1 the effects of ranging noise on multihop localization: an empirical study kamin whitehouse joint...
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1
The Effects of Ranging Noise on Multihop Localization:
An Empirical Study
Kamin WhitehouseJoint With: Chris Karlof, Alec Woo, Fred Jiang, David Culler
IPSN ‘054/24/05
2
Introduction
Ranging Localization
Single-hop Multi-hop
3
Introduction
Ranging Localization
Single-hop Multi-hop
“Noisy Disk”
4
Introduction
Ranging Localization
Single-hop Multi-hop
“Noisy Disk” Unit Disk Connectivity Guassian Noise
Design and comparison Optimal solutions Cramer-rao bounds Algorithmic proofs Empirical parameters
Prediction gap Difference between
predicted and observed error
dmax
σ
5
Introduction
Loca
lizat
ion
Err
or
EmpiricalDeployment
NoisyDisk
PredictionGap
6
Methodology
Loca
lizat
ion
Err
or
EmpiricalDeployment
NoisyDisk
Model B Model C
Significant
Dominant
Sufficient
7
Outline
Deployment Setup Simulation Methodology Comparisons and Analysis
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Ultrasound Hardware
Circuitry derived from the Medusa node
Cricket’s RF envelope Millibots reflective cone
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Radio (RSS)
Chipcon CC1000 similar fidelity to WiFi In our experiments, 2m
std error near 20m range RFIDeas: 2m std error near
2m range RFM DR3000 and TR1000:
6m std error near 6m range
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DV-distance Algorithm
True distance to anchor is approximated by shortest-path distance
Representative of large class using shortest path or bounding box Zig-zag makes paths
longer Noise makes paths
shorter
[16]
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Ultrasound Deployment
49 nodes on a paved surface
13x13m area 4 anchor nodes Randomized grid
topology Distributed
implementation 7 executions Median
localization error of 0.78m
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Signal Strength Deployments
49 and 25 node topologies in a grassy field
50x50m area Median localization
error ~4.3 and 13.4m Comparable to GPS
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Outline
Deployment Setup Simulation Methodology Comparisons and Analysis
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Traditional Simulation
Ranging estimates are generated using parametric functions
Noisy Disk
Parameters σ and dmax must be estimated from data
[16]
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Parameter Estimation
[14]
Maximum Range: dmax
Error: σ
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Statistical Sampling For each in
simulation, randomly choose
Data set includes ranging failures
Can be divided into two components Sampled Noise Sampled Connectivity
±
RangingFailures
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Data Collection
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Data Collection
Traditional Data Collection Low spatial resolution
Single pair of nodes at a single orientation
Single path through space
Our Data Collection For each , ~400 empirical
readings taken within 0.05m Represents wide range of
node, antenna, and orientation variability
Captures variability due to dips, bumps, rocks, etc
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Outline
Deployment Setup Simulation Methodology Results and Analysis
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Experimental Setup
2 Connectivity and noise components Unit Disk connectivity (D) Gaussian noise (G) Sampled connectivity (S) Sampled noise (S)
Hybrid Simulations (C/N)
Unit Disk
Sampled Conn
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
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Experimental Setup
Unit Disk
Sampled Conn
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
Loca
lizat
ion
Err
or
D/N
S/N
D/G D/S
S/SS/G
Deployment
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49 Node RSS Experiment
Unit Disk
Sampled Connectivity
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
D/N D/G D/S S/N S/SS/G Deployment
D/N
S/N
D/N S/N
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49 Node Ultrasound Experiment
Unit Disk
Sampled Connectivity
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
D/N D/G D/S S/N S/SS/G Deployment
D/N D/G D/S
D/N D/G D/S
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Non-disk like Connectivity
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Non-disk like Connectivity
Less constraints on location
Reduced connectivity can cause more “zig-zag” in the shortest paths This increases
shortest-path distance
[16]
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49 Node Ultrasound Experiment
Unit Disk
Sampled Connectivity
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
D/N D/G D/S S/N S/SS/G Deployment
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Non-Gaussian Noise
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Non-Gaussian Noise
The shortest-path algorithm selectively chooses underestimated distances
Heavy-tailed noise can decrease shortest path distance
[16]
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25 Node RSS Experiment
Unit Disk
Sampled Connectivity
No Noise Gaussian Noise Sampled Noise
D/N
S/N
D/G D/S
S/SS/G
D/N D/G D/S S/N S/SS/G Deployment
Significant
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Conclusions
Non-disk like connectivity Non-Gaussian noise
Methodology A deployment is required to evaluate predictive
ability of a model