1 an evaluation of multi-resolution storage for sensor networks d. ganesan, b. greenstein, d....

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1 An Evaluation of Multi- resolution Storage for Sensor Networks D. Ganesan, B. Greenstein, D. Perelyubsk iy, D. Estrin, J. Heidemann ACM SenSys 2003 ACM SenSys 2003

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1

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

4

Introduction

5

Dimensions ArchitectureSpatial and Temporal Summarization

6

Dimensions ArchitectureDrill-down Querying

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

14

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

16

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

20

Experimental EvaluationImplementation Block Diagram

Construct the summaries.

Allocate storage to summaries.

Hierarchical storage and drill-down search.

9/7 wavelet filter

21

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%.

26

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

30

Conclusion

An ideal search and storage system for sensor networks. Low communication overhead Efficient search for a broad range of query Long-term storage capability DIMENSIONS

Long-term storage and query processing. Progressive aging of summaries Load-sharing by cluster-rotation