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

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

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-21364 32 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

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

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

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

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

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

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

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

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)

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

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