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

Zhang, Yang and Meratnia, Nirvana and Havinga, Paul (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. ISBN 9781605663289

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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.
Item Type:	Book Section
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Pervasive Systems (PS)
Link to this item:	http://purl.utwente.nl/publications/68273
Official URL:	http://www.igi-global.com/reference/details.asp?id=35535
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