Efficient RFID-Based Mobile Object Localization
Kirti Chawla, Gabriel Robins, and Liuyi Zhang
Department of Computer Science
University of Virginia, Charlottesville, USA
{kirti, robins, lz3m}@virginia.edu
This work is supported by U.S. National Science Foundation (NSF) grant: CNS-0716635 (PI: Professor Gabriel Robins)
For more details, visit: www.cs.virginia.edu/robins
2/26
Overview
Future
Directions
RFID
Localization
Results
Proposed
Approach
3/26
Overview
Activity Recognition
Real-time Tracking
Novel Application Scenarios
RFID
Localization
Pervasive Media
Elderly Care
4/26
Overview
RFID-based Object Localization
RFID Localization
Reader Localization
Tag Localization
Stationary Reader Localization
Tag Reader Localization
Mobile Reader Localization
Mobile Tag Localization
5/26
Overview
Localization Challenges
RF Interference
Occlusions
Tag Sensitivity
Tag Spatiality
Tag Orientation
Reader Locality
6/26
Proposed
Approach
Underlying Principle
Reader Power
Distance
Tag Power
2
Tag Power
 Frequency 
= Reader Gain  Tag Gain  

Reader Power
 4  π  Distance 
7/26
Proposed
Approach
Basic Approach
Intersection of Detectability Regions
Calibration phase
Localization phase
8/26
Proposed
Approach
Multi-Tag Calibration
Platform Design
Multi-Tag Platform
Calibration under Proximity
Calibration under Rotation
9/26
Proposed
Approach
Tag Localization Algorithms: Algorithm I
Linear Search for Tags
Start
For each Tag
Linear search for optimal tag
detection power level
NO
Current
Power Level
> Threshold ?
NO
Optimal
Power Level
FOUND ?
Time = O(# tags  power levels)
YES
YES
Report Optimal
Power Level
Stop
10/26
Proposed
Approach
Tag Localization Algorithms: Algorithm II
Binary Search for Tags
Start
For each Tag
Binary search for optimal tag
detection power level
NO
Current
Power Level
> Threshold ?
NO
Optimal
Power Level
FOUND ?
YES
YES
Time = O(# tags  log(power levels))
Report Optimal
Power Level
Stop
11/26
Proposed
Approach
Tag Localization Algorithms: Algorithm III
Parallel Search for Tags
Start
Initialize power level of all tags to maximum
Linearly decrement power level for all tags
NO
Power
level = 0 ?
NO
Power level
of a tag
“fixed” ?
YES
YES
Tag has optimal power
level
Stop
Time = O(power levels)
12/26
Proposed
Approach
Reader Localization Algorithm
Measure and Report
Start
NO
Tag Found
?
Return Tag-ID and
Timestamp
Return “Not Found”
Stop
Time = O(1)
YES
13/26
Proposed
Approach
Localization Error
Error
Reference Tag
Target Tag
14/26
Proposed
Approach
Error Reduction Heuristics: Heuristic I
Heuristics: Absolute Difference
M
H1 : Min( ΔI (R J ))
J
I=1
M
M
I=1
I=1
M
M
I=1
I=1
H2 : Min( ΔI (R J ) +  ΔI (RK ))
J,K
JK
H3 : Min( ΔI (R J ) +  ΔI (RK ))
J,K
JK
J, K are neighbors
M
M
I=1
I=1
H4 : Min( ΔI (R J ) +  ΔI (RK )) such that
J,K
JK
J, K are neighbors
M
M
 Δ (R ) <  Δ (R )
I
I=1
J
I
I=1
K
15/26
Proposed
Approach
Error Reduction Heuristics: Heuristic II
Heuristics: Minimum Power Reader Selection
H5 : Min (Δ J (T) + ΔK (T))
J,K,S,Q
JK
S Q
J, K are planar orthogonally oriented
H6 : Min (Δ J (T) + ΔK (T))
J,K,S,Q
JK
S Q
S, Q are neighbors
16/26
Proposed
Approach
Error Reduction Heuristics: Heuristic III
Heuristics: Root Sum Square Absolute Difference
M
H7 : Min(
J
 Δ (R )
J,K
JK
 Δ (R )
2
I
J
J,K
JK
)
M
+
I=1
M
H9 : Min(
J
I=1
M
H8 : Min(
2
I
 Δ (R )
2
I
I=1
J
 Δ (R )
2
I
K
)
I=1
M
+
 Δ (R )
2
I
K
)
I=1
J, K are neighbors
M
H10 : Min(
J,K
JK
 Δ (R )
2
I
I=1
J
M
+
 Δ (R )
2
I
K
M
) such that
I=1
J, K are neighbors
 Δ (R )
2
I
I=1
J
M
<
 Δ (R )
2
I
I=1
K
17/26
Proposed
Approach
Error Reduction Heuristics: Meta-Heuristic
Heuristics: Absolute Difference
Heuristics: Minimum Power Reader Selection
Localization
Error
Heuristics: Root Sum Square Absolute
Difference
Other Novel
Heuristics
Meta-Heuristic:
Overall
Minimum
18/26
Results
Experimental Setup
Track Design
Mobile Robot Design
4
X-axis
1
3
2
Y-axis
19/26
Results
Multi-Tag Calibration – Proximity Sensitivity Invariant
Constant Distance/Variable Power
Variable Distance/Constant Power
20/26
Results
Multi-Tag Calibration – Rotation Sensitivity Invariant-B
Constant Distance/Variable Power
21/26
Results
Multi-Tag Calibration – Rotation Sensitivity Invariant-A
Variable Distance/Constant Power
22/26
Results
Localization Accuracy and Speed
Localization Accuracy
Localization Speed
23/26
Results
Impact on Localization Accuracy
due to Tag Density and Power-Step Size
Accuracy vs. Tag Density
Diminishing
returns
Accuracy vs. Power-Step Size
24/26
Results
Comparative Analysis
Average Time (minutes)
Setup Phase
Localization Phase
Test area
(m2)
Chae and Han [5]
Choi and Lee [8]
Hansel et al [11]
Han et al [12]
Koch et al [14]
Milella et al [18]
Santa et al [20]
Seo and Lee [21]
Vorst et al [23]
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
Not Reported
48.36
14.4
784
1
60
70
2
5
125
0.23
0.016 – 0.024
1 – 10
0.09
0.1
0.64
0.2
0.2 – 1.6
0.2 – 0.6
Linear Search (HL)
Linear Search (LH)
Binary Search
Parallel Search
Measure and Report
Combined Approach
29.78
161.23
47.24
1.67
0
161.23
1.42
5.28
1.95
1.67
0
10.32
8
8
8
8
8
8
0.29
0.27
0.31
0.35
0.25
0.18
Technique
Localization Error
(m)
25/26
Results
Localization Visualization
Heuristics
Work Area
Accuracy
Antenna Control
26/26
Future
Directions
Open Research
Problems
Tag Spatiality Impact on
Localization Accuracy
and Speed
Simultaneous Multiple
Object Localization
Activity Recognition
Novel Applications
Questions ?
References
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