Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets

Kushal Mukherjee; Shalabh Gupta; Asok Ray; Thomas A. Wettergren

doi:10.1109/TSMCB.2010.2092763

Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets

Kushal Mukherjee
, Shalabh Gupta
, Asok Ray
, Thomas A. Wettergren

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

31 Scopus citations

Abstract

This paper presents a statistical-mechanics-inspired procedure for optimization of the sensor field configuration to detect mobile targets. The key idea is to capture the low-dimensional behavior of the sensor field configurations across the Pareto front in a multiobjective scenario for optimal sensor deployment, where the nondominated points are concentrated within a small region of the large-dimensional decision space. The sensor distribution is constructed using location-dependent energy-like functions and intensive temperature-like parameters in the sense of statistical mechanics. This low-dimensional representation is shown to permit rapid optimization of the sensor field distribution on a high-fidelity simulation test bed of distributed sensor networks.

Original language	English (US)
Article number	5669356
Pages (from-to)	783-791
Number of pages	9
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	41
Issue number	3
DOIs	https://doi.org/10.1109/TSMCB.2010.2092763
State	Published - Jun 2011

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMCB.2010.2092763

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title = "Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets",

abstract = "This paper presents a statistical-mechanics-inspired procedure for optimization of the sensor field configuration to detect mobile targets. The key idea is to capture the low-dimensional behavior of the sensor field configurations across the Pareto front in a multiobjective scenario for optimal sensor deployment, where the nondominated points are concentrated within a small region of the large-dimensional decision space. The sensor distribution is constructed using location-dependent energy-like functions and intensive temperature-like parameters in the sense of statistical mechanics. This low-dimensional representation is shown to permit rapid optimization of the sensor field distribution on a high-fidelity simulation test bed of distributed sensor networks.",

author = "Kushal Mukherjee and Shalabh Gupta and Asok Ray and Wettergren, \{Thomas A.\}",

note = "Funding Information: Manuscript received April 12, 2010; revised October 18, 2010; accepted November 9, 2010. Date of publication December 17, 2010; date of current version May 18, 2011. This work was supported in part by the U.S. Office of Naval Research under Grant N00014-09-1-0688 and in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant W911NF-07-1-0376. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. This paper was recommended by Associate Editor S. Ferrari.",

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doi = "10.1109/TSMCB.2010.2092763",

language = "English (US)",

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AU - Ray, Asok

AU - Wettergren, Thomas A.

N1 - Funding Information: Manuscript received April 12, 2010; revised October 18, 2010; accepted November 9, 2010. Date of publication December 17, 2010; date of current version May 18, 2011. This work was supported in part by the U.S. Office of Naval Research under Grant N00014-09-1-0688 and in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant W911NF-07-1-0376. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies. This paper was recommended by Associate Editor S. Ferrari.

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AB - This paper presents a statistical-mechanics-inspired procedure for optimization of the sensor field configuration to detect mobile targets. The key idea is to capture the low-dimensional behavior of the sensor field configurations across the Pareto front in a multiobjective scenario for optimal sensor deployment, where the nondominated points are concentrated within a small region of the large-dimensional decision space. The sensor distribution is constructed using location-dependent energy-like functions and intensive temperature-like parameters in the sense of statistical mechanics. This low-dimensional representation is shown to permit rapid optimization of the sensor field distribution on a high-fidelity simulation test bed of distributed sensor networks.

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Statistical-mechanics-inspired optimization of sensor field configuration for detection of mobile targets

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