“Real” Signal Processing with Wireless Sensor Networks György Orosz, László Sujbert, Gábor Péceli 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 , 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 mote 1 mote G DSP board reference signal gateway mote codec DSP mote 2 mote N  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  Identification microphone

Physical arrangement sensor mote DSP board gateway mote active loudspeaker

Sampling precision 1. Sampling with low priority Shared timer Sampling with high priority Dedicated 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 Increasing deviation (t d ) from periodic disturbance t Average period Deviation from average period ( t d )

Synchronization 1.  Delay: T d = T t + dt  Unsynchronized subsystems: Changing delay Stability problems in feedback systems  Goal: constant delay T t =const.: deterministic protocol dt=const.: synchronization TiTi TtTt t mote T S_mote : sampling rate of the motes T i-1 TtTt T S_mote TnTn T n-1 T n-2 TtTt dt i–1 T S_DSP t DSP T S_DSP : sampling rate of the DSP T i-2 dt i T t : data transmission delay TtTt 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 T a (when data arrived).  Synchronization in the ANC system: Motes: physical Motes  DSP: linear interpolation Td1Td1 Td2Td2 TnTn t syst1 T d1 =T d2 =const t d1d1 d2d2 T Smote dt TiTi d3d3 f(t)f(t) t syst2 T a : arrival time of data t motes t DSP TnTn TiTi Physical synch. Interpolation Interp. TtTt

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  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 mote 1 FA mote N A(z) gateway mote 2 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