

Tags’ Location Estimates

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Experimental SetupEvaluation

Reference Tag

Antenna

Mobile Robot with onboard reader

and multi-tag

RFID Reader

Backend Host

TabletInternet

Tag SelectionResearch

Problem: Tags have variable performance

Solution: Select tags based on their performance

Read Range

RSS

Read Count

Tag Selection

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Tag Collection Candidate Tags

Key Contribution: Tag Selection Process

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Tag SelectionEvaluation

Insight: Select tags on multi-objective criteria

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Tag BinningResearch

Problem: Tags have variable sensitivities

Solution: Bin tags based on their sensitivity

RSS

Read Count

Tag BinningSame Type

Tags CollectionUniformly

Sensitive Tags

Key Contribution: Tag Binning Process

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Tag BinningEvaluation

Insight: Sort tags on their RF performance

0.61 meters

1.83 meters3.05 meters

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Tag BinningEvaluation

0.61 meters1.83 meters

3.05 meters

Yield: ~70 % (350 out of 500)Tags are uniformly sensitive

Power-Distance RelationshipResearch

Problem: RF signal variability renders Friis Eq. useless

Solution: Utilize empirical power-distance relationship

RFID Tag

Transmitted Power: PT

Received Power: PR

RFID ReaderTag-Reader Distance: D

1

N

R

T

PP D

Friis Transmission Equation

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Power-Distance RelationshipEvaluationInsight: Empirical power-distance relationship

enables higher performance

Ideal Friis (N = 2)

Ideal Friis (N = 3)

Ideal Friis (N = 6)

Empirical

Environment Dependent

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Power-Distance RelationshipResearch

Problem: Locate objects using empirical power-distance relationship

Solution: Utilize TX and RX empirical power-distance relationship

Read Count

Empirical Power-Distance

Relationship

TX-Side Algorithms

RX-Side Models

Tags’ Location Estimates

Uniformly Sensitive Tags

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TX-Side AlgorithmsResearch

Insight: Similarly behaving tags are neighbors

Radio Wave

Shared Region

Locate Tags: Power-Modulating Algorithms

Antenna

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TX-Side AlgorithmsResearch

Locate Tags: Power-Modulating Algorithms

Problem: Locate tags using TX RF signal power

Solution: Algorithmically modulate TX RF signal power

Algorithms

Key Contributions: TX-Side Power-Modulating Algorithms

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TX-Side Localization AccuracyEvaluationInsight: Performance can be improved by

denser reference tag deployment

Time

Overall Accuracy: 0.18 meters

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Density Vs PerformanceEvaluation

Insight: Localization performance varies with reference tag density

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RX-Side ModelsResearch

Insight: Match tags to readers for higher performance

RFID Tag - A

RFID Reader - A

RFID Reader - B

RFID Tag - B

Key Contributions: Tag-Reader Matched Pairs

Axial Orientation

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RX-Side ModelsResearch

Locate Tags: RSS Decay Models

Problem: Locate tags using RX RF signal powerSolution: Adapt theoretical physics model to reality

Radial Orientation

NRSS DRSS Decay Model

1

N

R

T

PP D

Friis Physics Model Key Contributions: Tag Orientation Inclusive

RSS Decay Models

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RSS Decay ModelsEvaluationInsight: Orientation-based decay models lead

to orientation-agnostic localization Radial

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RX-Side Localization AccuracyEvaluationInsight: Performance can be improved by

minimizing RF dead-zones

Overall Accuracy: 0.15-0.70 meters

Scalability: No. of ObjectsEvaluation

Insight: No. of objects -invariant localization accuracy feasible

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HeuristicsResearch

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

error

Solution: Consider neighbor reference tags that minimize localization error

Localization Error

Reference Tag

Target Tag

Heuristics

Key Contributions: Heuristics for Improving

Localization Accuracy

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Experimental SetupEvaluation

RFID Reader

Backend Host

TabletInternet

Target Tag

Antenna

Scalability: EnvironmentEvaluation

Insight: Scale-invariant localization accuracyfeasible

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Overall Accuracy: 0.32 meters

Approach Localization Time

Test Region (m2)

Localization Accuracy (m)

Reference Tags

Ni et al., 2003 Not Reported 2D, 20 2 Active

Bekkali et al., 2007 Not Reported 2D, 9 0.5 – 1.0 Passive

Zhao et al., 2007 Not Reported 2D, 20 0.14 – 0.29 Passive

Choi and Lee, 2009 Not Reported 2D, 14 0.21 Passive

Choi et al., 2009 Not Reported 2D, 3 0.2 – 0.3 Passive

Zhang et al., 2010 Not Reported 2D, 36 0.45 Active

Brchan et al., 2012 A few seconds 2D, 22 1-2 Active

TX-Side: Combined Algorithms

1.67 minutes 2D, 8 0.18 Passive

RX-Side: Combined Models (without ref. tags)

