A Robust High-Performance
RFID-Based Location System
Kirti Chawla
Department of Computer Science
University of Virginia
Introduction
Location, Location, Location
Locate
Objects
Environments
Goal: Locate objects in an environment
Attributes:
-Reliable
-Accurate and Fast
1/30
Background
RFID Primer
RFID Reader
RFID Tag
Near-field
Communication
Far-field Communication
Tags and Readers:
- Form Factors
- Operating Frequency
- Power Source
2/30
Contributions
Intellectual
Adaptability
 Resilient to environmental conditions / noise
Accommodates numerous scenarios
 Tag orientation and vendor hardware –agnostic
Reliability
 Signal strength as a reliable metric
 Tag sensitivity influences performance
 Tag selection & sorting ensures uniformity
 Heuristics enhance accuracy
Scalability
 Tag selection optimizes range & cost
 Improved performance by matching tags to readers
 Reference tags are unnecessary
3/30
Background
Current State of the Art
Mismatched
Solutions
Technologies
Techniques
Limiting
Constraints
4/30
Motivation
Pros/Cons
Cons
Pros
No Line of
Sight
Solid
Obstacles
Dark
Environment
Cost
Effective
Adaptive
Unintended
Use
Invasive
Susceptible
Entry
Barrier
Targeted
5/30
Motivation
Retail
Warehouse-Store
Backend
Save Time
Minimize Misuse
Frontend
Stimulate Spending
Improve Turnaround
6/30
Motivation
Backend: Save Time
100, 000 Ft2
4000 Stores
Floor space and Nos.
100 People
$ 12/Hour
275 Days/Year
Workforce Cost
Warehouse-Store
30 Min./Day
Avg. Search Time
Potential New Savings = $ 600 Million / Year
7/30
Motivation
Frontend: Stimulate Spending
100, 000 Ft2
4000 Stores
Floor space and Nos.
$ 319B /Year
$ 79M /Store/Year
$ 218K /Store/Day
Revenue Generation
Warehouse-Store
$ 72 /Day/Person
$ +1 /Day/Person
$ 323B /Year
Improve Spending
Potential New Revenue = $ 4.3 Billion / Year
8/30
Motivation
Hospitals
Other Use-Cases
Locate:
- Medical Supplies
- Surgical Instruments
- Caregivers
- Patients
Locate:
- Guests / Travelers
- Freight
- Baggage
Airports
9/30
Research
Localization Framework
Collection of Tags
Tag Selection
Candidate Tags
Tag Binning
Uniformly Sensitive Tags
Empirical Power-Distance Relationship
Tags’ Location Estimates
Performance-Enhancing Heuristics
Improved Location Estimates
10/30
Research
Tag Selection
Results
Read Range
RSS
Read Count
Tag Collection
Tag Selection
Candidate Tags
Problem: Tags have variable performance
Solution: Select tags based on their performance
11/30
Research
Tag Binning
Results
RSS
Read Count
Same Type
Tags Collection
Tag Binning
Uniformly
Sensitive Tags
Problem: Tags have variable sensitivities
Solution: Bin tags based on their sensitivity
12/30
Research
Power-Distance Relationship
Comparison
N
PR  1 

Transmitted Power: PT
RFID Reader
PT


D 
Friis Transmission Equation
Tag-Reader Distance: D
Received Power: PR
RFID Tag
Problem: RF signal variability renders Friis Eq. useless
Solution: Utilize empirical power-distance relationship
13/30
Research
Power-Distance Relationship
TX-Side
Algorithms
RX-Side
Read
Count
Models
Uniformly
Sensitive Tags
Empirical
Power-Distance
Relationship
Tags’ Location
Estimates
Problem: Locate objects using empirical power-distance
relationship
Solution: Utilize TX and RX empirical power-distance
relationship
14/30
Research
TX-Side Algorithms
Locate Tags: Power-Modulating Algorithms
Radio Wave
Shared Region
Antenna
Insight: Similarly behaving tags are neighbors
15/30
Research
TX-Side Algorithms
Results
Algorithms
Locate Tags: Power-Modulating Algorithms
Problem: Locate tags using TX RF signal power
Solution: Algorithmically modulate TX RF signal power
16/30
Research
RX-Side Models
RFID Reader - A
RFID Tag - A
RFID Tag - B
RFID Reader - B
Insight: Match tags to readers for higher performance17/30
Research
RX-Side Models
Locate Tags: RSS Decay Models
