A Semantic Sensor Web for Environmental Decision Support Applications

作     者：Gray, Alasdair J. G. Sadler, Jason Kit, Oles Kyzirakos, Kostis Karpathiotakis, Manos Calbimonte, Jean-Paul Page, Kevin Garcia-Castro, Raul Frazer, Alex Galpin, Ixent Fernandes, Alvaro A. A. Paton, Norman W. Corcho, Oscar Koubarakis, Manolis De Roure, David Martinez, Kirk Gomez-Perez, Asuncion 

作者机构：Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England Univ Southampton GeoData Inst Southampton SO17 1BJ Hants England Potsdam Inst Climate Impact Res Dept Climate Impacts & Vulnerabil D-14412 Potsdam Germany Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Politecn Madrid Ontol Engn Grp E-28660 Madrid Spain Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2011年第11卷第9期

页      面：8855-8887页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：European Commission [FP7-223913] 

主　　题：semantic sensor web application and visualisation semantic data integration 

摘      要：Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England.