A Survey of Methods for Finding Outliers in Wireless Sensor Networks
Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Her...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
McDonald, Dylan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of network and systems management - Springer US, 1993, 23(2013), 1 vom: 26. Sept., Seite 163-182 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2013 ; number:1 ; day:26 ; month:09 ; pages:163-182 |
| Links: |
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| DOI / URN: |
10.1007/s10922-013-9287-z |
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OLC2066987972 |
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| 520 | |a Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. | ||
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| 650 | 4 | |a Trust based algorithms | |
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| 700 | 1 | |a Madria, Sanjay |4 aut | |
| 700 | 1 | |a Ercal, Fikret |4 aut | |
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10.1007/s10922-013-9287-z doi (DE-627)OLC2066987972 (DE-He213)s10922-013-9287-z-p DE-627 ger DE-627 rakwb eng 004 VZ McDonald, Dylan verfasserin aut A Survey of Methods for Finding Outliers in Wireless Sensor Networks 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. Sensor networks Centralized outlier detection Distributed outlier detection Distance based algorithms Density based algorithms Trust based algorithms Un-supervised outlier detection Sanchez, Stewart aut Madria, Sanjay aut Ercal, Fikret aut Enthalten in Journal of network and systems management Springer US, 1993 23(2013), 1 vom: 26. Sept., Seite 163-182 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:23 year:2013 number:1 day:26 month:09 pages:163-182 https://doi.org/10.1007/s10922-013-9287-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 23 2013 1 26 09 163-182 |
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10.1007/s10922-013-9287-z doi (DE-627)OLC2066987972 (DE-He213)s10922-013-9287-z-p DE-627 ger DE-627 rakwb eng 004 VZ McDonald, Dylan verfasserin aut A Survey of Methods for Finding Outliers in Wireless Sensor Networks 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. Sensor networks Centralized outlier detection Distributed outlier detection Distance based algorithms Density based algorithms Trust based algorithms Un-supervised outlier detection Sanchez, Stewart aut Madria, Sanjay aut Ercal, Fikret aut Enthalten in Journal of network and systems management Springer US, 1993 23(2013), 1 vom: 26. Sept., Seite 163-182 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:23 year:2013 number:1 day:26 month:09 pages:163-182 https://doi.org/10.1007/s10922-013-9287-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 23 2013 1 26 09 163-182 |
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10.1007/s10922-013-9287-z doi (DE-627)OLC2066987972 (DE-He213)s10922-013-9287-z-p DE-627 ger DE-627 rakwb eng 004 VZ McDonald, Dylan verfasserin aut A Survey of Methods for Finding Outliers in Wireless Sensor Networks 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. Sensor networks Centralized outlier detection Distributed outlier detection Distance based algorithms Density based algorithms Trust based algorithms Un-supervised outlier detection Sanchez, Stewart aut Madria, Sanjay aut Ercal, Fikret aut Enthalten in Journal of network and systems management Springer US, 1993 23(2013), 1 vom: 26. Sept., Seite 163-182 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:23 year:2013 number:1 day:26 month:09 pages:163-182 https://doi.org/10.1007/s10922-013-9287-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 23 2013 1 26 09 163-182 |
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10.1007/s10922-013-9287-z doi (DE-627)OLC2066987972 (DE-He213)s10922-013-9287-z-p DE-627 ger DE-627 rakwb eng 004 VZ McDonald, Dylan verfasserin aut A Survey of Methods for Finding Outliers in Wireless Sensor Networks 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. Sensor networks Centralized outlier detection Distributed outlier detection Distance based algorithms Density based algorithms Trust based algorithms Un-supervised outlier detection Sanchez, Stewart aut Madria, Sanjay aut Ercal, Fikret aut Enthalten in Journal of network and systems management Springer US, 1993 23(2013), 1 vom: 26. Sept., Seite 163-182 (DE-627)182373657 (DE-600)1202352-8 (DE-576)9182373655 1064-7570 nnns volume:23 year:2013 number:1 day:26 month:09 pages:163-182 https://doi.org/10.1007/s10922-013-9287-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 AR 23 2013 1 26 09 163-182 |
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Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN. © Springer Science+Business Media New York 2013 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2066987972</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503145430.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2013 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10922-013-9287-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2066987972</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10922-013-9287-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">McDonald, Dylan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Survey of Methods for Finding Outliers in Wireless Sensor Networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media New York 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensor networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Centralized outlier detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed outlier detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distance based algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Density based algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Trust based algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Un-supervised outlier detection</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sanchez, Stewart</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Madria, Sanjay</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ercal, Fikret</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of network and systems management</subfield><subfield code="d">Springer US, 1993</subfield><subfield code="g">23(2013), 1 vom: 26. 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