PR  1 
 
PT  D 
N
Friis Physics Model
Results
RSS  D N
RSS Decay Model
Radial Orientation
Axial Orientation
Problem: Locate tags using RX RF signal power
Solution: Adapt theoretical physics model to reality
18/30
Research
Heuristics
Localization Error
Heuristics
Target Tag
Reference Tag
Problem: Assumption that target and reference tag
location coincide leads to localization error
Solution: Consider neighbor reference tags that
minimize localization error
19/30
Evaluation
Experimental Setup
Tablet
Internet
RFID Reader
Backend Host
Antenna
Reference Tag
Mobile Robot with
onboard reader
and multi-tag
20/30
Evaluation
Back
Tag Selection
Insight: Select tags on multi-objective criteria
Average RSS (in Thousands)
40
19.6 dBm, 0.61 m
19.6 dBm, 3.05 m
25.6 dBm, 1.83 m
31.6 dBm, 0.61 m
31.6 dBm, 3.05 m
35
30
25
19.6 dBm, 1.83 m
25.6 dBm, 0.61 m
25.6 dBm, 3.05 m
31.6 dBm, 1.83 m
20
15
10
5
0
4
10
14
Tag ID
18
22
21/30
Evaluation
Back
250
Tag Binning
Insight: Sort tags on their RF performance
19.6 dBm, 0.61 m
19.6 dBm, 3.05 m
25.6 dBm, 1.83 m
31.6 dBm, 0.61 m
31.6 dBm, 3.05 m
Number of Tags
200
150
19.6 dBm, 1.83 m
25.6 dBm, 0.61 m
25.6 dBm, 3.05 m
31.6 dBm, 1.83 m
100
50
0
200
1000 1800 2600 3400 4200 5000 5800 6600
22/30
Average RSS
Evaluation
Back
Reader Output Power (dBm)
100
Power-Distance Relationship
Insight: Empirical power-distance relationship
enables higher performance
Ideal Friis (N = 6)
80
60
Ideal Friis (N = 3)
40
Empirical
20
Ideal Friis (N = 2)
0
0.15
0.49
0.84 1.12 1.37 1.57 1.70
Distance from Antenna (meters)
1.91
23/30
Evaluation
RSS Decay Models
Average RSS (in Thousands)
Insight: Orientation-based decay models lead
Back
to orientation-agnostic localization
Radial
0-10000
10000-20000
20000-30000
30
20
10
0
0.13 0.89
1.65 2.41
3.18
0 Degrees
15 Degrees
30 Degrees
45 Degrees
60 Degrees
75 Degrees
90 Degrees
270 Degrees
24/30
Evaluation
TX-Side Localization Accuracy
Insight: Performance can be improved by
Back
denser reference tag deployment
Average Distance (meters)
3
2.5
Time
Actual Location
Measured Location
2
1.5
1
0.5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
25/30
Measurement Point
Evaluation
Density Vs Performance
Insight: Localization performance varies with
reference tag density
Localization Error (meters)
1.6
1.2
0.8
0.4
0
1
4
7
10 13 16 19 22 25
Number of Reference Tags
28
31
26/30
Evaluation
RX-Side Localization Accuracy
Insight: Performance can be improved by
Back
minimizing RF dead-zones
Average Distance (meters)
8
6
Actual Location
Measured, Alien, Tag-14 (ID: 47)
4
2
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
27/30
Measurement Point
Evaluation
Comparative Evaluation
Localization
Time
Test
Region
(m2/m3)
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
Active
TX-Side:
Combined Algorithms
1.67 minutes
2D, 8
0.18
Passive
~4 seconds
2D, 8
3D, 16
0.30- 0.60
Optional, Passive
Approach
RX-Side:
Combined Models
Conclusion
Summary and Future Work
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:
- Scalability
- Combination of approaches
- Visualization tools
- Field testing and commercialization
29/30
Contributions
Deliverables
• Co-directed 10 undergraduate theses and Capstone projects
• Won the 2011 SEAS Entrepreneurial Concept Competition
• Placed 2nd at the 2012 Darden Business Competition
Journal Publications:
• Kirti Chawla, Christopher McFarland, Gabriel Robins, and Wil Thomason,
A Robust Real-Time RFID-Based Location System, 2013, In preparation
• 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, Best Presentation Award
• 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, 2013
• 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
30/30
Backup Slides
Motivation
Backend: Minimize Misuse
Back
100, 000 Ft2
4000 Stores
Floor space and Nos.
