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![Page 1: September 16,2003 APIT @ MobiCom'03 University of Virginia 1 Range-Free Localization Schemes in Large Scale Sensor Networks Tian He Chengdu Huang Brian.](https://reader035.fdocuments.net/reader035/viewer/2022070411/56649f465503460f94c68b72/html5/thumbnails/1.jpg)
September 16,2003 APIT @ MobiCom'03 University of Virginia
1
Range-Free Localization Schemes in Large Scale Sensor Networks
Range-Free Localization Schemes in Large Scale Sensor Networks
Tian He
Chengdu Huang
Brian. M. Blum
John A. Stankovic
Tarek F. Abdelzaher
Department of Computer Science, University of Virginia
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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OutlineOutline
• Problem Statement
• State of the Art
• Motivation & Contribution
• A.P.I.T. Algorithm Details
• Evaluation
• Conclusion
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Problem StatementProblem Statement
• Localization Problem: – How nodes discover their geographic positions in 2D or 3D space?
• Target Systems:– Static large scale sensor networks or one with a low mobility
• Goal:– An affordable solution suitable for large-scale deployment with a
precision sufficient for many sensor applications.
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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State of the Art (1)State of the Art (1)
• Range-based Fine-grained localizations – TOA (Time of Arrival ): GPS– TDOA (Time Difference of Arrival): MIT Cricket &
UCLA AHLos – AOA (Angle of Arrive ): Aviation System and Rutgers
APS– RSSI (Receive Signal Strength Indicator) : Microsoft
RADAR and UW SpotOn
Required Expensive hardware
Limited working range ( Dense anchor requirement)
Log-normal model doesn’t hold well in practice [D. Ganesan]
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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State of the Art (2)State of the Art (2)
• Range-Free Coarse-grained localization– USC/ISI Centroid localization
– Rutgers DV-Hop Localization
– MIT Amorphous Localization
– AT&T Active Badge
Simple hardware/ Less accuracy
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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MotivationMotivation
• High precision in sensor network localization is overkill for a lot of applications.
• Large scale deployment require cost-effective solutions.
Routing Delivery Ratio Entity Tracking Time
Under different localization Error ( % Radio Range)
0%
20%
40%
60%
80%
100%
6 8 10 12 14 16 18 20
Node Density
No Error
0.2R
0.4R
0.6R
0.8R
1.0R
100%
110%
120%
130%
140%
5 6 7 8 9 10
Pursuer speed (units/sec)
No
rmal
ized
tra
ckin
g t
ime 0.4R
0.6R
0.8R
1.0R
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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ContributionsContributions
• A novel range-free algorithm with enhanced performance under irregular radio patterns and random node placement with a much smaller overhead than flooding based solutions
• The first to provide a realistic and detailed quantitative comparison of existing range-free algorithms.
• First investigation into the effect of localization accuracy on application performance
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Overview of APIT AlgorithmOverview of APIT Algorithm
• APIT employs a novel area-based approach. Anchors divide terrain into triangular regions
• A node’s presence inside or outside of these triangular regions allows a node to narrow the area in which it can potentially reside.
• The method to do so is called Approximate Point In Triangle Test (APIT). Out
IN
IN
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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IN
IN
APIT Main AlgorithmAPIT Main Algorithm
Pseudo Code:
Receive beacons (Xi,Yi) from N anchors
N anchors form triangles.
For ( each triangle Ti Є ){
InsideSet Point-In-Triangle-Test (Ti)
}
Position = COG ( ∩Ti InsideSet);
For each node
• Anchor Beaconing
• Individual APIT Test
• Triangle Aggregation
• Center of Gravity Estim.
3
N
3
N
OUT
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Point-In-Triangle-Test Point-In-Triangle-Test
• For three anchors with known positions: A(ax,ay), B(bx,by), C(cx,cy), determine whether a point M with an unknown
position is inside triangle ∆ABC or not.
B(bx,by)C(cx,cy),
A(ax,ay)
M
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Perfect P.I.T TheoryPerfect P.I.T Theory
• If there exists a direction in which M is departure from points A, B, and C simultaneously, then M is outside of ∆ABC. Otherwise, M is inside ∆ABC.
• Require approximation for practical use– Nodes can’t move, how to recognize direction of departure
– Exhaustive test on all directions is impractical
M
A
CB
MA
CB
Inside Case Outside Case
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Departure TestDeparture Test
Recognize directions of departure via neighbor exchange1. Receiving Power Comparison ( the solution we adopt)
2. Smoothed Hop Distance Comparison ( Nagpal 1999 MIT)
Experimental Result from Berkeley Experiment Result from UVA
M
NA
Anchor Receiving nodes
300
350
400
450
500
550
600
1 5 9 13 17 21 25 29 33 37Beacon Sequence Number
Sign
al S
tren
gth
(mv) 1 Foot
5 Feet
10 Feet
15 Feet
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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A.P.I.T. TestA.P.I.T. Test
Approximation: Test only directions towards neighbors – Error in individual test exists , however is relatively small and can be masked
by APIT aggregation.
A
C
1
23
4
M
B
A
CB
A. Inside Case B. OutSide Case
1
23
4
M
APIT(A,B,C,M) = IN APIT(A,B,C,M) = OUT
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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APIT AggregationAPIT Aggregation
• Aggregation provides a good accuracy, even results by individual tests are coarse and error prone.
