SWATS: Wireless Sensor Networks for Steamflood and Waterflood Pipeline Monitoring. Yoon, S., Ye, W., Heidemann, J., Littlefield, B., & Shahabi, C. IEEE Network Magazine, 25(1):50–56, January, 2011.
SWATS: Wireless Sensor Networks for Steamflood and Waterflood Pipeline Monitoring [link]Paper  doi  abstract   bibtex   
State-of-the-art anomaly detection systems deployed in the oilfields are expensive, not scalable to a large number of sensors, require manual operation, and provide data with a long delay. To overcome these problems, we design a wireless sensor network system that detects, identifies, and localizes major anomalies such as blockage and leakage that arise in steamflood and waterflood pipelines in oilfields. A sensor network consists of small, inexpensive nodes equipped with embedded processors and wireless communication, which enables flexible deployment and close observation of phenomena without human intervention. Our sensor network based system, SWATS (Steamflood and WAterflood Tracking System), aims to allow continuous monitoring of the steamflood and waterflood systems with low cost, short delay, and fine granularity coverage while providing high accuracy and reliability. The anomaly detection and identification is challenging because of the inherent inaccuracy and unreliability of sensors and the transient characteristics of the flows. Moreover, observation by a single node cannot capture the topological effects on the transient characteristics of steam and water fluid to disambiguate similar problems and false alarms. We address these hurdles by utilizing multi-modal sensing and multi-sensor collaboration and exploiting temporal and spatial patterns of the sensed phenomena.
@Article{Yoon11a,
	author = 	"SunHee Yoon and Wei Ye and John Heidemann and
                  Brian Littlefield and Cyrus Shahabi",
	title = 	"SWATS: Wireless Sensor Networks for
                  Steamflood and Waterflood Pipeline Monitoring",
	journal = 	"IEEE Network Magazine",
	year = 		2011,
	sortdate = "2011-01-01",
	project = "ilense, imsc, pecase, cens, cisoft",
	jsubject = "sensornet_fusion",
	volume = 25,
	number = 1,
	month = jan,
	pages = "50--56",
	location = 	"johnh: pafile",
	keywords = 	"sensornet, oilfield, steamflood, waterflood",
	doi = "http://dx.doi.org/10.1109/MNET.2011.5687953",
	  url =		"http://www.isi.edu/%7ejohnh/PAPERS/Yoon11a.html",
	  pdfurl =	"http://www.isi.edu/%7ejohnh/PAPERS/Yoon11a.pdf",
	  copyrightholder = "IEEE",
	  copyrightterms = "	Personal use of this material is permitted.  However, 	permission to reprint/republish this material for advertising 	or promotional purposes or for creating new collective works         for resale or redistribution to servers or lists, 	or to reuse any copyrighted component of this work in other works 	must be obtained from the IEEE. ",
	  myorganization = 	"USC/Information Sciences Institute",
	abstract = "
State-of-the-art anomaly detection systems deployed in the oilfields
are expensive, not scalable to a large number of sensors, require
manual operation, and provide data with a long delay. To overcome
these problems, we design a wireless sensor network system that
detects, identifies, and localizes major anomalies such as blockage
and leakage that arise in steamflood and waterflood pipelines in
oilfields. A sensor network consists of small, inexpensive nodes
equipped with embedded processors and wireless communication, which
enables flexible deployment and close observation of phenomena without
human intervention. Our sensor network based system, SWATS (Steamflood
and WAterflood Tracking System), aims to allow continuous monitoring
of the steamflood and waterflood systems with low cost, short delay,
and fine granularity coverage while providing high accuracy and
reliability. The anomaly detection and identification is challenging
because of the inherent inaccuracy and unreliability of sensors and
the transient characteristics of the flows. Moreover, observation by a
single node cannot capture the topological effects on the transient
characteristics of steam and water fluid to disambiguate similar
problems and false alarms. We address these hurdles by utilizing
multi-modal sensing and multi-sensor collaboration and exploiting
temporal and spatial patterns of the sensed phenomena.
",
}
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