Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

1 Citation (Scopus)

Abstract

Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.
Original languageUndefined
Title of host publicationIntelligent Techniques for Warehousing and Mining Sensor Network Data
PublisherIGI Global
Pages136-158
Number of pages23
ISBN (Print)978-1-60566-328-9
DOIs
Publication statusPublished - 1 Dec 2009

Publication series

Name
PublisherIGI Global

Keywords

  • METIS-264098
  • EWI-16411
  • IR-68273

Cite this

Zhang, Y., Meratnia, N., & Havinga, P. J. M. (2009). Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks? In Intelligent Techniques for Warehousing and Mining Sensor Network Data (pp. 136-158). IGI Global. https://doi.org/10.4018/978-1-60566-328-9.ch007
Zhang, Y. ; Meratnia, Nirvana ; Havinga, Paul J.M. / Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?. Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global, 2009. pp. 136-158
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abstract = "Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.",
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Zhang, Y, Meratnia, N & Havinga, PJM 2009, Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks? in Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global, pp. 136-158. https://doi.org/10.4018/978-1-60566-328-9.ch007

Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks? / Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global, 2009. p. 136-158.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

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N2 - Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.

AB - Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current state-of-the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.

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Zhang Y, Meratnia N, Havinga PJM. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks? In Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global. 2009. p. 136-158 https://doi.org/10.4018/978-1-60566-328-9.ch007