Post on 14-Jan-2016
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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
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
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.
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]
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
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
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
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
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
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
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
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
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
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
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)
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
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
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.
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
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
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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
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
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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)
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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
September 16,2003 APIT @ MobiCom'03 University of Virginia
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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