An e-Science Cyberinfrastructure for Solar-Enabled Water Production and Recycling
We propose an e-Science cyberinfrastructure to support the scientific processes of a solar-enabled water production and recycling application. It forms the key resource sharing backbone that allows each participating scientific process to expose its sensor, instrument, data, and intellectual resources in a service-oriented manner, accompanied by domain-specific resource knowledge. The cyberinfrastructure integrates sensor grid, service-oriented architecture, and semantic Web in an innovative manner. We discuss the design of the ontology to describe the resources in the project, such as data, services, computational and storage resources. An object-oriented database is designed for flexible and scalable data storage. Data management issues are discussed within the context of the project requirements. Various data services are created to meet the needs of the users. In this manner, the cyberinfrastructure facilitates the resource sharing among the participants of the project. Complex workflows are also supported by the proposed cyberinfrastructure.
KeywordsModel Predictive Control Virtual Organization Membrane Distillation Ontology Design Sensor Grid
This work was supported by Microsoft Research, Intelligent Systems Center of Nanyang Technological University, Research Support Office of Nanyang Technological University, and the Singapore National Research Foundation (NRF) under grant number NRF-G-CRP-2007-02.
- 1.J. Joseph, C. Fellenstein, Grid Computing, IBM Press, 2004.Google Scholar
- 2.T. Erl, Service Oriented Architecture: Concepts, Technology, and Design, Prentice-Hall, 2005.Google Scholar
- 3.J. Zibrida, R. Zuhl, J. Lewis and Z. Amjad, “Advances in reverse osmosis application in water reuse,” Corrosion 2000, Mar 2000.Google Scholar
- 6.K. Ling, J. Maciejowski and B. Wu, “Multiplexed Model Predictive Control,” 16th IFAC World Congress, Prague, Jul 2005.Google Scholar
- 7.H. B. Lim, M. Iqbal, Y. Yao, and W. Wang, “A smart e-Science cyberinfrastructure for cross-disciplinary scientific collaborations,” To appear in Semantic e-Science, Springer Annals of Information Systems (AoIS), Springer-Verlag, 2010.Google Scholar
- 8.National Weather Study Project, http://nwsp.ntu.edu.sg
- 9.H. B. Lim, M. Iqbal, W. Wang, and Y. Yao, “The National Weather Sensor Grid: A large-scale cyber-sensor infrastructure for environmental monitoring,” International Journal of Sensor Networks (IJSNet), Inderscience, Vol 7, No. 1/2, pp. 19–36, 2010.Google Scholar
- 11.F. Koushanfar, M. Potkonjak, A. Sangiovanni-Vincentelli, “Error models for light sensors by statistical analysis of raw sensor measurement,” Proc. of the IEEE Sensors, pp. 1472–1475, Oct 2004.Google Scholar
- 12.X. Yang, R. Bruin, M. Dove, “Developing an end-to-end scientific workflow,” Computing in Science and Engineering, vol. 12, no. 3, pp 52–61, May/Jun 2010. http://www.computer.org/portal/web/csdl/doi/10.1109/MCSE.2010.61
- 13.MapleSoft, http://www.maplesoft.com/
- 14.Google Maps, http://code.google.com/apis/maps/