DUMORA, Christophe; AUBER, David; BIGOT, Jérémie; COUALLIER, Vincent; LECLERC, Cyril

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http://creativecommons.org/licenses/by-nc-nd/

DUMORA, Christophe
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Institut de Mathématiques de Bordeaux [IMB]

AUBER, David
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

BIGOT, Jérémie
Institut de Mathématiques de Bordeaux [IMB]

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Communication dans un congrès

Ce document a été publié dans

Multi Conference on Computer Science and Information Systems, 2018-07-17, Madrid. 2018-07-17p. 273

Résumé en anglais

The aim of this paper is to present how data collected from a water distribution network (WDN) can be used to reconstruct flow rate and flow direction all over the network to enhance knowledge and detection of unforeseen events. The methodological approach consists in modeling the WDN and all available sensor data related to the management of such a network in the form of a flow network graph G = (V, E, s, t, c), with V a set of nodes, E a set of edges whose elements are ordered pairs of distinct nodes, s a source node, t a sink node and c a capacity function on edges. Our objective is to reconstruct a real-valued function f(u,v):VxV => ℝ on all the edges E in VxV from partial observations on a small number of nodes V = {1, ..., n}. This reconstruction method consists in a data-driven Ford-Fulkerson maximum-flow problem in a multi-source, multi-sink context using a constrained bidirectional breadth-first search based on Edmonds-Karp method. The innovative approach is its application in the context of smart cities to operate from sensor data, structural data from a geographical information system (GIS) and consumption estimates.< Réduire

Mots clés en anglais

Graph theory

maximum flow problem

data driven

sensors

Internet of Things

water distribution network

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Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network

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