Sensor network scheduling for identification of spatially distributed processes

Dariusz Uciński

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 1, page 25-40
  • ISSN: 1641-876X

Abstract

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The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, the Dₛ-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional diffusion process.

How to cite

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Dariusz Uciński. "Sensor network scheduling for identification of spatially distributed processes." International Journal of Applied Mathematics and Computer Science 22.1 (2012): 25-40. <http://eudml.org/doc/208098>.

@article{DariuszUciński2012,
abstract = {The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, the Dₛ-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional diffusion process.},
author = {Dariusz Uciński},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {sensor network; parameter estimation; distributed parameter system; optimum experimental design; Fisher information matrix},
language = {eng},
number = {1},
pages = {25-40},
title = {Sensor network scheduling for identification of spatially distributed processes},
url = {http://eudml.org/doc/208098},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Dariusz Uciński
TI - Sensor network scheduling for identification of spatially distributed processes
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 1
SP - 25
EP - 40
AB - The work treats the problem of fault detection for processes described by partial differential equations as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A simple node activation strategy is discussed for the design of a sensor network deployed in a spatial domain that is supposed to be used while detecting changes in the underlying parameters which govern the process evolution. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset of them can be selected because of the cost constraints. As a suitable performance measure, the Dₛ-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. The problem is then formulated as the determination of the density of gauged sites so as to maximize the adopted design criterion, subject to inequality constraints incorporating a maximum allowable sensor density in a given spatial domain. The search for the optimal solution is performed using a simplicial decomposition algorithm. The use of the proposed approach is illustrated by a numerical example involving sensor selection for a two-dimensional diffusion process.
LA - eng
KW - sensor network; parameter estimation; distributed parameter system; optimum experimental design; Fisher information matrix
UR - http://eudml.org/doc/208098
ER -

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