Gaussian process as a benchmark for optimal sensor placement strategy

Nalika Ulapane; Karthick Thiyagarajan; Sarath Kodagoda

doi:10.1109/SENSORS47087.2021.9639873

Gaussian process as a benchmark for optimal sensor placement strategy

Nalika Ulapane, Karthick Thiyagarajan, Sarath Kodagoda

Western Sydney University

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

4 Citations (Scopus)

Abstract

Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.

Original language	English
Title of host publication	Proceedings of the 20th IEEE Sensors Conference, Oct 31 - Nov 4, 2021, Virtual
Publisher	IEEE
Number of pages	4
ISBN (Print)	9781728195018
DOIs	https://doi.org/10.1109/SENSORS47087.2021.9639873
Publication status	Published - 2021
Event	IEEE International Conference on Sensors - Duration: 1 Jan 2021 → …

Conference

Conference	IEEE International Conference on Sensors
Period	1/01/21 → …

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10.1109/SENSORS47087.2021.9639873

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title = "Gaussian process as a benchmark for optimal sensor placement strategy",

abstract = "Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.",

author = "Nalika Ulapane and Karthick Thiyagarajan and Sarath Kodagoda",

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T1 - Gaussian process as a benchmark for optimal sensor placement strategy

AU - Ulapane, Nalika

AU - Thiyagarajan, Karthick

AU - Kodagoda, Sarath

PY - 2021

Y1 - 2021

N2 - Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.

AB - Optimal sensor placement is an important problem to look at. This problem becomes all the more relevant nowadays due to advancements in infrastructure monitoring robotic technologies including underground sensing. While there are multiple ways to solve optimal sensor placement problems, one of the most generic methods available is Bayesian Optimization and its variants. In this paper, we present a simple benchmark-like formulation for exploiting Gaussian Process uncertainty for sensor placement to measure a scalar field.

UR - https://hdl.handle.net/1959.7/uws:77742

U2 - 10.1109/SENSORS47087.2021.9639873

DO - 10.1109/SENSORS47087.2021.9639873

M3 - Conference Paper

SN - 9781728195018

BT - Proceedings of the 20th IEEE Sensors Conference, Oct 31 - Nov 4, 2021, Virtual

PB - IEEE

T2 - IEEE International Conference on Sensors

Y2 - 1 January 2021

ER -

Gaussian process as a benchmark for optimal sensor placement strategy

Abstract

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