RangeFree Localization Schemes in Large Scale Sensor Networks

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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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Outline

• Problem Statement
• State of the Art
• Motivation Contribution
• A.P.I.T. Algorithm Details
• Evaluation
• Conclusion

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Problem 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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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 doesnt hold well in practice
  D. Ganesan

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State of the Art (2)

• Range-Free Coarse-grained localization
• USC/ISI Centroid localization
• Rutgers DV-Hop Localization
• MIT Amorphous Localization
• ATT Active Badge
• Simple hardware/ Less accuracy

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Motivation

• 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)
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Contributions

• 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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Overview of APIT Algorithm

• APIT employs a novel area-based approach. Anchors
  divide terrain into triangular regions
• A nodes 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).

IN
IN
Out
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APIT Main Algorithm

• Pseudo Code
• Receive beacons (Xi,Yi) from N anchors
• N anchors form triangles.
• For ( each triangle Ti ? )
• InsideSet ? Point-In-Triangle-Test (Ti)
• Position COG ( nTi ? InsideSet)

• For each node
• Anchor Beaconing
• Individual APIT Test
• Triangle Aggregation
• Center of Gravity Estim.

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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.

A(ax,ay)
M
B(bx,by)
C(cx,cy),
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Perfect 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 cant move, how to recognize direction of
  departure
• Exhaustive test on all directions is impractical

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Departure Test

• Recognize directions of departure via
  neighbor exchange
• Receiving Power Comparison ( the solution we
  adopt)
• Smoothed Hop Distance Comparison ( Nagpal 1999
  MIT)

Experimental Result from Berkeley
Experiment Result from UVA
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A.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.

APIT(A,B,C,M) IN
APIT(A,B,C,M) OUT
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APIT Aggregation

• Aggregation provides a good accuracy, even
  results by individual tests are coarse and error
  prone.

High Possibility area
Grid-Based Aggregation
With a density 10 nodes/circle, Average 92
A.P.I.T Test is correct Average 8 A.P.I.T Test
is wrong
Low possibility area
Localization Simulation example
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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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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

a
DOI 0 DOI
0.05 DOI 0.2
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Simulation 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 0.52
• 90 confidence intervals are within in 510 of
  the mean
• Metrics
• Localization Estimation Error ( normalized to
  units of radio range)
• Communication Overhead in terms of message

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Error Reduction by Increasing Anchors
AH1028,ND 8, ANR 10, DOI 0
Placement Uniform
Placement Random
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Error Reduction by Increasing Node Density
AH16, Uniform, AP 0.62, ANR 10
DOI0.1
DOI0.2
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Error Under Varying DOI
ND 8, AH16, AP 2, ANR 10
Placement Uniform
Placement Random
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Communication Overhead

• Centroid and APIT
• Long beacons
• DV-Hop and Amorphous
• Short beacons
• Assume 1 long beacon Range2 ? short beacons
  100 short beacons
• APIT gt Centroid
• Neighborhood information exchange
• DV-Hop gt Amorphous
• Online HopSize estimation

ANR10, AH 16, DOI 0.1, Uniform
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Performance Summary
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Hermes Project _at_ UVA
NEST Demo
EnviroTrack
Real-Time Routing
QoS Scheduling
Data Aggregation
Lazy Binding MAC
Sensing Coverage
APIT Localization
Mote Test Bed
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Conclusions

• 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?

• Thanks

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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

PIT IN while APIT OUT
PIT OUT while APIT IN
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Empirical 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