WHAT
CORIE is a pilot environmental observation and forecasting system (EOFS) for the Columbia River. The goal of CORIE is to characterize and predict complex circulation and mixing processes in a system encompassing the lower river, the estuary, and the near-ocean using a multi-scale data assimilation model. The challenge for scientists is to maintain the accuracy of their modeling system while minimizing resource usage.
Our goal is to develop an adaptive sampling scheme to guide cruises where and when to collect measurements in the river in order to minimize the uncertainty of certain phenomena of intestest.
FUNDED BY
This work was funded by the National Science Foundation Science and Technology Center for Coastal Margin Observation and Prediction, through a subcontract to Portland State University.
Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).
WHO
Thanh Dang, Sergey Frolov, Nirupama Bulusu, Wu-chi Feng, and Antonio Baptista
HOW
(1) As a first step, we propose a metric for characterizing the error in the CORIE data assimilation model and study the impact of the number of sensors on the error reduction. We use a genetic algorithm to compute the optimal configuration of sensors that reduces the number of sensors to the minimum required while maintaining a similar level of error in the data assimilation model. We verify the results of our algorithm with 30 runs of the data assimilation model. Each run uses data collected and estimated over a two-day period. We can reduce the sensing resource usage by 26.5% while achieving comparable error in data assimilation. Publications [1][2].
(2) We also develop CoTrack, a Collaborative Tracking framework, that allows mobile sensors to cooperate with fixed sensors and numerical models to accurately track dynamic features in an environment. The key innovation in CoTrack is the incorporation of numerical models at different scales and sensor measurements to guide mobile sensors for tracking. The framework includes three components: a macro model for large-scale estimation, a micro model for locale estimation of specific features based on sensor measurements, and an adaptive sampling scheme that guides mobile sensors to accurately track dynamic features. We apply our framework to track salinity intrusion in the Columbia River estuary in Oregon, United States. Our framework is fast and can reduce tracking error by more than half compared to existing data assimilation and state-of-the-art numerical models.
PUBLICATIONS
1.Thanh Dang, Sergey Frolov, Nirupama Bulusu, Wu-chi Feng, and AntÓnio Baptista,"Near Optimal Sensor Selection in The CORIE Observation Network for Data Assimilation Using Genetic Algorithms", In Proceedings of DCOSS 2007, Santa Fe, New Mexico, June 2007.
2. Thanh Dang, Sergey Frolov, Nirupama Bulusu, Wu-chi Feng, and Antonio Baptista, "Adaptive Sampling in the Columbia River Observation Network", SENSY 07 Demo, Sydney, Australia, Nov 2007
3. Thanh Dang, Nirupama Bulusu, Wu-chi Feng, Sergey Frolov, and Antonio Baptista, "CoTrack: A Framework for Tracking Dynamic Features with Static and Mobile Underwater Sensors", WUWNET09 Demo Abstract, Berkeley , California, Nov 2009 (to appear)
LIMITATIONS
(1) Genetic algorithm is slow. We are still investigating other approaches.
CURRENT STATUS
The project is currently active.
DOWNLOAD
We are working on making CoTrack more generic. This version of CoTrack works with CORIE data assimilation framework,
which is released independently and can be accessed at http://www.stccmop.org/~frolovs/software/rdda_code_latest/
OTHERS
NSF Science and Technology Center for Coastal Margin Observation and
Prediction (CMOP)