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 proceedingConference contribution

5 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 languageEnglish
Title of host publication2013 IEEE 52nd Annual Conference on Decision and Control, CDC 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4766-4771
Number of pages6
ISBN (Print)9781467357173
DOIs
Publication statusPublished - 2013 Jan 1
Event52nd IEEE Conference on Decision and Control, CDC 2013 - Florence, Italy
Duration: 2013 Dec 102013 Dec 13

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other52nd IEEE Conference on Decision and Control, CDC 2013
CountryItaly
CityFlorence
Period13/12/1013/12/13

Fingerprint

Mobile Sensor Networks
Gaussian Process
Sensor networks
Wireless networks
Regression
Uncertainty
Maximum a Posteriori
Distributed Algorithms
Jacobi
Discrete-time
Numerical Simulation
Resources
Parallel algorithms
Range of data
Communication
Computer simulation

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

Cite this

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

Distributed gaussian process regression for mobile sensor networks under localization uncertainty. / Choi, Sungjoon; Jadaliha, Mahdi; Choi, Jongeun; Oh, Songhwai.

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 proceedingConference contribution

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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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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). https://doi.org/10.1109/CDC.2013.6760636