With a density 10 nodes/circle, Average 92% A.P.I.T Test is correctAverage 8% A.P.I.T Test is wrong
Localization Simulation example
Grid-Based Aggregation
High Possibility area
Low possibility area
-1-1-10011100
-1-1-10122200
0-1-10112211
00-10112210
0001111100
0001110100
0001000000
-1-1-10000
-1-10100
0-1011
00-1010
00011100
00011000
0001100000
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Evaluation (1) Evaluation (1) • Comparison with state-of-the art solutions
– USC/ISI Centroid localization by N.Bulusu and J. Heidemann 2000
– Rutgers DV-Hop Localization by D.Niculescu and B. Nath 2003
– MIT Amorphous Localization by R. Nagpal 2003
Centroid DV-Hop (online)/ Amorphous (offline)
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Evaluation (2)Evaluation (2)
• Radio Model: Continuous Radio Variation Model.– Degree of Irregularity (DOI ) is defined as maximum radio range variation
per unit degree change in the direction of radio propagation
DOI =0 DOI = 0.05 DOI = 0.2
α
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Simulation SetupSimulation Setup
• Setup – 1000 by 1000m area
– 2000 ~ 4000 nodes ( random or uniform placement )
– 10 to 30 anchors ( random or uniform placement )
– Node density: 6 ~ 20 node/ radio range
– Anchor percentage 0.5~2%
– 90% confidence intervals are within in 5~10% of the mean
• Metrics– Localization Estimation Error ( normalized to units of radio
range)
– Communication Overhead in terms of #message
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Error Reduction by Increasing #AnchorsError Reduction by Increasing #Anchors
AH=10~28,ND = 8, ANR = 10, DOI = 0
Placement = Uniform Placement = Random
0
0.5
1
1.5
2
2.5
10 14 18 22 26
Anchor Heard
Centroid AmorphousDV-Hop A.P.I.T
P.I.T.
0
0.5
1
1.5
2
2.5
10 14 18 22 26
Anchor Heard
Centroid Amorphous
DV-Hop A.P.I.TP.I.T.
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
6 10 14 18 22
Node Density
Centroid Amorphous
DV-Hop A.P.I.T
Error Reduction by Increasing Node DensityError Reduction by Increasing Node Density
AH=16, Uniform, AP = 0.6%~2%, ANR = 10
DOI=0.1 DOI=0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
6 10 14 18 22
Node Density
Centroid Amorphous
DV-Hop A.P.I.T
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Error Under Varying DOIError Under Varying DOI
ND = 8, AH=16, AP = 2%, ANR = 10
Placement = Uniform Placement = Random
0
0.5
1
1.5
2
2.5
3
3.5
0 0.1 0.2 0.3 0.4 0.5 0.6
Degree of irregularity
Centroid
Amorphous
DV-Hop
A.P.I.T
0
0.5
1
1.5
2
2.5
3
3.5
0 0.1 0.2 0.3 0.4 0.5 0.6
Degree of irregularity
Centroid
Amorphous
DV-Hop
A.P.I.T
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Communication OverheadCommunication Overhead
• Centroid and APIT– Long beacons
• DV-Hop and Amorphous– Short beacons
• Assume: 1 long beacon = Range2 short beacons = 100 short beacons
• APIT > Centroid– Neighborhood information
exchange
• DV-Hop > Amorphous– Online HopSize estimation ANR=10, AH = 16, DOI = 0.1, Uniform
0
5000
10000
15000
20000
25000
30000
6 11 15 18 22
Node Density
Centroid
AmorphousDV-Hop
A.P.I.T
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September 16,2003 APIT @ MobiCom'03 University of Virginia
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Performance SummaryPerformance Summary
Centroid DV-Hop Amorphous APITAccuracy Fair Good Good Good
Node Density >0 >8 >8 >6
Anchor >10 >8 >8 >10
ANR >0 >0 >0 >3
DOI Good Good Fair Good
GPSError Good Good Fair Good
Overhead Smallest Largest Large Small
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Hermes Project @ UVAHermes Project @ UVA
Hermes Network Architecture
Application
Embedded Device
Environmental Monitor Military Suvilliance
Emergency Response
Biomedicine
Smart spaceLearning environment
Entity-Aware Transport
Local-Addressed Soft Real-time Routing
Robust and Self-stabilized MAC
Localization
Application Independent Data Aggregation
Service API
Differentiated Packet Scheduling
Power and Coverage management
NEST Demo
EnviroTrack
Real-Time Routing
QoS Scheduling
Data Aggregation
Lazy Binding MAC
Sensing Coverage
APIT Localization
Mote Test Bed
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ConclusionsConclusions
• Range-free schemes are cost-effective solutions for large scale sensor networks.
• Through a robust aggregation, APIT performs best with irregular radio patterns and random node placements
• APIT performs well with a low communication overhead( e.g. 2500 instead of 25,000 msgs)
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Questions?Questions?
Thanks
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Error Case Error Case
Since the number of neighbors is limited, an exhaustive test on every direction is impossible.
– InToOut Error can happen when M is near the edge of the triangle
– OutToIn Error can happen with irregular placement of neighbors
1
2
4
M
A
C
1
2
4
B
A
CB
A. InToOut Error B. OutToIn Error
3
M
PIT = IN while APIT = OUT PIT = OUT while APIT = IN
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Empirical Study on APIT ApproximationEmpirical Study on APIT Approximation
• Percentage of error due to APIT approximation is relatively small (e.g. 14% in the worst case, 8% when density is 10)
• More important, Errors can be masked by APIT aggregation.
APIT Error under Varying Node Densities
0%
2%
4%
6%
8%
10%
12%
14%
16%
6 8 10 12 14 16 18 20 22 24Node Density Per Radio Range
OutToInErrorPercentage
InToOutErrorPercentage