Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

Pengfei Zhang; Ido Nevat; Gareth W. Peters; Wolfgang Fruehwirt; Yongchao Huang; Ivonne Anders; Michael Osborne

Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

Pengfei Zhang, Ido Nevat, Gareth W. Peters, Wolfgang Fruehwirt, Yongchao Huang, Ivonne Anders, Michael Osborne

Research output: Contribution to conference › Paper › peer-review

Abstract

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.

Original language	English
Publication status	Published - 12 Nov 2017
Event	NIPS 2017 Workshop on Optimization: 10th NIPS Workshop on Optimization for Machine Learning - Long Beach, Long Beach, United States Duration: 8 Dec 2017 → … Conference number: 10

Workshop

Workshop	NIPS 2017 Workshop on Optimization
Abbreviated title	NIPS
Country/Territory	United States
City	Long Beach
Period	8/12/17 → …

Keywords

stat.ML
eess.SP

Cite this

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title = "Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World",

abstract = " We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner. ",

keywords = "stat.ML, eess.SP",

author = "Pengfei Zhang and Ido Nevat and Peters, {Gareth W.} and Wolfgang Fruehwirt and Yongchao Huang and Ivonne Anders and Michael Osborne",

note = "Presented at NIPS 2017 Workshop on Machine Learning for the Developing World; NIPS 2017 Workshop on Optimization : 10th NIPS Workshop on Optimization for Machine Learning, NIPS ; Conference date: 08-12-2017",

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TY - CONF

T1 - Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

AU - Zhang, Pengfei

AU - Nevat, Ido

AU - Peters, Gareth W.

AU - Fruehwirt, Wolfgang

AU - Huang, Yongchao

AU - Anders, Ivonne

AU - Osborne, Michael

N1 - Conference code: 10

PY - 2017/11/12

Y1 - 2017/11/12

N2 - We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.

AB - We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.

KW - stat.ML

KW - eess.SP

M3 - Paper

T2 - NIPS 2017 Workshop on Optimization

Y2 - 8 December 2017

ER -

Sensor Selection and Random Field Reconstruction for Robust and Cost-effective Heterogeneous Weather Sensor Networks for the Developing World

Abstract

Workshop

Keywords

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