Sensor placement in water distribution networks using centrality-guided multi-objective optimisation

Journal of Hydroinformatics, vol. 25, pp. 2291–2303

Abstract

Abstract This paper introduces a multi-objective optimisation approach for the challenging problem of efficient sensor placement in water distribution networks for contamination detection. An important question is how to identify the minimal number of required sensors without losing the capacity to monitor the system as a whole. In this study, we adapted the NSGA-II multi-objective optimisation method by applying centrality mutation. The approach, with two objectives, namely the minimisation of Expected Time of Detection and maximisation of Detection Network Coverage (which computes the number of detected water contamination events), is tested on a moderate-sized benchmark problem (129 nodes). The resulting Pareto front shows that detection network coverage can improve dramatically by deploying only a few sensors (e.g. increase from one sensor to three sensors). However, after reaching a certain number of sensors (e.g. 20 sensors), the effectiveness of further increasing the number of sensors is not apparent. Further, the results confirm that 40–45 sensors (i.e. 31 − 35% of the total number of nodes) will be sufficient for fully monitoring the benchmark network, i.e. for detection of any contaminant intrusion event no matter where it appears in the network.

Authors 5

1. Kegong Diao corresponding

   De Montfort University

   Affiliation as printed

   a Faculty of Computing, Engineering, and Media, De Montfort University, Leicester LE1 9BH, UK

   Faculty of Computing, Engineering, and Media, De Montfort University, Leicester LE1 9BH, UK

2. Michael Emmerich Aachen b Faculty of Science, Leiden Institute of Advanced Computer Science Faculty of Science, Leiden Institute of Advanced Computer Science

   Leiden University · University of Jyväskylä

   Affiliation as printed

   b Faculty of Science, Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, Leiden, CA 2333, The Netherlands

   c Faculty of Information Technology, University of Jyvaskyla, P.O. Box 35 (Agora), Jyvaskyla, FI 40014, Finland

   Faculty of Information Technology, University of Jyvaskyla, P.O. Box 35 (Agora), Jyvaskyla, FI 40014, Finland

   Faculty of Science, Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, Leiden, CA 2333, The Netherlands

3. Jacob Lan Aachen b Faculty of Science, Leiden Institute of Advanced Computer Science Faculty of Science, Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   b Faculty of Science, Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, Leiden, CA 2333, The Netherlands

   Faculty of Science, Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, Leiden, CA 2333, The Netherlands

4. Iryna Yevseyeva

   De Montfort University

   Affiliation as printed

   a Faculty of Computing, Engineering, and Media, De Montfort University, Leicester LE1 9BH, UK

   Faculty of Computing, Engineering, and Media, De Montfort University, Leicester LE1 9BH, UK

5. Robert Sitzenfrei

   Universität Innsbruck

   Affiliation as printed

   d Unit of Environmental Engineering, Department of Infrastructure Engineering, Faculty of Engineering Sciences, University of Innsbruck, Technikerstrasse 13, Innsbruck 6020, Austria

   Unit of Environmental Engineering, Department of Infrastructure Engineering, Faculty of Engineering Sciences, University of Innsbruck, Technikerstrasse 13, Innsbruck 6020, Austria