“Real” Signal Processing with Wireless Sensor

NetworksGyörgy Orosz, László Sujbert, Gábor Péceli{orosz,sujbert,peceli}@mit.bme.hu

Department of Measurement and Information SystemsBudapest University of Technology and Economics, Hungary

Regional Conference on Embedded and Ambient Systems–RCEAS 2007Budapest, Hungary, Nov. 22-24, 2007

Wireless signal processing „Real” signal processing

Fast changing signals Hard real-time operation

Advantages of Wireless Sensor Networks (WSNs) Easy to install Flexible arrangement

Difficulties of utilization of WSN: Data loss Limit of the network bandwidth Lots of autonomous systems

Sensor network from signal processing aspects Topics

Signal sensing Synchronization Distributed signal processing

ANC: a case study

mote1

moteG

DSP board

reference signalgateway

mote

codec DSP

mote2

moteN

Plant to be controlled: acoustic system

Noise sensing:

Berkeley micaz motes

Actuators: active loudspeakers

Gateway: network DSP Signal processing:

DSP board ADSP-21364 32 bit floating point 330 MHz 8 analog output channels

Motes TinyOS ATmega128 Sensor boards

Identification

microphone

Physical arrangement

sensor mote

DSP board

gateway mote

active loudspeaker

Sampling precision 1.

Sampling with low priorityShared timer

50 100 150 200 250-40

-30

-20

-10

0

10

20

30

frequency [Hz]

ampl

itude

[dB

]

50 100 150 200 250-40

-30

-20

-10

0

10

20

30

frequency [Hz]am

plitu

de [

dB]

Sampling with high priorityDedicated timer

Sampling precision 2. □□ Middle level timing priority □□ 25 samples size packets □□ Effects of disturbances

Random disturbance: contributes to noise

Periodic disturbance : spurious spectrum lines 0 200 400

-40

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0

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40

frequency [Hz]

ampl

itude

[dB

]

0 200 400-40

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frequency [Hz]

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itude

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]

0 200 400-40

-20

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frequency [Hz]

ampl

itude

[dB

]

255 260 26510

20

30

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frequency [Hz]

ampl

itude

[dB

]

255 260 26510

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30

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frequency [Hz]

ampl

itude

[dB

]

255 260 26510

20

30

40

frequency [Hz]

ampl

itude

[dB

]

Increasing deviation (td) from periodic disturbance

t

Average period

Deviation from average period ( td )

Synchronization 1. Delay: Td = Tt + dt Unsynchronized subsystems:

Changing delay Stability problems in

feedback systems Goal: constant delay

Tt=const.: deterministic protocol

dt=const.: synchronization

Ti

Tt

tmote

TS_mote : sampling rate of the motes

Ti-1

Tt

TS_moteTnTn-1Tn-2

Tt

dti–1

TS_DSP

tDSP

TS_DSP : sampling rate of the DSP

Ti-2

dti

Tt : data transmission delay

Tt dt

Synchronization 2. Physical synchronization:

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: Motes: physical Motes DSP: linear interpolation

Td1Td2

Tn

tsyst1

Td1=Td2=const

t

d1

d2

TSmote

dt

Ti

d3

f(t)

dtT

dddTf

Smotei

212)(ˆ

tsyst2

Ta: arrival time of data

tmotes

tDSP

Tn

Ti

Physicalsynch.

Interpolation

Interp.Tt

Data transmission methods

Data transmission methods

Transmission of

row data 1.8 kHz sampling frequency on

the motes Synchronization of WSNDSP LMS and resonator based ANC

algorithms Bandwidth restriction:

about 3 sensors

Transformed domain

data transmission 1.8 kHz sampling frequency on

the motes Transmission of Fourier-

coefficients Increased number of sensors:

8 sensors (expansion possible)

Distributed ANC system

Fourier analysis on motes Control algorithm on DSP Synchronization of base functions Computational limits

acoustic plant

control signals

reference signal

ANC algorithm

R(z)

DSP

: synchronization messages: data (Fourier-coefficients) transmission messages

error signals

FA

mote1

FA

moteN

A(z)

gateway

mote2

FA

Summary and future plans Utilization of WSN in closed loop signal

processing systems Importance of signal observation

Sampling Synchronization

Distributed signal processing Searching for possible ways of data

reduction