Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models

Po Sen Huang; Mark Hasegawa-Johnson; Wotao Yin; Thomas S. Huang

doi:10.1109/MLSP.2012.6349815

Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models

Po Sen Huang, Mark Hasegawa-Johnson, Wotao Yin, Thomas S. Huang

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

Abstract

Unattended wireless sensor networks have been widely used in many applications. This paper proposes automatic sensor selection methods based on crowdsourcing models in the Opportunistic Sensing framework, with applications to unattended acoustic sensor selection. Precisely, we propose two sensor selection criteria and solve them via greedy algorithm and quadratic assignment. Our proposed method achieves, on average, 5.64% higher accuracy than the traditional approach under sparse reliability conditions.

Original language	English (US)
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
DOIs	https://doi.org/10.1109/MLSP.2012.6349815
State	Published - 2012
Event	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 - Santander, Spain Duration: Sep 23 2012 → Sep 26 2012

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Other

Other	2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Country/Territory	Spain
City	Santander
Period	9/23/12 → 9/26/12

Keywords

Cooperative Sensing
Crowdsourcing models
Opportunistic Sensing
Quadratic Assignment Problem
Unattended Sensor Networks

ASJC Scopus subject areas

Human-Computer Interaction
Signal Processing

Online availability

10.1109/MLSP.2012.6349815

Library availability

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Cite this

Huang, P. S., Hasegawa-Johnson, M., Yin, W., & Huang, T. S. (2012). Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models. In 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012 Article 6349815 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). https://doi.org/10.1109/MLSP.2012.6349815

Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models. / Huang, Po Sen; Hasegawa-Johnson, Mark; Yin, Wotao et al.
2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012. 2012. 6349815 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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

Huang, PS, Hasegawa-Johnson, M, Yin, W & Huang, TS 2012, Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models. in 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012., 6349815, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012, Santander, Spain, 9/23/12. https://doi.org/10.1109/MLSP.2012.6349815

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abstract = "Unattended wireless sensor networks have been widely used in many applications. This paper proposes automatic sensor selection methods based on crowdsourcing models in the Opportunistic Sensing framework, with applications to unattended acoustic sensor selection. Precisely, we propose two sensor selection criteria and solve them via greedy algorithm and quadratic assignment. Our proposed method achieves, on average, 5.64% higher accuracy than the traditional approach under sparse reliability conditions.",

keywords = "Cooperative Sensing, Crowdsourcing models, Opportunistic Sensing, Quadratic Assignment Problem, Unattended Sensor Networks",

author = "Huang, {Po Sen} and Mark Hasegawa-Johnson and Wotao Yin and Huang, {Thomas S.}",

year = "2012",

doi = "10.1109/MLSP.2012.6349815",

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AB - Unattended wireless sensor networks have been widely used in many applications. This paper proposes automatic sensor selection methods based on crowdsourcing models in the Opportunistic Sensing framework, with applications to unattended acoustic sensor selection. Precisely, we propose two sensor selection criteria and solve them via greedy algorithm and quadratic assignment. Our proposed method achieves, on average, 5.64% higher accuracy than the traditional approach under sparse reliability conditions.

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KW - Quadratic Assignment Problem

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BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012

Y2 - 23 September 2012 through 26 September 2012

ER -

Opportunistic sensing: Unattended acoustic sensor selection using crowdsourcing models

Abstract

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Keywords

ASJC Scopus subject areas

Online availability

Library availability

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