Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 540741
Title Real-time inverse distance weighting interpolation for streaming sensor data
Author(s) Liang, Qinghan; Nittel, Silvia; Whittier, John C.; Bruin, Sytze De
Source Transactions in GIS 22 (2018)5. - ISSN 1361-1682 - p. 1179 - 1204.
DOI https://doi.org/10.1111/tgis.12458
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2018
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 (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 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.
Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.