000			01913nam a22002417a 4500
003			ZW-GwMSU
005			20221128104231.0
008			221128b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aZhang ,Y _eauthor
245			_aStatistics-based outlier detection for wireless sensor networks _ccreated by Y. Zhang , N.A.S. Hamm , N. Meratnia , A. Stein , M. van de Voort & P.J.M. Havinga
264			_bTaylor & Francis _c2012
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aWireless 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.
650			_aoutlier detections
650			_awireless sensor network
650			_aspatial correlation
856			_uhttps://doi.org/10.1080/13658816.2012.654493
942			_2lcc _cJA
999			_c160621 _d160621