Mobicom'09 CDG

7/31/2019 Mobicom'09 CDG

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Compressive Data Gathering for

Large-Scale Wireless Sensor

Networks

Chong Luo, Feng Wu, Jun Sun and Chang Wen Chen

Mobicom09, Beijing, China


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Outline

Background

Compressive sensing theory

New research opportunities

Compressive Data Gathering

The first complete design to apply CS theory for

sensor data gathering

Conclusion


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Compressive Sensing

If an N-dimensional signal is K-sparse in a known domain, itcan be recovered from M random measurements by:


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New Research Opportunities

Compressive Sensing Hallmarks

Universal

Same random projection op.for any compressible signal

Democratic

Potentially unlimited numberof measurements

Each measurement carriesthe same amount of

information

Asymmetrical

Simple encoding, mostprocessing at decoder

Data Communications Research

Random linear networkcoding

Achieves multicast capacity

Fountain code

a.k.a. rateless erasure code

Perfect reconstruction fromN(1+) encoding symbols

Distributed source coding e.g. Slepian-Wolf coding

Blind encoding, jointdecoding


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From Compressive Sensing to

Compressive Data Gathering

The asymmetrical property makes CS a perfect

match for wireless sensor networks

Compressive Sensing Compressive Data Gathering

Sample-then-compress

Sample-with-compression

Compress-then-transmit

Compress-with-transmission


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Data Gathering in WSNs

Challenges

Global communication cost reduction

Energy consumption load balancing

Sink

Internet or

Satellite

Sensing field


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Basic Idea

A simple chain topology

sNs1 s2 s3

d1

d1

d2

d1

d2

dN

Global comm. cost Bottleneck load

Baseline transmission N(N+1)/2 N

Proposed CDG NM M


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Is Reconstruction Possible?

Facts

Sensor readings exhibit strong spatial correlations

According to CS theory

Reconstruction can be achieved in a noisy setting by

solving:

M


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Practical Problem 1

Abnormal readings compromise data sparsity

Solution:

-10

-5

0

5

10

Signal in time domain

-5

0

5

10

Representation in DCT domain

-10

-5

0

5

10

-5

0

5

10

-10

-5

0

5

10

-10

-5

0

5

10

Signal d1

Signal d2

-10

-5

0

5

10

-10

-5

0

5

10

Representation ofd1 in DCT domain

Representation ofd2 in time domain

7-sparse

Overcomplete

basis


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Practical Problem 2

If a signal is not sparse in any intuitively known domain

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56

7

8

9

10

11

12

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15

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20

t

value

y d


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Universal Sparsity

CS-based data representation and recovery isoptimal in exploiting data sparsity

Encoder

The same random projection operation Decoder

Select and design representation basis Reorder signal d to make it sparse in a known domain

Neither transform-based compression nordistributed source coding is able to exploit thesespecial types of data sparsity


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Network Capacity Gain

Theorem: In a wireless sensor

network with N nodes, CDG

can achieve a capacity gain

of N/M over baselinetransmission, given that

sensor readings are K-sparse,

and M = c1K.

Mathematical proof

Simulation verification


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Example 1

CTD data from NOAA

N=1000, K40

0

10

20

30

0 200 400 600 800 1000

T

emperature()

Depth / Pressure (dbar)

-10

0

10

20

30

0 200 400 600 800 10000

10

20

30

40

50

0 50 100 150 200

SNR(dB

)

Number of random measurements (M)

M=100

Recon. Precision 99.2%Comm. Reduction 5

Capacity Gain 10


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Utilizing Temporal Correlation

Sensor readings at t0 + t are sparse as well

Temperatures do not change violently with time

10

20

30

(a) Original

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20

30

(b) Reconstruction from 0.5N measurements

10

20

30

(c) Reconstruction from 0.3N measurements

t=30min


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Conclusion

Compressive Sensing is an emerging field whichmay bring fundamental changes to networkingand data communications research

Our Contributions The first complete design to apply CS theory to sensor

data gathering

CDG exploits universal sparsity

CDG improves network capacity

Future Work Bring innovations to LDPC, NC, DSC, and Fountain

code through CS theory


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THANKS!

Documents

Mobicom'09 CDG