The South Esk Hydrological Sensor Web: A test-bed for research on sensor data management

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The South Esk Hydrological Sensor Web, a test-bed for research on sensor data management [Download]

Abstract:
Limited freshwater resources in many parts of Australia have led to a highly regulated system of water allocation. Poor situation awareness can result in over-extraction of water from river systems, compromising river ecosystems. To increase situation awareness, the Tasmanian ICT Centre is developing a continuous flow forecasting system based on the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards. SWE standards provide an interoperability layer over existing observation systems, numerical models and processing systems. A prototype Hydrological Sensor Web has been established in the South Esk river catchment in north-eastern Tasmania. Observations from the aggregated sensor assets drive a rainfall-runoff model that predicts river flows at key monitoring points in the catchment. River managers use the flow predictions to manage water restrictions.

The South Esk Hydrological Sensor Web is our test-bed for research on management and re-use of sensor observations and sensor metadata. In the first part of my talk, I will discuss our experience with developing a provenance management system for a continuous flow forecasts system. The generation of predicted river flows involves complex interactions between instruments, simulation models, computational facilities and data providers. Correct interpretation of information produced at various stages of the information life-cycle requires detailed knowledge of data creation and transformation processes. Such provenance information allows hydrologists and decision-makers to make sound judgments about the trustworthiness of hydrological information. In the second part of my talk, I will present our current work on developing an infrastructure for community-based sharing of sensor data and knowledge about data and sensors to increase reuse of data. One of the biggest obstacles to re-use of third-party sensor data is lack of knowledge about the properties (e.g., provenance and quality) of data that consequently leads to a lack of trust in the data. A community-based peer review approach for sensor data can help address the trustworthiness issue. The idea is to provide a spatial data infrastructure based on linked data principles that not only facilitates discovery and sharing of sensor data but also allows community-based storage, annotation and linking of sensor data (citizen science for sensor data).