Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs
conference contribution
posted on 2014-01-01, 00:00authored byW 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
Pagination
167-178
Location
Berlin, Germany
Start date
2014-04-15
End date
2014-04-17
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