scholarly journals Sensor network scheduling for identification of spatially distributed processes

2012 ◽  
Vol 22 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Dariusz Uciński
Keyword(s):  
Sensor Network ◽  
Hypothesis Test ◽  
Information Matrix ◽  
Spatial Domain ◽  
Performance Measure ◽  
Design Criterion ◽  
Network Scheduling ◽  
Cost Constraints ◽  
Distributed Processes ◽  
Spatially Distributed

Sensor network scheduling for identification of spatially distributed processesThe 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, theDs-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.

scholarly journals Configuring A Sensor Network for Fault Detection in Distributed Parameter Systems

2008 ◽  
Vol 18 (4) ◽  
pp. 513-524 ◽  
Author(s):  
Maciej Patan ◽  
Dariusz Uciński
Keyword(s):  
Fault Detection ◽  
Sensor Network ◽  
Hypothesis Test ◽  
Information Matrix ◽  
Performance Measure ◽  
Distributed Parameter Systems ◽  
Projection Algorithm ◽  
Distributed Parameter ◽  
Cost Constraints ◽  
Sensor Locations

Configuring A Sensor Network for Fault Detection in Distributed Parameter SystemsThe problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset can be selected because of the cost constraints. As a suitable performance measure, theDs-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. Then, the solution of a resulting combinatorial problem is determined based on the branch-and-bound method. As its essential part, a relaxed problem is discussed in which the sensor locations are givena prioriand the aim is to determine the associated weights, which quantify the contributions of individual gauged sites. The concavity and differentiability properties of the criterion are established and a gradient projection algorithm is proposed to perform the search for the optimal solution. The delineated approach is illustrated by a numerical example on a sensor network design for a two-dimensional convective diffusion process.

scholarly journals Sensor network design for the estimation of spatially distributed processes

2010 ◽  
Vol 20 (3) ◽  
pp. 459-481 ◽  
Author(s):  
Dariusz Uciński ◽  
Maciej Patan
Keyword(s):  
Network Design ◽  
Sensor Network ◽  
Source Identification ◽  
Design Theory ◽  
Information Matrix ◽  
Identification Problem ◽  
Parameter Estimates ◽  
Sensor Network Design ◽  
Distributed Processes ◽  
Spatially Distributed

Sensor network design for the estimation of spatially distributed processesIn a typical moving contaminating source identification problem, after some type of biological or chemical contamination has occurred, there is a developing cloud of dangerous or toxic material. In order to detect and localize the contamination source, a sensor network can be used. Up to now, however, approaches aiming at guaranteeing a dense region coverage or satisfactory network connectivity have dominated this line of research and abstracted away from the mathematical description of the physical processes underlying the observed phenomena. The present work aims at bridging this gap and meeting the needs created in the context of the source identification problem. We assume that the paths of the moving sources are unknown, but they are sufficiently smooth to be approximated by combinations of given basis functions. This parametrization makes it possible to reduce the source detection and estimation problem to that of parameter identification. In order to estimate the source and medium parameters, the maximum-likelihood estimator is used. Based on a scalar measure of performance defined on the Fisher information matrix related to the unknown parameters, which is commonly used in optimum experimental design theory, the problem is formulated as an optimal control one. From a practical point of view, it is desirable to have the computations dynamic data driven, i.e., the current measurements from the mobile sensors must serve as a basis for the update of parameter estimates and these, in turn, can be used to correct the sensor movements. In the proposed research, an attempt will also be made at applying a nonlinear model-predictive-control-like approach to attack this issue.

scholarly journals Distributed scheduling of measurements in a sensor network for parameter estimation of spatio-temporal systems

2018 ◽  
Vol 28 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Maciej Patan ◽  
Damian Kowalów
Keyword(s):  
Parameter Estimation ◽  
Sensor Network ◽  
A Priori ◽  
Information Matrix ◽  
Sufficient Conditions ◽  
Design Criterion ◽  
Sensor Nodes ◽  
Distributed Scheduling ◽  
Parameter Estimates ◽  
Sensor Locations

AbstractThe main aim of the paper is to develop a distributed algorithm for optimal node activation in a sensor network whose measurements are used for parameter estimation of the underlying distributed parameter system. Given a fixed partition of the observation horizon into a finite number of consecutive intervals, the problem under consideration is to optimize the percentage of the total number of observations spent at given sensor nodes in such a way as to maximize the accuracy of system parameter estimates. To achieve this, the determinant of the Fisher information matrix related to the covariance matrix of the parameter estimates is used as the qualitative design criterion (the so-called D-optimality). The proposed approach converts the measurement scheduling problem to a convex optimization one, in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gaged sites to the total measurement plan. Then, adopting a pairwise communication scheme, a fully distributed procedure for calculating the percentage of observations spent at given sensor locations is developed, which is a major novelty here. Another significant contribution of this work consists in derivation of necessary and sufficient conditions for the optimality of solutions. As a result, a simple and effective computational scheme is obtained which can be implemented without resorting to sophisticated numerical software. The delineated approach is illustrated by simulation examples of a sensor network design for a two-dimensional convective diffusion process.

Multi-hop sensor network scheduling for optimal remote estimation

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109498
Author(s):  
Takuya Iwaki ◽  
Junfeng Wu ◽  
Yuchi Wu ◽  
Henrik Sandberg ◽  
Karl Henrik Johansson
Keyword(s):  
Sensor Network ◽  
Network Scheduling ◽  
Remote Estimation
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