Aggregating Linked Sensor Data

Sensor observations are usually offered in relation to a specific purpose, e.g., for reporting fine dust emissions, following strict procedures, and spatio-temporal scales. Consequently, the huge amount of data gathered by today’s public and private sensor networks is most often not reused outside of its initial creation context. Fostering the reusability of observations and derived applications calls for (i) spatial, temporal, and thematic aggregation of measured values, and (ii) easy integration mechanisms with external data sources. In this paper, we investigate how work on sensor observation aggregation can be incorporated into a Linked Data framework focusing on external linkage as well as provenance information. We show that Linked Data adds new aspects to the aggregation problem, e.g., whether external links from one of the original observations can be preserved for the aggregate. The Stimulus-SensorObservation (SSO) ontology design pattern is extended by classes and relations necessary to model the aggregation of sensor observations.