1 Compression and Storage Schemes in a Sensor Network with Spatial and Temporal Coding Techniques You-Chiun Wang, Yao-Yu Hsieh, and Yu-Chee Tseng IEEE.

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Compression and Storage Schemes in a Sensor Network with Spatial and Temporal Coding TechniquesYou-Chiun Wang, Yao-Yu Hsieh, and Y

u-Chee Tseng

IEEE Vehicular Technology Conference, 2008

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Outline

Introduction Multi-resolution compression and storage

(MCS) framework Compression and storage schemes Implementation and experimental results Conclusions

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Introduction

The communication overhead will dominate sensor node’s energy consumption

Sensing data reported from sensor nodes often exhibit a certain degree of data correlation Spatial correlation Temporal correlation

People may query different resolutions of sensing data from a wireless sensor network

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Multi-resolution compression and storage (MCS) framework

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Compression and storage schemes Spatial compression scheme Temporal compression scheme Storage scheme

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Spatial compression scheme

Layer-1 compression Layer-i (i > 1) compression Decompression

Compression ratio: ( 0 ≦ γ ＜ 1 )

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Layer-1 compression

A layer-1 processing node collects the sensing data from the sensor nodes in its block

M = (si,j)k×k

M =

28 27 28 29

29 28 28 29

30 29 28 29

29 29 28 28

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Layer-1 compression (2D-DCT) Two-dimensional discrete cosine transform (2

D-DCT) method 2D-DCT will compact those significant values

in the upper-left part of the transformed matrix

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Layer-1 compression (RZS)

A reduced zigzag scan (RZS) method is applied to translate M’ into an one dimensional array

k2×λ

λ = 1 −γ

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Layer-i (i > 1) compression

Reduce the length of array D (passed from the layer i−1) to λi × k2 elements

Layer-1

Layer-2

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Decompression

The sink recovers the corresponding array D to a two-dimensional matrix M’ = (ti,j)k×k

Adopt the inverse 2D-DCT method to transform M to a new matrix M’’ = (si,j)k×k

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Temporal compression scheme

The temporal compression scheme is performed by each sensor node

Users can specify a small update threshold δ to determine whether a node should transmit its data or not

δ= 2°C

S1,1= 28°C

Range: 28°C ± 2°C

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Storage scheme(1/2)

For a node i, we will store frames ft, ft−1, ft−3, ft−

7, · · · , and ft−2ni−1

+1

1 2 3

ft ft−1 ft−3

4 3 1

5 4 2

→

→

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Storage scheme(2/2)

fj has been stored in node i’s local memory, node I directly replies fj to the sink

j < t−2ni−1 +1, node i replies a fail message to the sink because fj is too old to be stored in node i

5 4 2

f3 = ?

(f4+f2)/2

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Implementation and experimental results We use the MICAz Motes as sensor nodes a

nd processing nodes Set the system parameters α = 4 and k = 2 We use this prototype to collect indoor tempe

ratures during 25 hours The compression ratio γ is set to 0.25 The update threshold δ is set to 0.2°C

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The total amount of message transmissions

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Average temperatures reported by the 16 nodes

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Conclusions

MCS provides multi-resolution data compression and storage in a wireless sensor network

MCS can effectively reduce message transmissions of sensor nodes

MCS framework not only significantly reduces the message transmissions but also preserves important characteristics of sensing reports

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Thank you!

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M

26 28 30

26 28 29

26 27 28

M’

77.9 118.5 126.7

110 167.4 172.9

109.8 161 166.5