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

Du, Wan, Xing, Zikun, Li, Mo, He, Bingsheng, Chua, Lloyd Hock Chye and Miao, Haiyan 2015, Sensor placement and measurement of wind for water quality studies in urban reservoirs, ACM Transactions on sensor networks, vol. 11, no. 3, pp. 1-27.

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Title Sensor placement and measurement of wind for water quality studies in urban reservoirs
Author(s) Du, Wan
Xing, Zikun
Li, Mo
He, Bingsheng
Chua, Lloyd Hock Chye
Miao, Haiyan
Journal name ACM Transactions on sensor networks
Volume number 11
Issue number 3
Start page 1
End page 27
Total pages 27
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Publication date 2015-02-01
ISSN 1550-4859
1550-4867
Keyword(s) Sensor placement
Spatial prediction
Urban reservoir
Water quality
Wind measurements
Summary We study the 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 impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. 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 in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.
Language eng
Field of Research 059999 Environmental Sciences not elsewhere classified
Socio Economic Objective 970105 Expanding Knowledge in the Environmental Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Persistent URL http://hdl.handle.net/10536/DRO/DU:30074957

Document type: Journal Article
Collection: School of Engineering
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Created: Fri, 14 Aug 2015, 08:46:48 EST

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