Statistics-based outlier detection for wireless sensor networks

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

Research output: Journal PublicationArticlepeer-review

136 Citations (Scopus)


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 languageEnglish
Pages (from-to)1373-1392
Number of pages20
JournalInternational Journal of Geographical Information Science
Issue number8
Publication statusPublished - Aug 2012
Externally publishedYes


  • geostatistics
  • outlier detection
  • spatial correlation
  • temporal correlation
  • time-series analysis
  • wireless sensor networks

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences


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