Compressive Data Gathering for Large-Scale Wireless Sensor Networks Chong Luo Feng Wu Shanghai Jiao Tong University Microsoft Research Asia Jun Sun Chang Wen Chen Shanghai Jiao Tong University SUNY at Buffalo, NY 14260- 2000, USA MobiCom 2009, Sep. 20- 25
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Compressive Data Gathering for Large-Scale Wireless Sensor Networks
Compressive Data Gathering for Large-Scale Wireless Sensor Networks. Chong Luo Feng Wu Shanghai Jiao Tong University Microsoft Research Asia Jun Sun Chang Wen Chen - PowerPoint PPT Presentation
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Compressive Data Gathering for Large-Scale Wireless Sensor Networks
Chong Luo Feng WuShanghai Jiao Tong University Microsoft Research Asia
Jun Sun Chang Wen ChenShanghai Jiao Tong University SUNY at Buffalo, NY 14260-
2000, USA
MobiCom 2009, Sep. 20-25
Outline
• Compression techniques on sensor networks– Compression with explicit communication– Distributed source coding– Compressive Sensing(sampling)
• Proposed Compressive Data Gathering– Data gathering diagram– Compressive sensing
• Simulation• Conclusions
Compression Techniques on Sensor Networks
• Compression with explicit communication• Cristescu et al. (2006) proposed a joint entropy coding
approach
1 2X1
H(X2|X1)
X1, H(X2|X1)
EZLMS Link: http://www.powercam.cc/slide/3023
Distributed Wavelet Transform
• Assumptions: piecewise smooth data – Ciancio et al. (2006) and A’cimovi’c et al. (2005)
(1) Even nodes first broadcast their readings. (2) Upon receiving the readings from both sides, odd nodes compute the high pass coefficients
h(·)(3) Then, odd nodes transmit h(·) back and even nodes compute the low pass coefficients l(·)(4) After the transform, nodes transmit significant coefficients to the sink
Proposed Compressive Data Gathering-- Measurement Matrix
Proposed Compressive Data Gathering-- Measurement Matrix
Goal:(1) Reduce global
communication cost.
(2) Load balance
Proposed Compressive Data Gathering-- Measurement Matrix
44, di 55, di
66, di
6
3,
jjji d
Proposed Compressive Data Gathering-- Data Recovery
• Conditions:(1)
(2) Incoherence: correlation between and
kk MnkCM4 ~ 3 :Suggestion
log),(2
matrixt measuremen Random :Suggestion
|,|max),(,1 jiNji
N
Reconstruction: optimization
syss
osubject t ||||min 1
Linear programmingOrthogonal matching pursuit (OMP)
Recover Data with Abnormal Readings
Proposed SolutionNormal reading
Deviated values of abnormal readings
New basis
NS-2 Simulation
• Topology:– Chain vs. Grid
• Data sparsity is assumed to be 5%.– For example, when N = 1000, K = 50, and M = 200
Capacity-- Chain topology
• N=1000• The distance between
adjacent nodes are 10 meters
Capacity-- Grid topology
• N=1089• 33 rows x 33 cols• The distance between adjacent
nodes is 14 meters
Packet Loss Rate-- Grid topology
Experiments on Real Data Sets-- CTD Data from Ocean
K=40M=100
Experiments on Real Data Sets-- CTD Data from Ocean
Experiments on Real Data Sets-- Temperature in Data Center
Experiments on Real Data Sets-- Temperature in Data Center
Low spatial correlation : not sparse
Experiments on Real Data Sets-- Temperature in Data Center
• Sort di in ascending order according to their sensing values at a particular moment t0
– The resulting readings are piece-wise smooth.
– server temperatures do not change violently,• sensor readings collected
within a relatively short time period can also be regard as piece-wise smooth if organized in the same order.
• N=498
Experiments on Real Data Sets-- Temperature in Data Center
Conclusions
• This paper proposed a novel scheme for energy efficient data gathering in large scale wireless sensor networks based on compressive sampling theory.– Convert compress-then-transmit process into
compress-with-transmission process• We have shown that CDG can achieve a