D-optimal design of a monitoring network for parameter estimation of distributed systems

Uciński, Dariusz; Patan, Maciej

doi:10.1007/s10898-007-9139-z

Cited by 85 publications

(51 citation statements)

References 63 publications

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“…Since selecting the best subset of sites to locate the sensors constitutes an inherently discrete large-scale resource allocation problem whose solution may be prohibitively time-consuming, an efficient guided search algorithm based on the branch-and-bound method was developed, which implicitly enumerates all the feasible sensor configurations, using relaxed optimization problems that involve no integer constraints (cf. also Uciński and Patan, 2007). …”

Section: 3mentioning

confidence: 87%

Sensor network scheduling for identification of spatially distributed processes

Uciński

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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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.

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Section: 3mentioning

confidence: 87%

Sensor network scheduling for identification of spatially distributed processes

Uciński

2010

2010 Conference on Control and Fault-Tolerant Systems (SysTol)

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“…A comprehensive treatment of both theoretical and algorithmic aspects of the resulting sensor location strategies is contained in the monograph by Uciń-ski (2005). The potential of the approach for generalizations was exploited, e.g., in the work by Uciński and Patan (2007), describing the setting in which a large number of possible sites at which to locate a sensor are given, but cost constraints allow only some proper subset of them to be selected. The search for the optimal solution was performed using the branch-and-bound method in which an extremely simple and efficient technique was employed to produce an upper bound to the maximum objective function.…”

Section: Problem Formulation In Terms Ofmentioning

confidence: 99%

Sensor network design for the estimation of spatially distributed processes

Uciński¹,

Patan²

2010

International Journal of Applied Mathematics and Computer Science

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In 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.

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“…In addition, sensor selection can be discussed within the framework of optimal estimation, where the estimation error is normally used as the cost function of the optimization. In previous works [19]- [21]], the problem of sensors selection is formulated as a constrained 0-1 integer programming problem, where the estimation error is minimized.…”

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confidence: 99%

Routing Optimization and Secure Target Tracking in Distributed Wireless Sensor Networks

Mansouri

Khoukhi

Snoussi

et al. 2012

Wireless Sensor Networks and Energy Efficiency

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Due to the limited energy supplies of nodes in wireless sensor networks (WSN), optimizing their design under energy constraints, reducing their communication costs, and securing their aggregated data are of paramount importance. To this goal and in order to efficiently solve the problem of target tracking in WSN with quantized measurements, we propose to jointly estimate the target position, the relay location, select the secure sensor nodes and the best communication path. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a single sensor and a cluster head (CH). Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Thirdly, we select the best communication path between the candidate sensor and the CH. Then, we estimate jointly the target position and the relay location using Quantized Variational Filtering (QVF) algorithm. The selection of candidate sensors is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation, while the best communication path is selected as well as the highest signal-to-noise ratio (SNR) at the CH. The efficiency of the proposed method is validated by extensive simulations in target tracking for wireless sensor networks. Index TermsWireless sensor networks, target tracking, quantized variational filtering, relay localization, best December 28, 2010 DRAFT 2 communication path, malicious sensor.

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D-optimal design of a monitoring network for parameter estimation of distributed systems

Cited by 85 publications

References 63 publications

Sensor network scheduling for identification of spatially distributed processes

Sensor network scheduling for identification of spatially distributed processes

Sensor network design for the estimation of spatially distributed processes

Routing Optimization and Secure Target Tracking in Distributed Wireless Sensor Networks

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