On sensor selection in linked information networks

Charu C. Aggarwal; Amotz Bar-Noy; Simon Shamoun

doi:10.1016/j.comnet.2017.05.024

On sensor selection in linked information networks

Charu C. Aggarwal, Amotz Bar-Noy, Simon Shamoun

Department of Computer Science

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Sensor networks are often redundant by design in order to achieve reliability in information processing. In many cases, the relationships between the different sensors are known a-priori, and can be represented as virtual linkages among the different sensors. These virtual linkages correspond to an information network of sensors, which provides useful external input to the problem of sensor selection. In this paper, we propose the unique approach of using external linkage information in order to improve the efficiency of very large scale sensor selection. We design efficient theoretical models, including a greedy approximation algorithm and an integer programming formulation for sensor selection. Our greedy selection algorithm provides an approximation bound of 1−1/e, where e is the base of the natural logarithm. We show that our approach is much more effective than baseline sampling strategies. We present experimental results that illustrate the effectiveness and efficiency of our approach.

Original language	English
Pages (from-to)	100-113
Number of pages	14
Journal	Computer Networks
Volume	126
DOIs	https://doi.org/10.1016/j.comnet.2017.05.024
State	Published - 24 Oct 2017

Bibliographical note

Publisher Copyright:
© 2017

Funding

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu). We thank Dror Rawitz and Matthew P. Johnson for their insights to the analysis of the greedy algorithm.

Funders	Funder number
EU-FP6
Army Research Laboratory	W911NF-09-2-0053

Keywords

Greedy algorithm
Information networks
Sensor selection
Stream prediction

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10.1016/j.comnet.2017.05.024

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On sensor selection in linked information networks

Abstract

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Keywords

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