~4 seconds2D, 8

0.22- 0.70 Not Applicable

RX-Side: Combined Models (with ref. tags)

Variable 2D, 80.15- 0.41 Passive

Comparative EvaluationEvaluation

Key Results: Localization Accuracy (Sub-meter)Localization Time (A few seconds)

Reference Tags (Optional)

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Summary and Future WorkConclusion

RFID-Based Location System:- Pure RFID reliably locates objects- Match tags to readers- Tag selection & binning improves tag performance- TX/RX empirical power-distance relationship- Algorithms, models, and heuristics for object localization- Identify / mitigate key localization challenges

Future Research Directions:- 3D Visualization tools- Field testing and commercialization

• Co-directed 10 undergraduate theses and Capstone projects• Won the 2011 SEAS Entrepreneurial Concept Competition• Placed 2nd at the 2012 Darden Business Competition• Best Presentation Award at 2013 IEEE Conference on Localization

Journal Publications: • Kirti Chawla, Christopher McFarland, Gabriel Robins, and Wil Thomason,

An Accurate Real-Time RFID-Based Location System, 2014, In Submission• 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, Christopher McFarland, Gabriel Robins, and Connor Shope, Real-Time RFID Localization

using RSS, IEEE International Conference on Localization and Global Navigation Satellite System, 2013, Italy, pp. 1-6

• Kirti Chawla, Gabriel Robins, and Liuyi Zhang, Efficient RFID-Based Mobile Object Localization, 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, IEEE International Symposium on Wireless Pervasive Computing, 2010, Italy, pp. 301-306

Patents:• Kirti Chawla and Gabriel Robins, System and Method For Real-Time RFID Localization, 2014• Kirti Chawla and Gabriel Robins, Real-Time RFID Localization Using Received Signal Strength (RSS)

System and Related Method, US Patent: 61/839,617, 2013• Kirti Chawla & Gabriel Robins, Object Localization with RFID Infrastructure,

WIPO Patent: 2012047559 A3, 2012; US Patent: 20130181869 A1, 2013

DeliverablesContributions

Object Localization with RFID Infrastructure

USPTO and WIPOPatents

Backup Slides

Backend: Minimize MisuseMotivation

Warehouse-Store

100, 000 Ft2

4000 Stores

Floor space and Nos.

Potential New Savings = $ 200 Million / Year

1 Million Items5 % Misuse Rate$ 1 / Item

Reported Misuse

Back

Frontend: Improve TurnaroundMotivation

Warehouse-Store $ 72 /Day/Person3K /Store/Day+5 /Store/Day

Maximize Utility

100, 000 Ft2

4000 Stores

Floor space and Nos.

Potential New Revenue = $ 500 Million / Year

$ 319B Rev/Year$ 79M /Store/Year$ 218K /Store/Day

Revenue Generation

Back

How Our Research Can Affect Your Bottom LineMotivation

$ 600 Million / Year

$ 200 Million / Year

$ 500 Million / Year

Stimulate Spending

$ 4.3 Billion / Year

Save Time

Improve Turnaround

Minimize Misuse

Localization ChallengesApproach

Radio Interference Occlusions Tag Sensitivity

Tag Spatiality Tag Orientation

Reader Locality

RFID Reader RFID Tag

Vertical Horizontal

Reliability through Multi-TagsApproach

Platform Side View

Parallel Orthogonal

RFID TagPlatform Top View

Problem: Optimal tag reads occur at certain orientations

Solution: Multi-Tags provide orientation redundancy

Power-Modulating 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

Back

Heuristics FrameworkApproach

Root Sum Square

Minimum Power

Selection

Absolute Difference

Localization Error

Meta Heuristic

Back

Problem: There can be multiple neighbor reference tags

Solution: Select neighbor reference tags using different selection criteria

RSS Decay ModelsEvaluationBack Insight: Orientation-based decay models lead

to orientation-agnostic localization

TX-Side Localization TimeEvaluationBack Insight: Faster algorithms provide lower tag

detectability

Technology Cost Breakup (Post R&D)Product

Warehouse-Store Variable (Software)$ 50K (Backend)

Software and Misc. Cost

100, 000 Ft2

4000 Stores

Floor space and Nos.

$ 20K (300 Ant.)$ 20K (80 Readers)$ 10K (1M Tags)

RFID Hardware Cost

* Software License Cost, + Annual Maintenance Cost | All costs are current estimates

Old Revenue = 79M / Store / Year

New Revenue = 81M / Store / Year

TX-Side AlgorithmsResearch

Locate Readers: Proximity-Sensing Algorithm

Problem: Locate readers using TX RF signal power

Solution: Sense proximity of neighbor tags

Ambient Noise: FrequencyEvaluation

RFID Lab (without) RFID Lab (with)

D M Lab Walmart