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Real‐time inverse distance weighting interpolation for streaming sensor data
Author(s) -
Liang Qinghan,
Nittel Silvia,
Whittier John C.,
Bruin Sytze
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12458
Subject(s) - computer science , inverse distance weighting , data stream mining , weighting , scalability , raster graphics , real time computing , grid , data mining , data stream , interpolation (computer graphics) , sampling (signal processing) , multivariate interpolation , distributed computing , database , computer graphics (images) , computer vision , geography , filter (signal processing) , medicine , telecommunications , geodesy , radiology , bilinear interpolation , animation
Abstract 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 article, we present an approach leveraging data stream engines ( DSE s) 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 DSE s. 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 cells 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 of 2011 in Japan. Our results showed that the k ‐Shell algorithm of the proposed framework produces a raster based on 250k observation streams in under 0.5 s using a state‐of‐the‐art workstation.