SewerSnort: A Drifting Sensor for In-situ Sewer Gas Monitoring

Kim, Ji‐Hyoung; Lim, Jung Soo; Friedman, Jonathan; Lee, Uichin; Vieira, Luiz F. M.; Rosso, Diego; Gerla, Mário; Srivastava, Mani

doi:10.1109/sahcn.2009.5168971

Cited by 34 publications

(15 citation statements)

References 21 publications

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“…ii) Sink nodes are deployed only on manholes of the pipeline so it is difficult to find the exact leakage place. SewerSnort [13] i) All the sensors drift inside the sewerage pipelines have specific coverage area.…”

Section: Requirements and Limitationsmentioning

confidence: 99%

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

et al. 2016

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Underwater Wireless Linear Sensor Networks (UW-LSNs Keywords: scalability, heterogeneity, nodes deployment algorithm, underwater pipeline monitoringCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionUnderwater environments do not remain feasible for human operators due to the harsh underwater activities, high water pressure, hazardous underwater creatures, vast areas for exploration and lack of high frequency signals propagation [1]. Due to the underwater acoustic communication, the signal propagation speed is decreased to the speed of sound. Although in underwater, sound waves travel longer and faster than the air but still they are five times slower than the electromagnetic waves [2]. Most of deployment algorithms and routing protocols do not remain suitable for such environments as they necessitate random nodes deployment, joint topology, 2D area, higher data rate, and outcomes in large end-to-end delays [3]. Although significant areas of improvement are highly required to be there in UW-LSN monitoring techniques like scalable nodes deployment, distribution of large network, minimization of the delay in communication and efficient data deliveries to the sink; but the available data rates for the long range underwater applications is very low and not feasible for the real time communication. In short, it seems to be hard to increase the data rate for the long range underwater communication where scalable and efficient nodes deployments in distributed topology are highly supportive in this regard.In fact, UW-LSNs and terrestrial sensor networks have many common properties including deployment of large number of nodes and energy constraints; but UW-LSN stays unique in many aspects from the terrestrial sensor networks [4]. Firstly, most of the times homogenous types of sensor nodes are deployed randomly that are not scalable for extension of underwater pipelines monitoring coverage. Secondly, UW-LSN requires special deployment algorithms that assign proper nodes positions in 3D dynamic underwater environment. Thirdly, sensors are linearly deployed to maintain the linear topology and distributed topology network [5] that divides the pipeline length into sub-zones and ranges of heterogeneous sensors.

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Section: Requirements and Limitationsmentioning

confidence: 99%

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

et al. 2016

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“…Several systems have been proposed in the literature to monitor pipelines using mobile sensors, e.g., [5,18,24,25]. The basic idea is to use mobile drifting sensors to (a) monitor the pipeline, the liquid flowing in the pipeline, and the chemicals generated inside the pipeline; (b) provide close monitoring of the different areas of the pipeline; and (c) generate and transmit event-related data when it observes failing statuses (through beacons, for example).…”

Section: Sensor-based Pipeline Monitoring Systemsmentioning

confidence: 99%

“…In recent years, sensor networks have witnessed a rapid growth due to the development of inexpensive sensing devices and communication technologies and are used for several applications *Correspondence: sharma@cs.kent.edu 2 Department of Computer Science, Kent State University, 268 Mathematics and Computer Science Building, Kent, OH 44242, USA Full list of author information is available at the end of the article such as agriculture, military, health care, and pipeline monitoring. Several sensor network-based pipeline monitoring systems have been proposed in the literature, e.g., [1,[3][4][5][6][7]. However, these systems are passive in the sense that they do not perform corrective activities and only report on incidents.…”

Section: Introductionmentioning

confidence: 99%

Autonomous pipeline monitoring and maintenance system: a RFID-based approach

Kim

Sharma

Boudriga

et al. 2015

J Wireless Com Network

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Pipeline networks are one of the key infrastructures of our modern life. Proactive monitoring and frequent inspection of pipeline networks are very important for sustaining their safe and efficient functionalities. Existing monitoring and maintenance approaches are costly and inefficient because pipelines can be installed in large scale and in an inaccessible and hazardous environment. To overcome these challenges, we propose a novel Radio Frequency IDentification (RFID)-based Autonomous Maintenance system for Pipelines, called RAMP, which combines robotic, sensing, and RFID technologies for efficient and accurate inspection, corrective reparation, and precise geo-location information. RAMP can provide not only economical and scalable remedy but also safe and customizable solution. RAMP also allows proactive and corrective monitoring and maintenance of pipelines. One prominent advantage of RAMP is that it can be applied to a large variety of pipeline systems including water, sewer, and gas pipelines. Simulation results demonstrate the feasibility and superior performance of RAMP in comparison to the existing pipeline monitoring systems.

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“…SewerSnort [Kim et al 2009] is a WSN system for monitoring sewer gas pipelines. A drifter node prototyped on a MicaZ mote and connected to an electrochemical sensor flows inside sewer pipelines.…”

Section: Related Workmentioning

confidence: 99%

Mapping hidden water pipelines using a mobile sensor droplet

Lai

Chen

et al. 2013

ACM Trans. Sen. Netw.

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This study presents several extensions to our previous work on the PipeProbe system, which is a mobile sensor system for identifying the spatial topology of hidden water pipelines (i.e., non-moldable pipes such as copper and PVC) behind walls or under floors [Lai et al., 2010]. The PipeProbe system works by dropping a tiny wireless sensor capsule into the source of a water pipeline. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatio-temporal analysis of these sensor readings, the proposed algorithm locates all turning points in the pipelines and maps their 3D spatial topology. This study expands upon previous research by developing new sensing techniques that identify variable-diameter pipes and differentiate 90-degree pipe turns from 45-degree pipe bends.

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SewerSnort: A Drifting Sensor for In-situ Sewer Gas Monitoring

Cited by 34 publications

References 21 publications

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

Scalable Nodes Deployment Algorithm for the Monitoring of Underwater Pipeline

Autonomous pipeline monitoring and maintenance system: a RFID-based approach

Mapping hidden water pipelines using a mobile sensor droplet

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