New formulation and optimization methods for water sensor placement. (February 2016)

• Record Type: Journal Article
• Authors: Zhao, Yue; Schwartz, Rafi; Salomons, Elad; Ostfeld, Avi; Poor, H. Vincent
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 128
• Page End: 136
• Publication Date: 2016-02
• Subjects: Sensor placement -- Water distribution systems -- Optimization -- Water security -- Contamination -- Convex optimization; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.10.030
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events. Highlights: Mixed integer convex programming (MICP) for sensor placement. Branch and bound for finding the global optimum of the MICP. Competitive comparison to the battle of the water sensor networks. Branch and bound algorithm based on convex relaxation and a greedy heuristic. Implementation capability on real sized water networks.