Impact Driven Sensor Placement for Leak Detection in Community Water Networks

Venkateswaran, Praveen; Han, Qing; Eguchi, Ronald T.; Venkatasubramanian, Nalini

doi:10.1109/iccps.2018.00016

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…We argue that a more holistic approach which integrates geospatial and community-level information in the infrastructure design process is required for effective technology deployment. In initial work [8], community structures (e.g. locations of critical infrastructures, public organizations, schools) and demographics are used to characterize "impact" -the degree to which failure events can impact societal processes.…”

Section: Design-time Challenges For Deploymentmentioning

confidence: 99%

Designing community-based intelligent systems for water infrastructure resilience

Venkatasubramanian¹,

Davis

Eguchi

2020

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

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In this paper, we discuss how data-driven approaches using emerging IoT and machine learning based analytics can revolutionize the resilience and ef=iciency of urban water systems. Key challenges in creating a next generation water infrastructure includes issues of how and where to place instruments to gather a wide variety of information useful for improving operational ef=iciencies and for damage detection after major disasters. We discuss how an understanding of deployed infrastructure in diverse geographies and the dynamics of interconnected systems can help design more effective placement of technology solutions. We showcase recent work illustrating how knowledge of network structures and their behavior can help to more effectively instrument and gather operational data and how AI-based approaches utilizing geospatial data more effectively can help to maintain real-time awareness of system states which allows decision makers to more effectively monitor and control their systems. CCS CONCEPTS • Software and its engineering à Software organization and properties à Contextual software domains à Software infrastructure à Middleware

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Section: Design-time Challenges For Deploymentmentioning

confidence: 99%

Designing community-based intelligent systems for water infrastructure resilience

Venkatasubramanian¹,

Davis

Eguchi

2020

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Self Cite

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“…Most of the research on static leak detection systems is focused on the adequate estimation of the signals transmitted from the devices to the central server to detect an actual leak Mohamed et al [2012], Tijani et al [2022]. A few works analyze the optimal placement of a given number of static devices on a finite number of potential placements based on the capability of each of the potential places to detect a leak Venkateswaran et al [2018], or in the use of historic data to place the devices at the more convenient places Casillas et al [2013].…”

Section: Introductionmentioning

confidence: 99%

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

Blanco¹,

Martínez-Antón²

2023

Preprint

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In this paper we provide a mathematical optimization based framework to determine the location of leak detection devices along a network. Assuming that the devices are endowed with a given coverage area, we analyze two different models. The first model aims to minimize the number of devices to be located in order to (fully or partially) cover the volume of the network. In the second model, the number of devices is given, and the goal is to locate them to provide maximal volume coverage. In our models it is not assumed that the devices are located in the network (nodes or edges) but in the entire space, which allow to more flexible coverage. We report the results of applying our models to real-world water supply pipeline urban network, supporting the validity of our models.

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“…Usually, sensor placement focused on leak localization is addressed with an optimization approach from synthetic pressure data obtained by simulation. Some authors have focused on minimizing the number of undetectable leaks [19,20], whereas others reduce the error in the leak location [16,21]. In [22], a min-max optimization algorithm that considers the isolation of the leaks from their signatures obtained through simulation is proposed.…”

Section: Introductionmentioning

confidence: 99%

Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

Santos‐Ruiz

López‐Estrada

Puig

et al. 2022

Sensors

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This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.

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Impact Driven Sensor Placement for Leak Detection in Community Water Networks

Cited by 7 publications

References 32 publications

Designing community-based intelligent systems for water infrastructure resilience

Designing community-based intelligent systems for water infrastructure resilience

Optimal coverage-based placement of static leak detection devices for pipeline water supply networks

Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory

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