Warehouse-Store
1 Million Items
5 % Misuse Rate
$ 1 / Item
Reported Misuse
Potential New Savings = $ 200 Million / Year
Motivation
Frontend: Improve Turnaround
Back
100, 000 Ft2
4000 Stores
Floor space and Nos.
$ 319B Rev/Year
$ 79M /Store/Year
$ 218K /Store/Day
Revenue Generation
Warehouse-Store
$ 72 /Day/Person
3K /Store/Day
+5 /Store/Day
Maximize Utility
Potential New Revenue = $ 500 Million / Year
Motivation
How Our Research Can Affect Your Bottom Line
$ 200 Million / Year
Minimize Misuse
$ 500 Million / Year
Improve Turnaround
$ 600 Million / Year
Save Time
$ 4.3 Billion / Year
Stimulate Spending
Approach
Localization Challenges
Radio Interference
Occlusions
Tag Sensitivity
Tag Spatiality
Tag Orientation
Reader Locality
Approach
Reliability through Multi-Tags
Vertical
Parallel
Platform
RFID Reader
Side View
Horizontal
Orthogonal
Platform
RFIDTop
TagView
Problem: Optimal tag reads occur at certain
orientations
Solution: Multi-Tags provide orientation redundancy
Approach
Power-Modulating Algorithms
Back
0
MID
MAX
Reader Output Power Range
Linear Search
Binary Search
Parallel Search
O(#Tags 
#Power-Levels)
O(#Tags 
Log#Power-Levels)
O(#Power-Levels)
Approach
Heuristics Framework
Back
Absolute
Difference
Localization
Error
Minimum
Power
Selection
Meta
Heuristic
Root Sum
Square
Problem: There can be multiple neighbor reference tags
Solution: Select neighbor reference tags using different
selection criteria
Evaluation
Average RSS (in Thousands)
Back
RSS Decay Models
Insight: Orientation-based decay models lead
to orientation-agnostic localization
0-10000
10000-20000
20000-30000
30
20
0 Degrees
30 Degrees
60 Degrees
90 Degrees
270 Degrees
10
0
0.13
0.89
1.65
2.41
3.18
Evaluation
Back
25
TX-Side Localization Time
Insight: Faster algorithms provide lower tag
detectability
Linear Search (HL)
Binary Search
Time (Seconds)
20
Linear Search (LH)
Parallel Search
15
10
5
0
0.10
0.71
1.32
1.93
2.54
Distance from Antenna (Meters)
3.15
Product
Technology Cost Breakup (Post R&D)
100, 000 Ft2
4000 Stores
Old Revenue = 79M / Store / Year
Floor space and Nos.
$ 20K (300 Ant.)
$ 20K (80 Readers)
$ 10K (1M Tags)
RFID Hardware Cost
Warehouse-Store
Variable (Software)
$ 50K (Backend)
New Revenue = 81M / Store / Year
Software and Misc. Cost
Total Cost 1st Year = $ 100K + SLC* + AMC+ / Store
Total Cost Nth Year = SLC + AMC / Store; N ≥ 2
* Software License Cost, + Annual Maintenance Cost | All costs are current estimates
Research
TX-Side Algorithms
Locate Readers: Proximity-Sensing Algorithm
Problem: Locate readers using TX RF signal power
Solution: Sense proximity of neighbor tags
Patents
USPTO and WIPO
Object Localization with RFID Infrastructure
4/30