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Article: Cost-effective sensor placement optimization for large-scale urban sewage surveillance

TitleCost-effective sensor placement optimization for large-scale urban sewage surveillance
Authors
KeywordsMulti-objective genetic algorithm
Network-based spatial optimization
Optimal sensor placement
Pandemic control
Sewage surveillance
Issue Date1-Apr-2024
PublisherElsevier
Citation
Sustainable Cities and Society, 2024, v. 103 How to Cite?
Abstract

Early pandemic outbreak detection in cities is a crucial but challenging task. Complementary to the costly massive individual testing, urban sewage surveillance offers a rare, cost-effective solution for large-scale monitoring of pandemic spread in cities with minimal interference to people’s lives. One emerging question is how to derive a cost-effective sensor placement plan in city-scale sewage networks having complicated topologies. Inspired by remote sensing, we first provide a general multi-objective formulation of the optimal sensor placement problem on directed networks. Then, we introduce a connectivity-based objective evaluation approach and embed it into an NSGA-II algorithm to enable efficient optimization on large-scale directed graphs. The effectiveness of the proposed method is verified on a real-world sewage network in Hong Kong serving more than 500,000 urban residents. Results show that the proposed method efficiently generated optimal sensor placement plans on city-scale networks. Optimized sensor placement plans outperformed human placement heuristics by a significant margin of 102%, highlighting the necessity for data-driven decision support for large-scale urban sensing. Methodologically, this study provides a benchmark problem and datasets for network-based spatial optimization studies. Codes and datasets developed in this study are open-sourced to support future research in a real-world scenario.


Persistent Identifierhttp://hdl.handle.net/10722/342194
ISSN
2023 Impact Factor: 10.5
2023 SCImago Journal Rankings: 2.545
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Sunyu-
dc.contributor.authorXu, Ke-
dc.contributor.authorZhou, Yulun-
dc.date.accessioned2024-04-17T03:49:55Z-
dc.date.available2024-04-17T03:49:55Z-
dc.date.issued2024-04-01-
dc.identifier.citationSustainable Cities and Society, 2024, v. 103-
dc.identifier.issn2210-6707-
dc.identifier.urihttp://hdl.handle.net/10722/342194-
dc.description.abstract<p>Early pandemic outbreak detection in cities is a crucial but challenging task. Complementary to the costly massive individual testing, urban sewage surveillance offers a rare, cost-effective solution for large-scale monitoring of pandemic spread in cities with minimal interference to people’s lives. One emerging question is how to derive a cost-effective sensor placement plan in city-scale sewage networks having complicated topologies. Inspired by remote sensing, we first provide a general multi-objective formulation of the optimal sensor placement problem on directed networks. Then, we introduce a connectivity-based objective evaluation approach and embed it into an NSGA-II algorithm to enable efficient optimization on large-scale directed graphs. The effectiveness of the proposed method is verified on a real-world sewage network in Hong Kong serving more than 500,000 urban residents. Results show that the proposed method efficiently generated optimal sensor placement plans on city-scale networks. Optimized sensor placement plans outperformed human placement heuristics by a significant margin of 102%, highlighting the necessity for data-driven decision support for large-scale urban sensing. Methodologically, this study provides a benchmark problem and datasets for network-based spatial optimization studies. Codes and datasets developed in this study are open-sourced to support future research in a real-world scenario.<br></p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofSustainable Cities and Society-
dc.subjectMulti-objective genetic algorithm-
dc.subjectNetwork-based spatial optimization-
dc.subjectOptimal sensor placement-
dc.subjectPandemic control-
dc.subjectSewage surveillance-
dc.titleCost-effective sensor placement optimization for large-scale urban sewage surveillance-
dc.typeArticle-
dc.identifier.doi10.1016/j.scs.2024.105250-
dc.identifier.scopuseid_2-s2.0-85185280700-
dc.identifier.volume103-
dc.identifier.eissn2210-6715-
dc.identifier.isiWOS:001186860000001-
dc.identifier.issnl2210-6707-

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