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“Real” Signal Processing with Wireless Sensor Networks

“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

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“Real” Signal Processing with Wireless Sensor Networks

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  1. “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

  2. 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

  3. microphone mote1 mote2 moteN DSP codec DSP board moteG gateway mote reference signal ANC: a case study • 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

  4. activeloudspeaker DSP board gateway mote sensor mote Physical arrangement

  5. Sampling precision 1. Sampling with low priority Shared timer Sampling with high priority Dedicated timer

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

  7. Tn-2 Tn-1 Tn TS_mote tmote Tt Tt Tt dti–1 dti tDSP Ti-2 Ti-1 Ti TS_DSP dt Tt TS_mote:sampling rate of the motes TS_DSP:sampling rate of the DSP Tt:data transmission delay 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

  8. Tn tmotes Physical synch. Tt Interp. tDSP Interpolation Ti 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 d3 dt TSmote Ta: arrival time of data

  9. Data transmission methods 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)

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

  11. 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

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