distributed multi-scale data processing for sensor networks
DESCRIPTION
Distributed Multi-Scale Data Processing for Sensor Networks. Raymond S. Wagner Ph.D. Thesis Defense April 9, 2007. Collaborators. Marco Duarte. J. Ryan Stinnett. V é ronique Delouille. T.S. Eugene Ng. David B. Johnson. Albert Cohen. Shu Du. Richard Baraniuk. Shriram Sarvotham. - PowerPoint PPT PresentationTRANSCRIPT
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Distributed Multi-Scale Data Processing for Sensor Networks
Raymond S. Wagner
Ph.D. Thesis DefenseApril 9, 2007
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Collaborators
Marco Duarte
Véronique Delouille
Richard Baraniuk
David B. Johnson
J. Ryan Stinnett
T.S. Eugene Ng
Albert Cohen
Shu Du
Shriram Sarvotham
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Collections of small battery-powereddevices, called sensor nodes, that can:
Sensor Network Overview
• sense data
• process data
• share data
Nodes form ad-hoc networks to exchange data:
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PROBLEM: centralized collection very costly (power, bandwidth), especially near sink.
network bottleneck
region
Data Collection Problem
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SOLUTION: nodes locally exchange data with neighbors, finding answers to questions in-network.
Distributed Processing Solution
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Distributed Data Representations
Little / No Collaboration
Wide Meas.Field Support
Quick Decode
Dist. Source Coding
Dist. CompressedSensing
Dist. Multi-Scale Analysis
Dist. Regression
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Novel Contributions
• development of multi- scale transform
• survey of application communication requirements
• analysis of numerical stability
• development of protocols
• analysis of energy cost
• development of API
new algorithms support for algorithms
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Multi-scale Wavelet AnalysisUnconditional basis for wide range of signal classes – good choice for sparse representation when little known about signal.
WT
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Project onto to find scaling coefficient , or find from previous-scale SCs as
Fix V0 with scaling function basis set , with
Multi-Resolution Analysis (MRA)Vj+1
Vj
Vj-1
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Define difference spaces Wj s.t.
Wavelet Space Analysis
Vj+1
Vj
Vj-1Wj-1Wj
Give W0 wavelet function basis set
Project onto to find wavelet coefficient or find from previous-scale SC’s as
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MRA assumes regular sample point spacing, power-of-two sample size. Not likely in sensor networks.
Wavelet Analysis for Sensor Networks
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Wavelet Lifting In-place formulation of WT – distributable, tolerates
irregular sampling grids [Sweldens, 1995]
Starts with all nodes ( ) , scalar meas. ( )
At each scale j={J-1,…,j0} , transform into:
wavelet coefficients scaling coefficients
Iterate on SCs to j=j0 so that meas. replaced by:
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Lifting Stages
Each transform scale decomposes into three stages: split, predict, update…
split P
+
_U
split P
+
_U
…
…
SPLIT into ,
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Lifting Stages
Each transform scale decomposes into three stages: split, predict, update…
split P
+
_U
split P
+
_U
…
…
PREDICT wavelet coeffs.
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Lifting Stages
Each transform scale decomposes into three stages: split, predict, update…
split P
+
_U
split P
+
_U
…
…
UPDATE scaling coeffs.
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Lifting Stages
Each transform scale decomposes into three stages: split, predict, update…
split P
+
_U
split P
+
_U
…
…
SPLIT into ,
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Lifting Stages
Each transform scale decomposes into three stages: split, predict, update…
split P
+
_U
split P
+
_U
…
…
GOAL: design split,P,U to distributed easily, tolerate grid irregularity, provide sparse representation
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Split Design
Scale j+1 Scale j Scale j-1
Goal: mimic regular grid split, s.t. ,
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Split Design
Scale j+1 Scale j Scale j-1
Goal: mimic regular grid split, s.t. ,
1. Pick a , put it in ( )
2. Put all with ( )into ( )
3. Repeat until all elements of visited
Approach:
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Split Example
Original Grid Scale-5 Grid Scale-4 Grid
Scale-3 Grid Scale-2 Grid Scale-1 Grid
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Predict Design
Goal: encode WC at each ( ) as difference from summary of local neighborhood behavior
Approach: fit order-m polynomial to scale-(j+1) SCsat neighboring ( ), evaluate at :
WC for is difference between scale-(j+1) SC and estimate:
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, depend only on m, d (dim. of )
Predict Design
Given predict order m, must only specify to find weights
1. Consider points s.t.
2. Pick as smallest (cost) subset s.t.
3. If can’t satisfy, reduce to , repeat Step 1
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Use min-norm solution [Jansen et al., 2001]
Approach: choose update weights so that weighted by integrals of constant :
Update Design
Goal: enhance transform stability by preserving average value encoded by SCs across scales
with
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Transform Network Traffic
update
predict
Example: , with ,
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A function is ( ) at point if polynomial of degree and some
such that
We show that, if is at for , then
depends only on constants
Coefficient Decay
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WT Application: Distributed Compression
IDEA: compress measurements by only allowing sensors with large-magnitude WCs to transmit to the sink.
