Pervasive Monitoring and Control of Water Lifeline Systems for Disaster Recovery
"Pervasive Monitoring and Control of Water Lifeline Systems for Disaster Recovery"
is a joint research effort between USC/ISI and UC Irvine.
We propose to apply sensor networks to enhance the performance of
civil infrastructure systems, particularly utility lifeline systems,
under both emergency and daily operational conditions. Specific
objectives of our research are:
To develop a conceptual system
design capable of data acquisition, wireless data transmission, data
processing/analysis and operational control of a water delivery
system. Key challenges here are (a) to develop algorithms to
pre-evaluate the network for robustness in the event of disaster, (b)
to design and evaluate in-network data processing tools to permit
distributed processing of large amounts of sensor data, and (c) to
design algorithms that can control both the water and communication
systems, combining sensing and actuation with minimum (localized)
knowledge. After a disaster, actuation will be used both to disable
damaged parts of the lifeline systems and to repair the network by
augmenting a damaged wireline communication with selected wireless
- To develop innovative neural network-based inverse
analysis algorithms and software to determine in near real-time the
extent and locations of damage or malfunction sustained by system
components such as pipe segments, pipe joints, pumping stations, etc.
Key challenges here are to (a) to evaluate the effectiveness of such
algorithms in the face of partial knowledge (as in a disaster), (b) to
coordinate these algorithms with the in-network processing so they can
scale to metropolitan-sized utilities, (c) to develop techniques to
permit long-term data analysis and fault prediction.
- We will use
a Memphis Light, Gas and Water (MLGW) Division's water system as a
pilot case study, to demonstrate the improvements offered by our
MACSS is a project at USC/ISI funded by the
NSF Division of Civil and Mechanical Systems (award number E01-CMS-0112665).
- Fred Stann and John Heidemann.
BARD: Bayesian-Assisted Resource Discovery In Sensor Networks. In Proceedings of the IEEE Infocom, p. to appear.
Miami, Florida, USA, IEEE.
- John Heidemann and Ramesh Govindan.
Embedded Sensor Networks. In Handbook of Networked and Embedded Control Systems, D. Hristu-Varsakelis and W.S. Levine, editors, p. to appear.
- Abhishek Rajgarhia, Fred Stann, and John Heidemann.
Privacy-Sensitive Monitoring With a Mix of IR Sensors and Cameras. In Proceedings of the Second International Workshop on Sensor and Actor Network Protocols and Applications, pp. 21-29.
Boston, Massachusetts, USA, ACM.
- Fred Stann and John Heidemann.
RMST: Reliable Data Transport in Sensor Networks. In Proceedings of the First International Workshop on Sensor Net Protocols and Applications, pp. 102-112.
Anchorage, Alaska, USA, IEEE.
For a more complete list of related publications, see
the I-LENSE publications page.
Please contact Dr. Shinozuka's to obtain publications related to the civil engineering side of this research project.
This work has supported development of Directed Diffusion and other software
for sensor nets, particularly the BARD and RDD extensions to diffusion.
This software is available from
the I-LENSE software page.
I-LENSE: ISI Laboratory for Embedded Networked
Dr. Shinozuka's research at UC Irvine
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Last modified: $Date: 2005-07-26 13:16:11 -0700 (Tue, 26 Jul 2005) $