This paper addresses the identification of optimal "sensing spots", within a network for monitoring the spread of "effects"triggered by "events". Many real-world problems fit into this general framework: we focused on the early detection of contamination events in Water Distribution Networks (WDN). We model the sensor placement as a bi-objective optimization problem, aiming at minimizing the mean and standard deviation of detection time over a set of different simulated contamination events and solved using NSGA-II. A problem-specific data structure is proposed enabling a deeper analysis of empirical convergence of the population.
Candelieri, A., Ponti, A., Archetti, F. (2021). Risk aware optimization of water sensor placement. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp.295-296). Association for Computing Machinery, Inc [10.1145/3449726.3459477].
Risk aware optimization of water sensor placement
Candelieri, A;Ponti, A;Archetti, F
2021
Abstract
This paper addresses the identification of optimal "sensing spots", within a network for monitoring the spread of "effects"triggered by "events". Many real-world problems fit into this general framework: we focused on the early detection of contamination events in Water Distribution Networks (WDN). We model the sensor placement as a bi-objective optimization problem, aiming at minimizing the mean and standard deviation of detection time over a set of different simulated contamination events and solved using NSGA-II. A problem-specific data structure is proposed enabling a deeper analysis of empirical convergence of the population.
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