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Compression Evaluation
Sample field classes:
Piecewise smoothacross discontinuity
Globally smooth
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Compressing Smooth Fields
(250 nodes, 100 trials)
aver
age
MS
E
number of coefficients
P onlyP, U
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Compressing Piecewise-Smooth Fieldsav
erag
e M
SE
number of coefficients (250 nodes, 100 trials)
P onlyP, U
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Energy vs. Distortion (Smooth Field)
MS
E
bottleneck energy (Joules) (1000 nodes)
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Energy vs. Distortion (Smooth Field)
Energy to compute WT
MS
E
bottleneck energy (Joules) (1000 nodes)
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Energy vs. Distortion (Smooth Field)
Energy to dump all measurements to sink
MS
E
bottleneck energy (Joules) (1000 nodes)
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Energy vs. Distortion (Smooth Field)
Beneficial operating regime
MS
E
bottleneck energy (Joules) (1000 nodes)
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Energy vs. Distortion (Piecewise-smooth Field)
MS
E
bottleneck energy (Joules) (1000 nodes)
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noise dominates
coefficientcount
PS
NR
1. in-network de-noising (requires inverse dist. WT)
2. compression with de-noising (guides threshold choice)
WT Application: Distributed De-noising
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Implementation Lessons
Implemented WT in Duncan Hallsensor network
Need to support common patternswith abstraction to ease algorithmprototyping
Surveyed IPSN 2003-2006
Distilled common comm. patterns into network application programming interface (API) calls
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Send to single address – source node ( ) sends message to single destination ( ), drawn from node ID space
Address-Based Sending
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Address-Based Sending
Send to list of addresses – source node sends message to multiple destinations, drawn from node ID space
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Address-Based Sending
Send to multicast address – source node sends message to single group address, drawn from multi-cast address space
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Address-Based API Calls
sendSingle (data, address, effort, hopLimit)
sendList (data, addList, effort, hopLimit)
sendMulti (data, address, effort, hopLimit)
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Address-Based API Calls
sendSingle (data, address, effort, hopLimit)
sendList (data, addList, effort, hopLimit)
sendMulti (data, address, effort, hopLimit)
Provide packet fragmentation
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Address-Based API Calls
sendSingle (data, address, effort, hopLimit)
sendList (data, addList, effort, hopLimit)
sendMulti (data, address, effort, hopLimit)
Drawn from node-ID address space
Drawn from multicast group address space
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Address-Based API Calls
sendSingle (data, address, effort, hopLimit)
sendList (data, addList, effort, hopLimit)
sendMulti (data, address, effort, hopLimit)
Energy-based transmission effort abstraction (per-packet basis)
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Address-Based API Calls
sendSingle (data, address, effort, hopLimit)
sendList (data, addList, effort, hopLimit)
sendMulti (data, address, effort, hopLimit)
Limit on number of forwarding hops to destination
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Region-Based Sending
Send to hop radius – source node sends message to all nodes within specified number of radio hops
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Region-Based Sending
Send to geographic radius – source node sends message to all nodes within specified geographic distance from its location
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Region-Based Sending
Send to circle – source node sends message to nodes (single or many) within a specified geographic distance of specified center
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Region-Based Sending
Send to polygon – source node sends message to nodes (single or many) within convex hull of specified list of vertex locations
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Region-Based API Calls
sendHopRad (data, hopRad, effort, hopLimit)
sendGeoRad (data, geoRad, outHops, effort, hopLimit)
sendCircle (data, centerX, centerY, radius, single, outHops, effort, hopLimit)
sendPolygon (data, vertCount, vertices, single, outHops, effort, hopLimit)
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Region-Based API Calls
sendHopRad (data, hopRad, effort, hopLimit)
sendGeoRad (data, geoRad, outHops, effort, hopLimit)
sendCircle (data, centerX, centerY, radius, single, outHops, effort, hopLimit)
sendPolygon (data, vertCount, vertices, single, outHops, effort, hopLimit)
Region specification
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Region-Based API Calls
sendHopRad (data, hopRad, effort, hopLimit)
sendGeoRad (data, geoRad, outHops, effort, hopLimit)
sendCircle (data, centerX, centerY, radius, single, outHops, effort, hopLimit)
sendPolygon (data, vertCount, vertices, single, outHops, effort, hopLimit)
Limit number of hops to route outside region to find path around voids
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Region-Based API Calls
sendHopRad (data, hopRad, effort, hopLimit)
sendGeoRad (data, geoRad, outHops, effort, hopLimit)
sendCircle (data, centerX, centerY, radius, single, outHops, effort, hopLimit)
sendPolygon (data, vertCount, vertices, single, outHops, effort, hopLimit)
Send to single node or multiple nodes in region
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Device Hierarchy Sending
Send to sink – source node sends message to sensor network’s data sink
