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Conference Paper: Evolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance

TitleEvolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance
Authors
Issue Date26-Aug-2024
Abstract

Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing.
However, this problem is computationally challenging to solve, especially for massive sewage networks having complicated topologies. In this paper, we formulate this problem as a multi-objective
optimization problem to consider the conflicting objectives and put forward a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks.
The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong. The experiments on small-scale synthetic networks demonstrate a
consistent efficiency improvement with reasonable optimization performance and the real-world application shows that our method is effective in generating optimal sensor placement plans to guide
policy-making. 


Persistent Identifierhttp://hdl.handle.net/10722/351776

 

DC FieldValueLanguage
dc.contributor.authorWang, Sunyu-
dc.contributor.authorXia, Yutong-
dc.contributor.authorChen, Huanfa-
dc.contributor.authorTong, Xinyi-
dc.contributor.authorZhou, Yulun-
dc.date.accessioned2024-11-28T00:35:10Z-
dc.date.available2024-11-28T00:35:10Z-
dc.date.issued2024-08-26-
dc.identifier.urihttp://hdl.handle.net/10722/351776-
dc.description.abstract<p>Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing.<br>However, this problem is computationally challenging to solve, especially for massive sewage networks having complicated topologies. In this paper, we formulate this problem as a multi-objective<br>optimization problem to consider the conflicting objectives and put forward a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks.<br>The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong. The experiments on small-scale synthetic networks demonstrate a<br>consistent efficiency improvement with reasonable optimization performance and the real-world application shows that our method is effective in generating optimal sensor placement plans to guide<br>policy-making. <br></p>-
dc.languageeng-
dc.relation.ispartof13th International Workshop on Urban Computing (26/08/2024-26/08/2024, Barcelona)-
dc.titleEvolutionary Greedy Algorithm for Optimal Sensor Placement Problem in Urban Sewage Surveillance-
dc.typeConference_Paper-

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