Abstract
Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions.
Highlights
Sensor networks are usually part of infrastructures for the management of complex dynamic systems
The current versions of existing online geographic databases still present some problems when generating appropriate textual descriptions. We analyze these problems and propose future solutions to improve the performance of our sensor network textual generation method
There are significant differences among geographic areas corresponding to different basins (Figure 6)
Summary
Sensor networks are usually part of infrastructures for the management of complex dynamic systems. The data collected by sensors can help users in decision-making tasks (e.g., road networks for traffic surveillance, river channels for water management). These networks can include thousands of Sensors 2015, 15 sensors that periodically (every hour, every 15 min, etc.) measure physical magnitudes, producing large amounts of quantitative data with both spatial and temporal references. Several tools and middleware exist for the provision of data access mechanisms to sensor data (e.g., GSN [1], SOS-compliant services [2], Xively [3], etc.). In the hydrologic domain, sentences like “there is heavy rain in the South of Spain” or “the water level in the Ebro River at
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