ITR/IM+AP+SI+PR: Exploring the Environment in Time: Wireless Networks & Real-Time Management

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0121726 OrcuttThis project seeks to integrate disparate efforts in both the Earth sciences and information technology to develop a model approach for modern data collection and integration. The approach is to build upon existing sensor networks and wireless communications to develop the hardware and software needed for supporting research of the future and to make information available for emergency response, informed decision-making, outreach and education, and enhanced scientific discovery. In particular the existing southern California scientific and educational wireless network will be extended along the coast from San Diego to Santa Barbara and to the oceans beyond the Channel Islands. Because of southern California's reliance on water from the Sierra snow pack and the dependence of this source of water on climate, we will extend the network to include Yosemite National Park.Using this test bed the requirements and utility of wireless networks for collecting and streaming environmental sensor data in real-time will be demonstrated. Multidisciplinary data sets (e.g. earthquake, ocean current, hydrometeorological, and ecological) will be integrated to advance our understanding and management of coastal, ocean, riparian, and terrestrial geophysical phenomena and ecosystems in Southern California and well off shore. The software tools which must be developed for this integration do not exist, although limited prototype systems are available.In particular, existing concepts in object ring buffers (ORB) for collecting disciplinary data to virtual ORBs (VORB) for managing multiple connections to multiple field sensors will be extended. These VORBs will not only provide data to multiple users in real time, but will provide interfaces with archival ORB and more traditional databases. Many of these interactions will be mediated through XML wrappers which will provide the basis for data discovery. A rule-based programmable interface will be developed to dynamically reconfigure and prioritize data capture and analysis from this multiplicity of sensor networks. This approach should be scalable as network speeds increase and data volumes grow, likely geometrically.Funding is provided under the Information Technology Research Initiative.