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.
|Number of pages||27|
|Journal||ACM Transactions on Sensor Networks|
|Early online date||1 Feb 2015|
|Publication status||Published - 1 May 2015|
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- Department of Engineering and Technology - Senior Lecturer in Automotive Engineering
- School of Computing and Engineering
- Centre for Efficiency and Performance Engineering - Member
- Centre for Thermofluids, Energy Systems and High-Performance Computing - Member