1. Signal Processing Aspects of Real-time Wireless Sensor Network Applications
György Orosz
Department of Measurement and Information Systems
Budapest University of Technology and Economics, Hungary
HNI - MIT Knowledge Sharing Symposium
Budapest, Hungary, February 10., 2010

2. Wireless signal processing
Sensor network from signal processing aspects
Real-time 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
Undeterministic data transfer
Limit of the network bandwidth
Lots of autonomous systems
Topics
Signal sensing
Synchronization of autonomous subsystems
Network protocol
Distributed signal processing

3. ANC: a case study DSP Plant to be controlled: acoustic system
Noise sensing:
Berkeley
micaz motes
Actuators:
active loudspeakers
Gateway: network  DSP
Signal processing:
DSP board
ADSP bit floating point
330 MHz
8 analog output channels
Motes
TinyOS
ATmega128
Sensor boards
250kbps radio
Identification
mote1
moteG
DSP board
reference signal
gateway mote
codec
DSP
mote2
moteN
microphone

4. Physical arrangement active loudspeaker DSP board gateway mote
sensor mote
DSP board
gateway mote
active loudspeaker

5. Sampling precision 1. Sampling with low priority shared timer
Sampling with high priority
dedicated timer

6. Increasing deviation (td) from periodic disturbance
Sampling precision 2.
□ Middle level timing priority
□ 25 samples size packets
□ Effects of disturbances
Random disturbance: contributes to noise
Periodic disturbance : spurious spectrum lines t
Average period
Deviation from average period ( td )
Increasing deviation (td) from periodic disturbance

7. Synchronization 1. Delay: Td = Tt + dt Unsynchronized subsystems:
tmote
TS_mote : sampling period of the motes
Ti-1
TS_mote Tn
Tn-1
Tn-2
dti–1
TS_DSP
tDSP
TS_DSP : sampling period of the DSP
Ti-2
dti
Tt : data transmission delay
Delay: Td = Tt + dt
Unsynchronized subsystems:
Changing delay
Stability problems in feedback systems
Goal: constant delay
Tt=const.: deterministic protocol
dt=const.: synchronization Tt dt

8. Synchronization 1. (stability)
noise samples sent by the sensor
estimated noise according to the estimated delay Ti
time Tn
estimated delay
anti noise from estimated noise
real delay
sampling
anti noise:
real noise signal:
Stable:
noise suppression
the delay estimation is correct

9. Synchronization 1. (stability)
noise samples sent by the sensor
estimated noise according to the estimated delay
Ti-1
time
Tn-2
estimated delay
anti noise from estimated noise
real delay
sampling
anti noise:
real noise signal:
Unstable:
noise amplification
the delay estimation is incorrect

10. 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
tsyst1
Td1
Td2
tsyst2 Tn
Td1=Td2=const d1
f(t) d2 Ti t
tmotes
tDSP Tn Ti
Physical
synch.
Interpolation
Interp. Tt d3 dt
TSmote
Ta: arrival time of data

11. Data transmission methods
Transmission of
row data
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
1.8 kHz sampling frequency on the motes
Transmission of Fourier-coefficients
Increased number of sensors: 8 sensors (expansion possible)

12. Distributed ANC system
A(z)
error signals FA
mote1
reference signal
ANC algorithm
R(z)
DSP
acoustic plant
mote2 FA
gateway
control signals FA
moteN
: synchronization messages
: data (Fourier-coefficients) transmission messages
Fourier analysis on motes
Control algorithm on DSP
Synchronization of base functions
Computational limits

13. Summary and future plans
Utilization of WSN in closed loop signal processing systems
Importance of signal observation
Sampling
Synchronization
Distributed signal processing
Future research goals
Searching for possible ways of data reduction
Analysis of the effect of data loss