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PIPENETa wireless sensor network for pipeline monitoring
Abstract
US water utilities are faced with mounting operational and maintenance costs as a result of aging pipeline infrastructures. Leaks and ruptures in water supply pipelines and blockages and overflow events in sewer collectors cost millions of dollars a year, and monitoring and repairing this underground infrastructure presents a severe challenge. In this paper, we discuss how wireless sensor networks (WSNs) can increase the spatial and temporal resolution of operational data from pipeline infrastructures and thus address the challenge of near real-time monitoring and eventually control. We focus on the use of WSNs for monitoring large diameter bulk-water transmission pipelines. We outline a system, PipeNet, we have been developing for collecting hydraulic and acoustic/vibration data at high sampling rates as well as algorithms for analyzing this data to detect and locate leaks. Challenges include sampling at high data rates, maintaining aggressive duty cycles, and ensuring tightly time-synchronized data collection, all under a strict power budget. We have carried out an extensive field trial with Boston Water and Sewer Commission in order to evaluate some of the critical components of PipeNet. Along with the results of this preliminary trial, we describe the results of extensive laboratory experiments which are used to evaluate our analysis and data processing solutions. Our prototype deployment has led to the development of a reusable, field-reprogrammable software infrastructure for distributed high-rate signal processing in wireless sensor networks, which we also describe.
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Cites
The generalized correlation method for estimation of time delay
Knapp, C., & Carter, G. (1976) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1976.1162830 · Cited by 4,433 · cited 1×
“Many of the methods proposed for time delay estimation tdelay, between noisy signals which are received at two or more spatially separated sensors are related through a generalized cross correlation approach [14][15][ 16 ].”
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Cited by
Energy efficiency in wireless sensor networks: A top-down survey
Rault, T., Bouabdallah, A., & Challal, Y. (2014) · Computer Networks · doi:10.1016/j.comnet.2014.03.027 · Cited by 908
Applications of wireless sensor networks for urban areas: A survey
Rashid, B., & Rehmani, M. H. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.09.008 · Cited by 731
The role of advanced sensing in smart cities
Hancke, G., Silva, B., & Hancke, Jr. (2012) · Sensors · doi:10.3390/s130100393 · Cited by 626
Wan, J., Yu, Y., Wu, Y., Feng, R., & Yu, N. (2011) · Sensors · doi:10.3390/s120100189 · Cited by 122
Laszka, A., Abbas, W., Vorobeychik, Y., & Koutsoukos, X. (2020) · Cyber-Physical Systems · doi:10.1080/23335777.2019.1624620 · Cited by 22
Metadata
- Authors
- Stoianov, I., Nachman, L., Madden, S., & Tokmouline, T.
- Venue
- Proceedings of the 6th international conference on Information processing in sensor networks - IPSN '07
- Publisher
- ACM Press
- Published
- 2007
- Pages
- 264
- OpenAlex
- W2120117050
- Cited by
- 467
- References
- 1
- Type
- conference abstract
- Language
- en
- Open access
- closed
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- Stoianov, I., Nachman, L., Madden, S., & Tokmouline, T. (2007). PIPENETa wireless sensor network for pipeline monitoring. Proceedings of the 6th international conference on Information processing in sensor networks - IPSN '07. https://doi.org/10.1145/1236360.1236396
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