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Convex Optimization Approach to Design Sensor Networks using Information Theoretic Measures
  • Arjun M,
  • Nabil Magbool Jan
Arjun M
Indian Institute of Technology Tirupati
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Nabil Magbool Jan
Indian Institute of Technology Tirupati

Corresponding Author:nabil@iittp.ac.in

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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.
28 Mar 2023Submitted to AIChE Journal
28 Mar 2023Review(s) Completed, Editorial Evaluation Pending
28 Mar 2023Submission Checks Completed
28 Mar 2023Assigned to Editor
30 Mar 2023Reviewer(s) Assigned
29 Jul 2023Editorial Decision: Revise Major
24 Aug 20231st Revision Received
31 Aug 2023Review(s) Completed, Editorial Evaluation Pending
31 Aug 2023Submission Checks Completed
31 Aug 2023Assigned to Editor
31 Aug 2023Reviewer(s) Assigned