Our article “Real- time inverse distance weighting interpolation for streaming sensor data” (Q. Liang, S. Nittel, J.C. Whittier, and Sytze De Bruin) has been published in Transactions of GIS. It is available as pre-print online (link). (TGIS2018_liang_nittel)
With advances in technology and an increasing variety of inexpensive geosensors, environmental monitoring has become increasingly sensor-dense and real-time. Using sensor data streams enables real-time applications such as environmental hazard detection, or earthquake, wildfire or radiation monitoring.
In-depth analysis of such spatial fields is often based on a continuous representation. With very large numbers of concurrent observation streams, novel algorithms are necessary that integrate streams into rasters or other continuous representations continuously in real-time.
In this paper, we present an approach leveraging data stream engines (DSEs) to achieve scalable, high-throughput Inverse Distance Weighting (IDW). In detail, we designed and implemented a novel stream query operator framework that extends general-purpose DSEs. The proposed framework includes a two-panel, spatio-temporal grid based index and several algorithms, namely the Shell and k-Shell algorithms, to estimate individual grid cell efficiently and adaptively for different sampling scenarios. For our performance experiments, we generated several different spatio-temporal stream data sets based on the radiation deposits in the Fukushima region after the nuclear accident in 2011 in Japan. Our results showed that the k-Shell algorithm of the proposed framework produces a raster based on 250K observations streams in under 0.5 seconds using a state of the art workstation.