Transcript Document 7585544

“Real” Signal Processing
with Wireless Sensor
Networks
György Orosz, László Sujbert, Gábor Péceli
{orosz,sujbert,peceli}@mit.bme.hu
Department of Measurement and Information Systems
Budapest University of Technology and Economics, Hungary
Regional Conference on Embedded and Ambient Systems–RCEAS 2007
Budapest, Hungary, Nov. 22-24, 2007
Wireless signal processing
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„Real” signal processing
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Advantages of Wireless Sensor Networks (WSNs)
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Easy to install
Flexible arrangement
Difficulties of utilization of WSN:
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Fast changing signals
Hard real-time operation
Data loss
Limit of the network bandwidth
Lots of autonomous systems
Sensor network from signal processing aspects
Topics
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Signal sensing
Synchronization
Distributed signal processing
ANC: a case study
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mote1
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Plant to be controlled: acoustic
system
microphone
Noise sensing:
Berkeley
micaz motes
mote2
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moteN
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Actuators:
active loudspeakers
Gateway: network  DSP
Signal processing:
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DSP board
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DSP board
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moteG
codec
reference signal
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DSP
Motes
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gateway
mote
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ADSP-21364 32 bit floating point
330 MHz
8 analog output channels
TinyOS
ATmega128
Sensor boards
Identification
Physical arrangement
active
loudspeaker
DSP
board
gateway
mote
sensor
mote
30
30
20
20
10
10
amplitude [dB]
amplitude [dB]
Sampling precision 1.
0
-10
0
-10
-20
-20
-30
-30
-40
50
100
150
frequency [Hz]
200
250
Sampling with low priority
Shared timer
-40
50
100
150
frequency [Hz]
200
250
Sampling with high priority
Dedicated timer
40
40
20
20
20
0
-20
0
200
400
frequency [Hz]
0
200
400
frequency [Hz]
30
20
260
265
frequency [Hz]
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-20
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0
200
400
frequency [Hz]
Random disturbance:
contributes to noise
Periodic disturbance :
spurious spectrum lines
40
30
20
10
255
□ Middle level timing priority
□ 25 samples size packets
□ Effects of disturbances
0
-40
40
amplitude [dB]
amplitude [dB]
-20
-40
40
10
255
0
amplitude [dB]
-40
amplitude [dB]
40
amplitude [dB]
amplitude [dB]
Sampling precision 2.
260
265
frequency [Hz]
30
Deviation from average
period ( td )
20
10
255
260
265
frequency [Hz]
Increasing deviation (td) from periodic disturbance
t
Average period
Synchronization 1.
Tn-2
Tn-1
TS_mote
Tn
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tmote
Tt
Tt
Tt
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dti–1
TS_DSP
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Delay: Td = Tt + dt
Unsynchronized subsystems:
Ti-2
dti
Ti-1
Ti
tDSP
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Goal: constant delay
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TS_mote : sampling rate of the motes
TS_DSP : sampling rate of the DSP
Tt
: data transmission delay
Tt
dt
Changing delay
Stability problems in
feedback systems
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Tt=const.: deterministic
protocol
dt=const.: synchronization
Synchronization 2.
tsyst1
Td1
Physical synchronization:
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Td2
tsyst2
Tn
Td1=Td2=const
d  d2
fˆ(Ti )  d2  1
dt
TSmote
d2
Ti
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d1
f(t)
d3
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Sampling frequencies are the same
Tuning of the timers
Interpolation: Signal value is
estimated in signal processing points
Algorithm transformation: algorithm
parameters are transformed into Ta
(when data arrived).
Synchronization in the ANC system:
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Motes: physical
Motes  DSP: linear interpolation
Tn
t
dt
TSmote
Physical
synch.
tmotes
Tt
Ta: arrival time of data
Interp.
Interpolation
Ti
tDSP
Data transmission methods
Data transmission methods
Transmission of
row data
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1.8 kHz sampling frequency on
the motes
Synchronization of WSNDSP
LMS and resonator based ANC
algorithms
Bandwidth restriction:
about 3 sensors
Transformed domain
data transmission
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1.8 kHz sampling frequency on
the motes
Transmission of Fouriercoefficients
Increased number of sensors:
8 sensors (expansion possible)
Distributed ANC system
A(z)
error
signals
mote1
FA
DSP
mote2
acoustic
plant
FA
gateway
ANC
algorithm
R(z)
moteN
FA
: synchronization messages
: data (Fourier-coefficients) transmission messages
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Fourier analysis on motes
Control algorithm on DSP
Synchronization of base functions
Computational limits
reference
signal
control
signals
Summary and future plans
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Utilization of WSN in closed loop signal
processing systems
Importance of signal observation
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Sampling
Synchronization
Distributed signal processing
Searching for possible ways of data
reduction