Pervasive Monitoring and Control of Water Lifeline Systems for Disaster Recovery

Project Summary

"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 links.

• 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 system design.

MACSS is a project at USC/ISI funded by the NSF Division of Civil and Mechanical Systems (award number E01-CMS-0112665).

People

• Masanobu Shinozuka (UC Irvine)
• John Heidemann (USC/ISI)
• Fred Stann (USC/ISI)

Publications

• 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. March, 2005. <http://www.isi.edu/~johnh/PAPERS/Stann05a.html>.

• 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. Springer Verlag. 2004. <http://www.isi.edu/~johnh/PAPERS/Heidemann04a.html>.

  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. August, 2004. <http://www.isi.edu/~johnh/PAPERS/Rajgarhia04a.html>.

  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. April, 2003. <http://www.isi.edu/~johnh/PAPERS/Stann03a.html>.

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.

Software

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.

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