Sensor placement for field estimation via Poisson disk sampling

Sijia Liu; Nianxia Cao; Pramod Kumar Varshney

doi:10.1109/GlobalSIP.2016.7905896

Sensor placement for field estimation via Poisson disk sampling

Sijia Liu, Nianxia Cao, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.

Original language	English (US)
Title of host publication	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	520-524
Number of pages	5
ISBN (Electronic)	9781509045457
DOIs	https://doi.org/10.1109/GlobalSIP.2016.7905896
State	Published - Apr 19 2017
Event	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Washington, United States Duration: Dec 7 2016 → Dec 9 2016

Other

Other	2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Country	United States
City	Washington
Period	12/7/16 → 12/9/16

Keywords

Alternating direction method of multipliers
Field estimation
Poisson disk sampling
Sensor placement
Sparsity

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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Sensor placement for field estimation via Poisson disk sampling. / Liu, Sijia; Cao, Nianxia; Varshney, Pramod Kumar.

2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 520-524 7905896.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, S, Cao, N & Varshney, PK 2017, Sensor placement for field estimation via Poisson disk sampling. in 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings., 7905896, Institute of Electrical and Electronics Engineers Inc., pp. 520-524, 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016, Washington, United States, 12/7/16. https://doi.org/10.1109/GlobalSIP.2016.7905896

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abstract = "In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.",

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N2 - In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.

AB - In this paper, we study the problem of sensor placement for field estimation, where the best subset of potential sensor locations is chosen to strike a balance between the number of deployed sensors and estimation accuracy. Potential sensor locations are generated by sampling a continuous field of interest. We investigate the impact of sampling strategies on sensor placement, and show that compared to other commonly-used sampling strategies, the Poisson disk sampling method can provide a more accurate (discretized) representation of the random field. Based on the sampled locations, we propose an efficient placement algorithm that scales gracefully with problem size using the alternating direction method of multipliers and the accelerated gradient descent method. Numerical results are provided to demonstrate the effectiveness of our approach for sensor placement.

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Sensor placement for field estimation via Poisson disk sampling

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Keywords

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