A Novel Information Theoretic Measure Based Sensor Network Design Approach for Steady State Linear Data Reconciliation

Prakash, Om; Bhushan, Mani

doi:10.1016/j.ifacol.2020.12.1750

Cited by 4 publications

(9 citation statements)

References 10 publications

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“…It is known that the error covariance matrix Σ e may not be invertible due to degeneracy issues, as reported by Prakash and Bhushan. 26 This poses a challenge in computing the entropy of the estimation error distribution. To circumvent this degeneracy issue, we recast the optimization problem as minimizing the entropy of estimation errors in primary variables.…”

Section: Entropy-based Optimal Sensor Selectionmentioning

confidence: 99%

“…In light of the degeneracy issues highlighted by Prakash and Bhushan, 26 we rewrite the formulation in terms of the estimation errors in primary variables. The variance-covariance matrix of estimation errors in primary variables is given by:…”

Section: Sensor Network Design Using Forward Kullback-leibler Divergencementioning

confidence: 99%

“…25 By utilizing mutual information, a sensor network can be selected that provides the most comprehensive information about the target state, thereby optimizing data transfer and reducing waste. Very recently, Prakash and Bhushan 26 recently presented a reverse Kullback-Leibler (KL) divergence for designing sensor networks from a data reconciliation perspective for non-Gaussian noise distributions.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the previous work on sensor selection using reverse KL divergence, 26 we assume that the estimation errors can be characterized using multivariate Gaussian distributions. Moreover, we use symmetric KL divergence to quantify the process variables' estimation accuracy in a linear steady-state data reconciliation problem.…”

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to asymmetric reverse KL divergence used by Prakash and Bhushan. 26 Further, they obtained the optimal sensor network through enumeration.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Convex Optimization Approach to Design Sensor Networks using Information Theoretic Measures

Arjun

Jan

2023

Preprint

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Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, this paper aims at developing convex optimization formulations for designing the optimal sensor network using information-theoretic measures in linear steady-state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback-Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic-based optimal sensor selection problems are shown to be integer semidefinite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

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Section: Entropy-based Optimal Sensor Selectionmentioning

confidence: 99%

Section: Sensor Network Design Using Forward Kullback-leibler Divergencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This is in contrast to asymmetric reverse KL divergence used by Prakash and Bhushan. 26 Further, they obtained the optimal sensor network through enumeration.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Convex Optimization Approach to Design Sensor Networks using Information Theoretic Measures

Arjun

Jan

2023

Preprint

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Convex optimization approach to design sensor networks using information theoretic measures

Magbool Jan

2023

AIChE Journal

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Accurate and precise estimation of process variables is key to effective process monitoring. The estimation accuracy depends on the choice of the sensor network. Therefore, the article aims at developing convex optimization formulations for designing the optimal sensor network using information‐theoretic measures in linear steady‐state data reconciliation. To this end, the estimation errors are characterized by a multivariate Gaussian distribution, and thus the analytical form for entropy and Kullback–Leibler divergences (forward, reverse, and symmetric) of estimation errors can be obtained to formulate the optimal sensor network design. The proposed information theoretic‐based optimal sensor selection problems are shown to be integer semi‐definite programming problems where the relaxation of binary decision variables results in solving a convex optimization problem. Thus, we use a branch and bound method to obtain a globally optimal sensor network design. Demonstrative case studies are presented to illustrate the efficacy of the proposed optimal sensor selection formulations.

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Cumulative residual Kullback-Leibler divergence based sensor placement using reliability criteria

Prakash

Bhushan²

2022

Computers & Chemical Engineering

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A Novel Information Theoretic Measure Based Sensor Network Design Approach for Steady State Linear Data Reconciliation

Cited by 4 publications

References 10 publications

Convex Optimization Approach to Design Sensor Networks using Information Theoretic Measures

Convex Optimization Approach to Design Sensor Networks using Information Theoretic Measures

Convex optimization approach to design sensor networks using information theoretic measures

Cumulative residual Kullback-Leibler divergence based sensor placement using reliability criteria

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