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Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Aengus McCullough, Newcastle University Philip James, Stuart Barr [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support What is the Sensor Web? “A coordinated observation infrastructure composed of a distributed collection of resources that can collectively behave as a single, autonomous, task-able, dynamically adaptive and reconfigurable observing system that provides raw and processed data, along with associated meta-data, via a set of standards-based service-oriented interfaces.” (Glenn, 2007) [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Sensor Web Enablement Sourced from Reichardt (2005) [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Sensors MIDDLEWARE Compute Nodes [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Research Goals 1. To what extent can standards in Geospatial Web Services, Sensor Web and Distributed Computing be integrated within a geoprocessing context? 2. What are the potential bottlenecks in a real time monitoring and prediction system in relation to distributed geoprocessing? 3. Are there any generic methods of distributing real time geoprocessing operations? 4. To what degree is distributed computing a suitable tool for earth systems monitoring? [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Use Case: The ANGEL project Goals: • To monitor the position of vehicles carrying dangerous goods • To determine the optimum route to their destination • Minimise cost, fuel-consumption and the risk of hijacking [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support System Overview • Loosely coupled service-oriented architecture • Utilise live traffic and vehicle position data. • Use OGC web services as the major data source. • Use the Grid to perform computationally intensive processing, enabling the system to scale over multiple vehicles and large geographic areas whilst minimising the use of computing resources. [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Scalability Issues • Geographic Data is voluminous • Shortest Path computations are expensive = problem for large graphs Standards Issues • OGC, OGSA, WS-I, WSRF, OASIS…… [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Implementation To Date: • Services implemented – Data services – Pre-processing services • Next Step: – Build processing services on top of grid middleware [email protected] Sensor Web, Grid Computing and Geospatial Web Services for Real Time Decision Support Thanks for Listening Questions? [email protected]