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Device Hierarchy Sending
Send to parent – source node sends message to its parent in hierarchy of device classes
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Device Hierarchy Sending
Send to children – source node sends message to its children in hierarchy of device classes
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Device Hierarchy Sending
Send to children – source node sends message to its children in hierarchy of device classes
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Device Hierarchy API Calls
setLevel (level)
sendParent (data, effort, hopLimit) sendChildren (data, effort, hopLimit)
sendSink (data, effort, hopLimit)
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setLevel (level)
sendParent (data, effort, hopLimit) sendChildren (data, effort, hopLimit)
sendSink (data, effort, hopLimit)
Device Hierarchy API Calls
Allow application to assign hierarchy levels suited to device capabilities
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Device Hierarchy API Calls
Each level-L device associated with level-(L-1) parent, level-(L+1) children
setLevel (level)
sendParent (data, effort, hopLimit) sendChildren (data, effort, hopLimit)
sendSink (data, effort, hopLimit)
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Device Hierarchy API Calls
setLevel (level)
sendParent (data, effort, hopLimit) sendChildren (data, effort, hopLimit)childList = getChildren()
sendSink (data, effort, hopLimit)
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Device Hierarchy API Calls
setLevel (level)
sendParent (data, effort, hopLimit) sendChildren (data, effort, hopLimit)childList = getChildren()
sendSink (data, effort, hopLimit)
Each node can send directly to sink (level 1), regardless of level
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Receive Modes
1. receiveTarget – node can examine observe messages for which it is destination
2. receiveOverhear – node can passively examine any message transmitted within radio range
3. receiveForward – node can examine and modify any message it forwards
Finally, three receive modes supported:
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Receive Modes
1. receiveTarget – node can examine observe messages for which it is destination
2. receiveOverhear – node can passively examine any message transmitted within radio range
3. receiveForward – node can examine and modify any message it forwards
Finally, three receive modes supported:
Requires hop-by-hop reassembly of packet fragments
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Conclusions, Extensions
Developed distributed WT suited to sensor network deployment
Developed network API to support easy algorithm prototyping
• Investigate applicability to other tasks (e.g., query-routing, data recovery)
• Further study tradeoffs between temporal, spatio-temporal processing
• Implement API in resource-efficient manner
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For proper inverse WT at sink, each , must receive coeffs. from all ,
Options to ensure include:
1. Require high reliability from routing
2. Repair P,U on link failure
3. Repair P-only on link failure
Communication Reliability Requirements
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Starting with threshold , iterate:
Distributed Compression Protocol
1. sink broadcasts 2. each node with sends WC to sink (if not sent already)
3. sink collects WC’s, computes new estimate
4. while estimate residual exceeds some tolerance, repeat Step 1 for
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WT Application: Distributed De-noising
IDEA: exploit signal sparsity in WC’s to remove noise energyfrom measurements in wavelet domain
Denote original noisy measurements as , where IID
Can estimate each using modified WC’s
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noise dominates
coefficientcount
PS
NR
Two forms of distributed de-noising:
1. in-network de-noising (requires inverse dist. WT)
2. compression with de-noising (guides threshold choice)
De-noising Modes
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Universal Thresholding
Donoho/Johnstone’s universal threshold for univariate Gaussian noise
Must scale each coefficient to account for non-orthonormal transform (W) and nose variance :
Each scaling coefficient is modified as:
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Distributing Universal Hard Thresholding
To estimate , use median absolute deviation of fine-scale wavelet coefficients :
Use de-centralized median protocol, gossiping with local time-series estimates, single node collection/dissemination…
NOTE: must only estimate appropriate to stationarity of noise process
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De-noising Evaluation
Low, smooth
High, smooth
Low, disc.
High, disc.
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(PSNR in dB)
Results averaged over 50 trials of N=1000 nodes (randomly, uniformly placed)
De-noising Evaluation (In-Network)
Low, smooth
High, smooth
Low, disc.
High, disc.
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De-noising Evaluation (Compression with De-Noising)
Low, smooth
High, smooth
Low, disc.
High, disc.
PS
NR
coefficient count
PS
NR
coefficient count
PS
NR
coefficient count
PS
NR
coefficient count
Bayesuniv.orig.
Bayesuniv.orig.
Bayesuniv.orig.
Bayesuniv.orig.
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De-noising Evaluation (Compression with De-Noising)
Low, smooth
High, smooth
Low, disc.
High, disc.
PS
NR
coefficient count
PS
NR
PS
NR
coefficient count
PS
NR
coefficient count
Bayesuniv.orig.
Bayesuniv.orig.
Bayesuniv.orig.
Bayesuniv.orig.
coefficient count
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De-noising Evaluation (Compression with De-Noising)
(500 nodes, averaged over 50 trials)
Low, disc.
noise dominates
PS
NR
coefficient count
Bayesuniv.orig.