Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems

Suresh, Mahima Agumbe; Smith, Lidia; Rasekh, Amin; Stoleru, Radu; Banks, M. Katherine; Shihada, Basem

doi:10.1109/aina.2014.83

Cited by 10 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on that, they designed a solution called FBSM, which would deploy multiple mobile nodes and utilize beacon nodes for event detection. In [19], they further formulated the maximization of minimum pipeline monitor probability problem as an integer linear programming problem, and the maximization of mean pipeline monitor prob-ability as an integer non-linear programming one. Solutions are also proposed accordingly.…”

Section: A Manual Data Collectionmentioning

confidence: 99%

Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors

Liang

Xue

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Urban water distribution networks (WDNs) are usually threatened by leakage, reflux, infiltration and internal pollution. To ensure the safety of water supply, it is essential to properly monitor the WDNs. However, it is not easy to design a fast, fine-grained and comprehensive monitoring solution under limited cost, due to the large scale and high complexity of WDNs. In recent years, mobile sensor networks have been widely utilized to monitor WDNs with data uploaded by static nodes or receiver nodes. However, existing solutions haven't addressed the impact of duplicated uploading caused by the topology of WDNs. Hence, the coverage rate is unsatisfactory. In addition, the mobile sensor nodes could be potentially far away from the receiver nodes, which might introduce long data acquisition delay. In this work, we propose a low delay and high coverage WDNs monitoring solution, with the constraint of limited number of mobile and receiver nodes. The proposed solution can find the optimal deployment strategy of mobile and receiver nodes by constructing a probability dependent model between mobile and receiver nodes, and coordinating the trade-off between low-delay and high-coverage under multi-objective optimization model. We also analyzed the theoretical lower bound of the algorithm, by utilizing the submodular function characteristics of the objective. Extensive simulation proved that the proposed algorithm can obtain higher coverage with lower delay.

show abstract

Section: A Manual Data Collectionmentioning

confidence: 99%

Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors

Liang

Xue

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Sensor placement 175 to achieve a required sensing coverage is seen as 176 requirement for localization and beacon placement 177 problem is solved independent of sensor placement 178 under the assumption that sensing coverage is en-179 sured. The problem of sensor placement that ob-180 tains the optimal placement of sensors, given an 181 upper bound on the total number of sensors has 182 been tackled previously [27]. A simulator called 183 FlowSim is developed and used for the evaluation 184 of the system.…”

Section: Our Contributions 162mentioning

confidence: 99%

“…We use the same definition of "traversal probability matrix" as in [27], [24], [25], and [26] For the example in Figure 1, some elements of P are: p 9,10 = 1, p 9,11 = 1 (since q 9,10 = 1, q 10,11 = 1), [28].…”

Section: Sensor Mobility Modelmentioning

confidence: 99%

See 1 more Smart Citation

Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks

Gong

Suresh

Smith

et al. 2016

Environmental Modelling & Software

View full text Add to dashboard Cite

Leak and backflow detections are essential aspects of water distribution systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, and collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time.

show abstract

“…It is estimated by the world bank that 48.6 billion cubic meters of water are lost every year. In the US, it is estimated that there are 237 600 water main breaks per year, leading to $2.8 billion lost in yearly revenue . Furthermore, severe water leakage may also cause ground instabilities, thus lead to collapse of roads, buildings, and subsequent secondary economic losses in the forms of damages to transportation and communication lines.…”

Section: Introductionmentioning

confidence: 99%

Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

Shu

Yan

et al. 2018

Softw Pract Exp

View full text Add to dashboard Cite

Burst or leakage in drinkable water distribution system has occurred frequently in recent years, causing severe damages, economic loss, and long-lasting society impact. A viable solution is to use agile inline mobile sensors to detect and so as to mitigate the burst or leakage. Distinguishing from online fixed sensors, mobile sensors can swim freely along the piles in water distribution network, thus giving a more precise detection. To combat the low power, low computation, and low communication capability of mobile sensors, the newly emerged fog computing provides a promising means to gather and preprocess the sensing data. In practice, due to the budget limitation, we can deploy a limited number of sensors and fog nodes in the system. This introduces a challenging problem on how to deploy them in the system, ie, sensor and fog node placement. We first formulate mobile sensor placement (MSP) as a path cover problem and prove it as NP-complete, and then we propose a customized genetic algorithm and a mixed greedy algorithm to solve MSP and fog node placement, respectively. The correctness and efficiency of the proposed algorithm are illustrated by a comprehensive experiment. Moreover, some critical factors, eg, sensor battery lifetime and movement pattern, are all extensively investigated and the results show the coverage ratio is sensitive to these factors.

show abstract

Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems

Cited by 10 publications

References 12 publications

Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors

Low-Delay and High-Coverage Water Distribution Networks Monitoring Using Mobile Sensors

Mobile sensor networks for optimal leak and backflow detection and localization in municipal water networks

Inline wireless mobile sensors and fog nodes placement for leakage detection in water distribution systems

Contact Info

Product

Resources

About