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Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

conference contribution
posted on 01.01.2014, 00:00 authored by W Du, Z Xing, M Li, B He, Lloyd ChuaLloyd Chua, H Miao
We collaborate with environmental scientists to study the hydrodynamics and water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

History

Event

IEEE Computer Society. Symposium (13th : 2014 : Berlin, Germany)

Series

IEEE Computer Society Symposium

Pagination

167 - 178

Publisher

Institute of Electrical and Electronics Engineers

Location

Berlin, Germany

Place of publication

Piscataway, N.J.

Start date

15/04/2014

End date

17/04/2014

ISBN-13

9781479931460

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2014, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IPSN' 14 : Proceedings of the 13th International Symposium on Information Processing in Sensor Networks