Statistics-based outlier detection for wireless sensor networks

Y. Zhang, N.A.S. Hamm, N.A.S Hamm, Nirvana Meratnia, A. Stein, M. van de Voort, Paul J.M. Havinga

Research output: Contribution to journalArticleAcademicpeer-review

85 Citations (Scopus)
127 Downloads (Pure)

Abstract

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.
Original languageUndefined
Pages (from-to)1373-1392
Number of pages20
JournalInternational journal of geographical information science
Volume26
Issue number8
DOIs
Publication statusPublished - 28 Aug 2012

Keywords

  • Outlier Detection
  • Spatial correlation
  • Temporal correlation
  • EWI-21620
  • IR-80468
  • geostatistics
  • Wireless Sensor Networks
  • METIS-293989
  • time-series analysis

Cite this

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title = "Statistics-based outlier detection for wireless sensor networks",
abstract = "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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year = "2012",
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doi = "10.1080/13658816.2012.654493",
language = "Undefined",
volume = "26",
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journal = "International journal of geographical information science",
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Statistics-based outlier detection for wireless sensor networks. / Zhang, Y.; Hamm, N.A.S.; Hamm, N.A.S; Meratnia, Nirvana; Stein, A.; van de Voort, M.; Havinga, Paul J.M.

In: International journal of geographical information science, Vol. 26, No. 8, 28.08.2012, p. 1373-1392.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Statistics-based outlier detection for wireless sensor networks

AU - Zhang, Y.

AU - Hamm, N.A.S.

AU - Hamm, N.A.S

AU - Meratnia, Nirvana

AU - Stein, A.

AU - van de Voort, M.

AU - Havinga, Paul J.M.

PY - 2012/8/28

Y1 - 2012/8/28

N2 - 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.

AB - 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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KW - Temporal correlation

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KW - IR-80468

KW - geostatistics

KW - Wireless Sensor Networks

KW - METIS-293989

KW - time-series analysis

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