Collaborative Signal Processing for Energy-Efficient Self-Organizing - - PowerPoint PPT Presentation

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Collaborative Signal Processing for Energy-Efficient Self-Organizing - - PowerPoint PPT Presentation

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Collaborative Signal Processing for Energy-Efficient Self-Organizing Wireless Sensor Network Andrea Conti, Davide Dardari, Roberto Verdone IEIIT-BO/CNR, DEIS University of Bologna, Bologna, Italy a.conti@ieee.org IEIIT-BO/CNR, University of

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Collaborative Signal Processing for Energy-Efficient Self-Organizing Wireless Sensor Network

Andrea Conti, Davide Dardari, Roberto Verdone IEIIT-BO/CNR, DEIS University of Bologna, Bologna, Italy a.conti@ieee.org

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Introduction

  • We analyze the performance of a dense energy-efficient

Wireless Sensor Network for distributed collaborative environment monitoring

  • The target multi-dim process is estimate from samples

captured by nodes (sensor+wireless transceiver) randomly uniformly distributed

  • We evaluate the impact of

Collaborative Signal Processing

  • n both

– estimation error – lifetime

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Motivations of the work

Many applications require sensing from random node’s position Many papers in the literature are based on simulations or consider

deterministic channels analytical framework for WSN design in realistic wireless medium

WSN design aspects:

  • Channel model path-loss + shadowing
  • Connectivity
  • Energy consumption
  • Information routing
  • Process estimation quality
  • Node’s density
  • Localization (centralized or distributed)
  • MAC protocol
  • System and process parameters

Low-cost device proper balance between communication capabilities and signal processing

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Self-organized WSN

Supervisor more capable (in processing and transmission) than other nodes that are typically in sleep mode and periodically commute in rx mode SV triggers

↓

triggered nodes organize thamselves into cluster with random selection of the node Cluster Head

↓

each node sent its sample to its CH No CSP CSP round each CH sends a properly processed information to the SV that estimate the process each CH sends the aggregate information of its cluster to the SV that estimate the process SV estimates the process

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

LEACH-based alg. More information to be transmitted from CH (greater energy consumption) Random election of CH at each trigger with

  • prob. x
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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Statistical analysis of connectivity

Propagation-loss (dB) Readapting theory in [12][13] to WSN: …infinite plane of Poisson distributed nodes with density ρ

The number of nodes triggered by SV is Poisson distr. with mean Self-election of CH

Nch=x Nt ρch=x ρ

Broadcasting to notify CH’s election

Pt=α Psu shadowing ~N(0,σ2)

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Cluster selection (each nonCH node associates itself to the strongest rx CH) Mean cluster dimension, np Poisson distributed with mean Probability to have triggered but isolated nodes

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Energy spent by the CH to tx information

  • no CSP

(np+1) EH/T

  • CSP

m EH/T + energy spent for signal processing Ecsp

Energy spent by a nonCH

EH α /T

Energy budget

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Target Process Estimation

Sample space limited by SV tx range x(s) has finite energy with bandwidth per dimension B and Sequence of spatial samples is an Homogeneous Poisson point process With linear interpolation of sampled version of the target process we estimate

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Normalized Estimation Error

Without CSP

  • ς=1,
  • versampling factor

With CSP

  • the CH estimate the process in its cluster

and re-sample @ Nyquist frequency to tx to the SV only M samples ς=Nch

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Energy Budget

Mean energy/round spent by a node. Without CSP With CSP WSN lifetime

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

By playing with the parameters and CSP different trade-offs between WSN lifetime and process estimation quality are possible, e.g.,

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

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IEIIT-BO/CNR, University of Bologna, Bologna, Italy IWWAN’04, Oulu, Finland - June 1, 2004

Conclusions

We addressed interdependent aspects for WSN design by developing an analytical framework The trade-off between process estimation quality and life-time can evaluated The adoption of CSP can strongly improve the network life-time