Distributed gaussian process regression for mobile sensor networks under localization uncertainty

Sungjoon Choi; Mahdi Jadaliha; Jongeun Choi; Songhwai Oh

doi:10.1109/CDC.2013.6760636

Distributed gaussian process regression for mobile sensor networks under localization uncertainty

Sungjoon Choi, Mahdi Jadaliha, Jongeun Choi, Songhwai Oh

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

6 Citations (Scopus)

Abstract

In this paper, we propose distributed Gaussian process regression for resource-constrained mobile sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi over-relaxation (JOR) and discrete-time average consensus (DAC), can effectively handle localization uncertainty as well as limited communication ranges and computation capabilities of mobile sensor networks. The performance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori solution and the quick-and-dirty solution. We show that the proposed method outperforms the quick-and-dirty solution and achieves an accuracy comparable to the centralized solution.

Original language	English
Title of host publication	2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4766-4771
Number of pages	6
ISBN (Print)	9781467357173
DOIs	https://doi.org/10.1109/CDC.2013.6760636
Publication status	Published - 2013
Externally published	Yes
Event	52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy Duration: 2013 Dec 10 → 2013 Dec 13

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	52nd IEEE Conference on Decision and Control, CDC 2013
Country/Territory	Italy
City	Florence
Period	13/12/10 → 13/12/13

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

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10.1109/CDC.2013.6760636

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Choi, S., Jadaliha, M., Choi, J., & Oh, S. (2013). Distributed gaussian process regression for mobile sensor networks under localization uncertainty. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013 (pp. 4766-4771). Article 6760636 (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2013.6760636

Distributed gaussian process regression for mobile sensor networks under localization uncertainty. / Choi, Sungjoon; Jadaliha, Mahdi; Choi, Jongeun et al.
2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc., 2013. p. 4766-4771 6760636 (Proceedings of the IEEE Conference on Decision and Control).

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

Choi, S, Jadaliha, M, Choi, J & Oh, S 2013, Distributed gaussian process regression for mobile sensor networks under localization uncertainty. in 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013., 6760636, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 4766-4771, 52nd IEEE Conference on Decision and Control, CDC 2013, Florence, Italy, 13/12/10. https://doi.org/10.1109/CDC.2013.6760636

Choi S, Jadaliha M, Choi J, Oh S. Distributed gaussian process regression for mobile sensor networks under localization uncertainty. In 2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013. Institute of Electrical and Electronics Engineers Inc. 2013. p. 4766-4771. 6760636. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC.2013.6760636

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author = "Sungjoon Choi and Mahdi Jadaliha and Jongeun Choi and Songhwai Oh",

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AB - In this paper, we propose distributed Gaussian process regression for resource-constrained mobile sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi over-relaxation (JOR) and discrete-time average consensus (DAC), can effectively handle localization uncertainty as well as limited communication ranges and computation capabilities of mobile sensor networks. The performance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori solution and the quick-and-dirty solution. We show that the proposed method outperforms the quick-and-dirty solution and achieves an accuracy comparable to the centralized solution.

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Distributed gaussian process regression for mobile sensor networks under localization uncertainty

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