Abstract
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. In this paper, we first 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. Second, we propose 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. As a result, we can potentially save 40 thousand dollars in initial expenses and 10 thousand dollars in maintenance expense per year.
This algorithm can be used to guide operation of the existing observation network, as well as to guide deployment of future sensor stations. The novelty of our approach is that our problem formulation of network configuration is influenced by the data assimilation framework which is more meaningful to domain scientists, rather than using abstract sensing models.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bian, F., Kempe, D., Govindan, R.: Utility based sensor selection. In: Proceedings of the Fifth International Conference on Information Processing in Sensor Networks (IPSN 06), pp. 11–18, Nashville, Tennessee (April 2006)
Bredin, J.L., Demaine, E.D., Hajiaghayi, M., Rus, D.: Deploying sensor networks with guaranteed capacity and fault tolerance. In: Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing, Urbana-Champaign, Illinois, pp. 309–319. ACM Press, New York (2005)
Corcoran, P., Anglesea, J., Elshaw, M.: The application of genetic algorithms to sensor parameter selection for multisensor array configuration. Sensors and Actuators A Physical 76, 57–66 (1999)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley and Sons, New York (1991)
Cristescu, R., Vetterli, M.: On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks. In: Proceedings of the Fourth International Symposium on Information Processin In Sensor Networks, pp. 159–164, Los Angeles, California (April 2005)
Delaney, J.: Keynote: Next-generation earth and ocean sciences: Opportunities and challenges. In: Proceedings of the 3rd ACM Conference on Embedded Networked Sensor Systems (SenSys), San Diego, California (November 2005)
Ermis, E.B., Saligrama, V.: Adaptive statistical sampling methods for decentralized estimation and detection of localized phenomena. In: Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN 05), pp. 143–150, Los Angeles, California (April 2005)
Ertin, E., Fisher, J.W., Potter, L.C.: Maximum mutual information principle for dynamic sensor query problems. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, pp. 405–416. Springer, Heidelberg (2003)
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics. Journal of Geophysical Research, C5(10) (1999)
Frolov, S., Baptista, A., Lu, Z., van der Merwe, R., Leen, T.: Fast data assimilation with model surrogates: Application to circulation in a highly stratified estury. In: Submission to Ocean Modeling
Khan, A.A., Zohdy, M.A.: A genetic algorithm for selection of noisy sensor data in multisensor data fusion. In: Proceedings of American Control Conference, pp. 2256–2262, Albuquerque, NM (June 1997)
Krause, A., Guestrin, C., Gupta, A., Kleinberg, J.M.: Near-optimal sensor placements: Maximizing information while minimizing communication cost. In: Proceedings of the Fifth International Conference on Information Processing in Sensor Networks (IPSN 06), pp. 2–10, Nashville, Tennessee (April 2006)
Liu, J., Reich, J., Zhao, F.: Collaborative in-network processing for target tracking. EURASIP Journal on Applied Signal Processing, vol. 4 (2002)
Meguerdichian, S., Koushanfar, F., Potkonjak, M., Srivastava, M.B.: Coverage problems in wireless ad-hoc sensor networks. In: Proceedings of the Conference on Computer Communications 2001 (INFOCOM 2001), pp. 1380–1387. Anchorage, Alaska (April 2001)
Michell, T.M.: Machine Learning. McGraw Hill, New York (1997)
Ray, S., Lai, W., Paschalidis, I.C.: Deployment optimization of sensornet-based stochastic location-detection systems. In: Proceedings of the Conference on Computer Communications (INFOCOM 2005), Miami, Florida (March 2005)
Smith. L.I.: A tutorial on principle component analysis (February 2007) http://kybele.psych.cornell.edu/Ẽedelman/Psych-465-Spring-2003/PCA-tutoria l.pdf
van der Merwe, R., Leen, T., Lu, Z., Frolov, S., Baptista, A.M.: Fast neural network surrogates for very high dimensional physics-based models in computational oceanography. Neural Computation (To appear 2007)
van der Merwe, R., Wan, E.A.: Sigma-point kalman filters for probabilistic inference in dynamic state-space models. In: Proceedings of the Workshop on Advances in Machine Learning, Montreal, Canada (June 2003)
Wang, H., Yao, K., Pottie, G., Estrin, D.: Entropy-based sensor selection heuristic for target localization. In: Proceedings of the third international symposium on Information processing in sensor networks (IPSN 04), pp. 36–45, Berkeley, California, USA (April 2004)
Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., Gill, C.: Integrated coverage and connectivity configuration in wireless sensor networks. In: Proceedings of the 1st international conference on Embedded networked sensor systems (Sensys), pp. 28–39. ACM Press, New York (2003)
willett, R., Martin, A., Nowak, R.: Backcasting: Adaptive sampling for sensor networks. In: Proceedings of the Fifth International Conference on Information Processing in Sensor Networks (IPSN 06), pp. 36–45, Nashville, Tennessee (April 2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Dang, T., Frolov, S., Bulusu, N., Feng, Wc., Baptista, A. (2007). Near Optimal Sensor Selection in the COlumbia RIvEr (CORIE) Observation Network for Data Assimilation Using Genetic Algorithms. In: Aspnes, J., Scheideler, C., Arora, A., Madden, S. (eds) Distributed Computing in Sensor Systems. DCOSS 2007. Lecture Notes in Computer Science, vol 4549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73090-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-73090-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73089-7
Online ISBN: 978-3-540-73090-3
eBook Packages: Computer ScienceComputer Science (R0)