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An Evaluation of Multi-resolution Storage for Sensor Networks
D. Ganesan, B. Greenstein, D. Perelyubskiy, D. Estrin, J. Heidemann
ACM SenSys 2003ACM SenSys 2003
2
Papers
DIMENSIONS: Why do we need a new Data Handling architecture for Sensor Networks
Hotnets-I 2002Hotnets-I 2002
An Evaluation of Multi-resolution Storage for Sensor Networks
ACM Sensys 2003ACM Sensys 2003
Multi-resolution Storage and Search in Sensor Networks
ACM Transactions On Storage 2005ACM Transactions On Storage 2005
3
Outline
Introduction Dimensions Architecture Aging Problem Formulation System Implementation Experimental Evaluation Future Work and Conclusion
7
Hierarchy ConstructionFrom the view of communication
cluster headcluster head
cluster head cluster head
cluster head
8
Hierarchy ConstructionFrom the view of local storage
cluster head
cluster headcluster head
cluster head cluster head
9
Hierarchy ConstructionLoad-balancing Scheme
cluster head
cluster headcluster head
cluster head
cluster head
10
Hierarchy ConstructionProcessing at each level
…
local storage
data retrieval
x
y
time
At level i…
compressed summaries from children node...
Reconstructed Data Cube
for future query…
11
Storage UtilizationCircular Buffer
All available storage gets filled… When to drop these summaries? How to drop these summaries? Graceful query quality degradation.
local storage capacity
Resolution 4 Resolution 1Resolution 2Resolution 3
Local Storage Allocation
12
Graceful DegradationLong-term Storage vs. Query Quality (1/2)
Level 0
Level 1
Level 2
Time presentpast
Qu
ery
Accu
racy
High query accuracyLow compactness
Low query accuracyHigh compactness
low
high
13
Graceful DegradationLong-term Storage vs. Query Quality (2/2)
Example: gracefully degrading storage
the coarsest summaries,
the longest period of time
How long should a summary be stored in the network?
progressively shorter time period
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Aging ProblemCommunication Overhead
communication rate at level i
total amount of data from level i to i+1
level i
level i
level i
level i
level i+1ic ,
i
i
i cr
4
iNii rR 4log4
RateDataRaw :
15
Aging ProblemQuery Quality and Storage Overhead
Query accuracy if a drill-down terminates at level i
The amount of data that each node allocates for summaries from level i
iq
kqqq ...10
is
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Aging ProblemApproximate User-specified Aging Function
Qu
ery
Acc
ura
cy
Time
Quality Difference
present past
iAge
95%
50%
userQ
systemQ
user-desired quality degradation
system-provided step function
Objective: minimize the worst case quality difference
)))(diff((0 tqMaxMin Tt
1 4
ir
s
R
NsAge
i
ii
i
ii
17
Aging ProblemGiven Other Constraints
Drill-down constraint
Storage constraint
kiAgeAge ii 0 ,1
Sski i 0
variable integerr
s
i
i 4
S: local storage constraint
18
Choosing an Aging StrategyPrior Information
FullFull
No availableNo available
Omniscient Algorithm
Training-based Algorithm
Greedy Algorithm
Solve:
Constraint Optimization Problem
Use all data to determine optimal storage allocation.
Use training dataset to determine aging parameter.
resolution bias:
coarse finer finest
1 21
pri
or
info
rmat
ion
19
Experimental EvaluationImplementation and Parameter Settings
Present the design and implementation on Linux platform Emstar, a Linux-based emulator/simulator for sensor networks Query surveys on an iPAQ-based implementation
Geo-spatial precipitation dataset 15 x 12 grid, 50 kilometers apart Precipitation data from 1949 to 1994
System Parameters = 3 epochs * 365 samples/epoch * 2 bytes/sample = 2190 bytes c0 : c1 : c2 : c3 = 6 : 12 : 24 : 48
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Experimental EvaluationImplementation Block Diagram
Construct the summaries.
Allocate storage to summaries.
Hierarchical storage and drill-down search.
9/7 wavelet filter
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Experimental EvaluationIPAQ Wavelet Codec
y
x
time 3D DWT Quantization
RLE EncoderHuffman Encoder
Transmission over the air
Huffman Decoder RLE Decoder
yx
time Reconstructed 3D ArrayReconstructed 3D Array
Coding
Decoding
level i cluster head
level i+1 cluster head
22
Experimental EvaluationCommunication Overhead
Communication Rate per Level
6
1224
48
input compression parameter
The dimensions of the grid are not perfectly dyadic (power of 2).
23
Experimental EvaluationDrill-down Query Performance
Query Types GlobalDailyMax GlobalYearlyMax LocalYearlyMean GlobalYearlyEdge
Temporal Scale
Sp
atia
l S
cale
All
Nod
esS
ingl
e N
ode
Daily Yearly
Not evaluated
Daily MaxYearly Max
Yearly Edge
Local Mean
real
realmeasuredErrorFraction
24
Experimental EvaluationDrill-down Query Performance
Query Error vs. Terminate Level
40% - 50%
0% - 10%
GlobalYearlyEdge?
25
Experimental EvaluationAging Performance Evaluation
Error Comparison between different Aging Strategies
Omniscient (entire) vs. Training (first 6 years) DatasetOmniscient (entire) vs. Training (first 6 years) Dataset
The predicted error of the Training Scheme is within 5%.
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Experimental EvaluationAging Performance Evaluation
Aggregate results over a range of storage sizes and query types.
Storage Sizes: 0 – 100 KB, four query typesStorage Sizes: 0 – 100 KB, four query types
less than 1% worse than the optimal solution!
optimal
biasresolution :ttf 1)(
27
Experimental EvaluationAging Performance Evaluation
Comparison of Aging Strategies for GlobalYearlyMax
002.0
Increasing the storage size reduce fraction error!
28
Future Research ProblemsIrregular Node Placement
Micro-climate monitoring sensor network at James Reserve
How to handle irregularity?
29
Future Research ProblemsPerformance of Daily Max Query
The quality does not always improve!Level 2 Level 3