Imperial College London
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ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
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Contact

 

+44 (0)20 7594 8375j.mccann Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

260ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wei:2020:10.1109/tnse.2019.2941834,
author = {Wei, Z and Pagani, A and Fu, G and Guymer, I and Chen, W and McCann, J and Guo, W},
doi = {10.1109/tnse.2019.2941834},
journal = {IEEE Transactions on Network Science and Engineering},
pages = {1570--1582},
title = {Optimal sampling of water distribution network dynamics using graph fourier transform},
url = {http://dx.doi.org/10.1109/tnse.2019.2941834},
volume = {7},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Water distribution networks are critical infrastructures under threat from the accidental or intentional release of contaminants. Large-scale data collection is vital for digital twin modelling, but remains challenging in underground spaces over vast areas. Therefore, inferring the contaminant spread process with minimal sensor data is important. Existing sensor deployment optimisation approaches use scenario-based numerical optimisation, but suffer from scalability issues and lack performance guarantees. Analytical graph theoretic approaches link complex network topology (e.g. Laplacian spectra) to optimal sensing locations, but neglect the complex fluid dynamics. Alternative data-driven approaches such as compressed sensing offer limited sample node reduction. In this work, we introduce a novel data-driven Graph Fourier Transform that exploits the low-rank property of networked dynamics to optimally sample WDNs. The proposed GFT guarantees error free recovery of network dynamics and offers attractive compression and scaling improvements over existing numerical optimisation, compressed sensing, and graph theoretic approaches. By testing on 100 different contaminant propagation data sets, the proposed scheme shows that, on average, with nearly 30% of the junctions monitored, we are able to fully recover the networked dynamics. The framework is useful for other monitoring applications of WDNs and can be applied to a variety of infrastructure sensing for digital twin modelling.
AU  - Wei,Z
AU  - Pagani,A
AU  - Fu,G
AU  - Guymer,I
AU  - Chen,W
AU  - McCann,J
AU  - Guo,W
DO  - 10.1109/tnse.2019.2941834
EP  - 1582
PY  - 2020///
SN  - 2327-4697
SP  - 1570
TI  - Optimal sampling of water distribution network dynamics using graph fourier transform
T2  - IEEE Transactions on Network Science and Engineering
UR  - http://dx.doi.org/10.1109/tnse.2019.2941834
UR  - https://ieeexplore.ieee.org/document/8839864
UR  - http://hdl.handle.net/10044/1/83225
VL  - 7
ER  -
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