Statistics-based outlier detection for wireless sensor networks
Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information a...
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Published in | International journal of geographical information science : IJGIS Vol. 26; no. 8; pp. 1373 - 1392 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Abingdon
Taylor & Francis
01.08.2012
Taylor & Francis LLC |
Subjects | |
Online Access | Get full text |
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Summary: | Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1365-8816 1365-8824 1362-3087 |
DOI: | 10.1080/13658816.2012.654493 |