Optimising sensor layouts for direct measurement of discrete variables

X. Rosalind Wang; George Mathews; Don Price; Mikhail Prokopenko

doi:10.1109/SASO.2009.38

Optimising sensor layouts for direct measurement of discrete variables

X. Rosalind Wang, George Mathews, Don Price, Mikhail Prokopenko

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

5 Citations (Scopus)

Abstract

An optimal sensor layout is attained when a limited number of sensors are placed in an area such that the cost of the placement is minimised while the value of the obtained information is maximised. In this paper, we discuss the optimal sensor layout design problem from first principles, show how an existing optimisation criterion (maximum entropy of the measured variables) can be derived, and compare the performance of this criterion with three others that have been reported in the literature for a specific situation for which we have detailed experimental data available. This is achieved by firstly learning a spatial model of the environment using a Bayesian Network, then predicting the expected sensor data in the rest of the space, and finally verifying the predicted results with the experimental measurements. The development of rigorous techniques for optimising sensor layouts is argued to be an essential requirement for reconfigurable and self-adaptive networks.

Original language	English
Title of host publication	SASO 2009: Proceedings of the Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 14 - 18 September 2009, San Francisco, California, USA
Publisher	IEEE
Pages	92-102
Number of pages	11
ISBN (Print)	9780769537948
DOIs	https://doi.org/10.1109/SASO.2009.38
Publication status	Published - 2009
Event	International Conference on Self-Adaptive and Self-Organizing Systems - Duration: 14 Sept 2009 → …

Conference

Conference	International Conference on Self-Adaptive and Self-Organizing Systems
Period	14/09/09 → …

Keywords

Bayesian statistical decision theory
detectors
remote sensing
wireless sensor networks

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10.1109/SASO.2009.38

Cite this

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title = "Optimising sensor layouts for direct measurement of discrete variables",

abstract = "An optimal sensor layout is attained when a limited number of sensors are placed in an area such that the cost of the placement is minimised while the value of the obtained information is maximised. In this paper, we discuss the optimal sensor layout design problem from first principles, show how an existing optimisation criterion (maximum entropy of the measured variables) can be derived, and compare the performance of this criterion with three others that have been reported in the literature for a specific situation for which we have detailed experimental data available. This is achieved by firstly learning a spatial model of the environment using a Bayesian Network, then predicting the expected sensor data in the rest of the space, and finally verifying the predicted results with the experimental measurements. The development of rigorous techniques for optimising sensor layouts is argued to be an essential requirement for reconfigurable and self-adaptive networks.",

keywords = "Bayesian statistical decision theory, detectors, remote sensing, wireless sensor networks",

author = "Wang, {X. Rosalind} and George Mathews and Don Price and Mikhail Prokopenko",

year = "2009",

doi = "10.1109/SASO.2009.38",

language = "English",

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pages = "92--102",

booktitle = "SASO 2009: Proceedings of the Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 14 - 18 September 2009, San Francisco, California, USA",

publisher = "IEEE",

note = "International Conference on Self-Adaptive and Self-Organizing Systems ; Conference date: 14-09-2009",

}

Wang, XR, Mathews, G, Price, D & Prokopenko, M 2009, Optimising sensor layouts for direct measurement of discrete variables. in SASO 2009: Proceedings of the Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 14 - 18 September 2009, San Francisco, California, USA. IEEE, pp. 92-102, International Conference on Self-Adaptive and Self-Organizing Systems, 14/09/09. https://doi.org/10.1109/SASO.2009.38

Optimising sensor layouts for direct measurement of discrete variables. / Wang, X. Rosalind; Mathews, George; Price, Don et al.
SASO 2009: Proceedings of the Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 14 - 18 September 2009, San Francisco, California, USA. IEEE, 2009. p. 92-102.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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AB - An optimal sensor layout is attained when a limited number of sensors are placed in an area such that the cost of the placement is minimised while the value of the obtained information is maximised. In this paper, we discuss the optimal sensor layout design problem from first principles, show how an existing optimisation criterion (maximum entropy of the measured variables) can be derived, and compare the performance of this criterion with three others that have been reported in the literature for a specific situation for which we have detailed experimental data available. This is achieved by firstly learning a spatial model of the environment using a Bayesian Network, then predicting the expected sensor data in the rest of the space, and finally verifying the predicted results with the experimental measurements. The development of rigorous techniques for optimising sensor layouts is argued to be an essential requirement for reconfigurable and self-adaptive networks.

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KW - detectors

KW - remote sensing

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ER -

Optimising sensor layouts for direct measurement of discrete variables

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