  With the rising number of interconnected devices and sensors, modeling
distributed sensor networks is of increasing interest. Recurrent neural
networks (RNN) are considered particularly well suited for modeling sensory and
streaming data. When predicting future behavior, incorporating information from
neighboring sensor stations is often beneficial. We propose a new RNN based
architecture for context specific information fusion across multiple spatially
distributed sensor stations. Hereby, latent representations of multiple local
models, each modeling one sensor station, are jointed and weighted, according
to their importance for the prediction. The particular importance is assessed
depending on the current context using a separate attention function. We
demonstrate the effectiveness of our model on three different real-world sensor
network datasets.
