An Architecture for Managing Knowledge and System Dynamism in the Worldwide Sensor Web

An Architecture for Managing Knowledge and System Dynamism in the Worldwide Sensor Web

Deshendran Moodley (Centre for Artificial Intelligence Research, University of KwaZulu-Natal and Council for Scientific and Industrial Research, Durban, South Africa), Ingo Simonis (International Geospatial Services Institute Ltd, Germany) and Jules Raymond Tapamo (University of KwaZulu-Natal, Durban, South Africa)
Copyright: © 2012 |Pages: 25
DOI: 10.4018/jswis.2012010104
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Abstract

Sensor Web researchers are currently investigating middleware to aid in the dynamic discovery, integration and analysis of vast quantities of both high and low quality, but distributed and heterogeneous earth observation data. Key challenges being investigated include dynamic data integration and analysis, service discovery and semantic interoperability. However, few efforts deal with managing knowledge and system dynamism. Two emerging technologies that have shown promise in dealing with these issues are ontologies and software agents. This paper presents an integrated ontology driven agent based Sensor Web architecture for managing knowledge and system dynamism. An application case study on wildfire detection is used to illustrate the operation of the architecture.
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Introduction

Advances in sensor technology and space science have resulted in the availability of vast quantities of both high and low quality, distributed and heterogeneous earth observation data. This provides the potential for real-time monitoring of the earth and enhancement of our understanding of natural processes. The vision of Sensor Web researchers is to create a worldwide computing infrastructure that enables dynamic sharing and analysis of complex heterogeneous earth observation data sets (Balazinska et al., 2007; Gibbons, Karp, Ke, Nath, & Seshan, 2003; Liang, Croitoru, & Tao, 2005). Issues being investigated include dynamic data fusion, aggregation and analysis (Bröring et al., 2011). While current approaches provide promising ways to facilitate access to distributed sensor data, they do not provide explicit support to formalize and expose scientific understanding and common belief as an explicit and integral component of the Sensor Web.

Scientific understanding and theories in the earth sciences are constantly evolving. Natural processes depend on dynamic micro-scale and meso-scale environments. Processes may vary significantly at different locations and typically change over time. Subsequently, theoretical positions differ greatly and may even be contradictory. An integral part of data analysis and scientific enquiry is the application of different theories to analyse incoming observations. Well-founded and established theories can be incorporated into end user applications to aid decision support, while new theories can be constantly adapted and evaluated against incoming observations to test their validity. Moreover, data mining and pattern analysis techniques can be continuously applied to data over time to capture new patterns, formulate new theories and to refine existing theories. Ultimately the scientific community should publish dynamic and formal models of their theories and validation data online for immediate dissemination and reproducibility. This has the potential to accelerate and reduce the cost and effort for scientific discovery, and lower the barrier for end users to access the latest data analysis and simulation models to improve decision-making and planning.

The Sensor Web approach provides a more ambitious vision than current international activities, such as the Global Earth Observation System of Systems (GEOSS) (http://inspire.eu). (INSPIRE). It involves constructing a worldwide computing infrastructure that enables sharing, analysis and distribution of earth observation data. The availability of new and richer data sets provides new avenues for research and the potential for a better understanding of our natural environment. In Sensor Web applications, data transformation, data analysis, and information extraction algorithms are assembled into executable workflows. These workflows are continuously applied to sensor data to extract and deliver relevant information to decision makers. The algorithms, theories and knowledge used to compose individual workflows can be captured, shared and reconfigured for reuse in other workflows. This can ease the development of new applications and facilitate scientific experimentation.

Sensor Web research is still centered on the discovery, sharing and querying of data with many researchers taking a bottom up, data centric view (Balazinska et al., 2007; Janowicz, Schade, Bröring, Keßler, Maué, & Stasch, 2010). Though the Sensor Web has reached a level of maturity where sensor data can be transported and processed at several levels of abstraction, the integration of belief based assumptions with mathematic models is not properly addressed in current research. Scientists are still not fully empowered to capture and share their knowledge. The ability to capture and formalize knowledge and to evolve knowledge to incorporate new data and new analysis results, i.e., knowledge dynamism, is missing in the Sensor Web. The management of knowledge dynamism would open new avenues for research and the potential for a better understanding of our natural environment.

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