Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor...

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Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest University of Technology and Economics, Hungary Instrumentation and Measurement Technology Conference – IMTC 2007 Warsaw, Poland, May 1-3, 2007

Transcript of Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor...

Page 1: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Testbed for Wireless Adaptive Signal Processing SystemsGyörgy Orosz, László Sujbert, Gábor Péceli

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

Instrumentation and Measurement Technology Conference – IMTC 2007Warsaw, Poland, May 1-3, 2007

Page 2: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Wireless signal processing Advantages of Wireless Sensor Networks (WSNs)

Easy to install Flexible arrangement

Wireless signal processing Difficulties of utilization of WSN:

Data loss Undeterministic data transfer Limit of the network bandwidth

Purpose of the testbed Considerations in the design

Hardware structure Adequate application

Realistic demands Exploits the resources

Page 3: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

ANC as test application Principles of Active Noise Control (ANC) Why ANC?

Inherently MIMO systems: plenty of sensors Plant: acoustic system

Scalable Linear Exist everywhere

Various algorithms: No HW modification Comparability of structures

Easy to build and cheap Identification: characterization of signal path

Page 4: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

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 8 analog output channels 330 MHz

motes

System configuration

mote1

moteG

DSP board

reference signalgateway

mote

codec DSP

mote2

moteN

microphone

Page 5: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Research fields related to the testbed Signal processing adaptation to WSN Synchronization Data transmission

Effective algorithms Data compression

Distributed signal processing

MIMO plant

sensor1

sensor2

sensorN

WirelessNetwork

WirelessNetwork

Signalprocessing

Control signals

feedback signalssensors

Synchronization Distributed signal processing

Data transmission Error handling Signal processing

Sync.(WSN DSP)

Page 6: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Results 1. Implemented ANC algorithms Synchronization algorithm in WSN

Principles of operation

sensor mote

DSP board

gateway mote

active loudspeaker

Page 7: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Results 2. Deterministic network operation Implicit synchronization messages

Synchronization with continuous data flow No extra load for network

DSP

mote2 mote3

gateway

mote1 mote4

: data messages

: token

: synchron message

Network topology

Page 8: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Results 3.

Data transmission methods

Transmission of

row data 1.8 kHz sampling frequency on

the motes Synchronization of WSNDSP LMS and observer based ANC

algorithms Bandwidth restriction:

about 2-3 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)

Page 9: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Conclusions Platform for testing wireless systems

Application: ANC Components:

Berkeley micaz motes ADSP-21364 floating point DSP

Main difficulties Data transmission Synchronization

Some codes and technical details available at http://home.mit.bme.hu/~orosz/wireless

Page 10: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Future work Improvement of the website

http://home.mit.bme.hu/~orosz/wireless Discover the limits of the system

Sensor network: bandwidth limit DSP: computational and memory limits

Page 11: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.
Page 12: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Synchronization 1

Mechanism of the synchronization

reference timer

S/H controller tuneable timer

Ta

Tloc

IT IT

fquartz_2fquartz_ref

Ndiv

reception time of the messages

reference mote

mote to be synchronized

Page 13: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Synchronization 2

Graph of the reception time of synchronization messages

50 100 1500

1

2

3

4

5

x 10-4

time [sec]

Tx [

sec]

unsynchronized

synchronized

Page 14: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Synchronization 3

Tdiff = ∆tsend + TSend – Tloc2

Send(packet)

Receive(packet)

moteref

motei

Sampling time instants

tsend

trec

TSend

tsamp_r

tsamp_i

t

tTloc2

∆tsend

Page 15: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Synchronization 4

t

t

t

Ts

T1_a

T1_b

T1_c

T2_a

T2_b

T2_c

T1_ref T2_ref

Ts

Tloc.a_1

Tloc.b_1

Tloc.c_1

Tloc.a_2

Tloc.b_2

Tloc.c_2

a)

b)

c)

Tloc.a_1 = Tloc.ref

Tloc.b_1 > Tloc.ref

Tloc.c_1 < Tloc.ref

Tloc.ref : the reference value of Tloc.x_y that is the time difference between the sampling time

instant and reception time of the synchronization message

reception time instant of the synchronization message

Page 16: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Synchronization 5

Indirect proof for synchronization

Page 17: Testbed for Wireless Adaptive Signal Processing Systems György Orosz, László Sujbert, Gábor Péceli Department of Measurement and Information Systems Budapest.

Network timingt

t

t

t

t

Tp

DSP

gateway

mote0

mote1

mote2 Twin_0 Twin_1 Twin_2 Twin_0

Twin_i: time gap of ith moteTp: one network period

: data messages: synchronization messages