Robust management of outliers in sensor network aggregate queries

Yannis Kotidis, Vasilis Vassalos, Antonios Deligiannakis, Vassilis Stoumpos, Alex Delis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sensor networks are increasingly applied for monitoring diverse environments and applications. Due to their unsupervised nature of operation and inexpensive hardware used, sensor nodes may furnish readings of rather poor quality. We thus need to devise techniques that can withstand "dirty" data during query processing. In this paper we introduce a robust aggregation framework that can detect and isolate spurious measurements from computed aggregate values. Such readings are not injected in the reported aggregate, in order not to obscure the outcome, but are still maintained and returned to the user/application, which may investigate them further and take appropriate decisions. In addition, our framework provides a form of positive feedback to the user by enhancing the result with a set of nodes that contain the most characteristic values out of those included in the aggregation process. We perform an extensive experimental evaluation of our framework using real traces of sensory data and demonstrate its utility to the monitoring of applications.

Original languageEnglish (US)
Title of host publicationMobiDE'07
Subtitle of host publicationProceedings of the 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access
Pages17-24
Number of pages8
DOIs
StatePublished - 2007
EventMobiDE'07: 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access - Beijing, China
Duration: Jun 10 2007Jun 10 2007

Publication series

NameInternational Workshop on Data Engineering for Wireless and Mobile Access

Conference

ConferenceMobiDE'07: 6th ACM International Workshop on Data Engineering for Wireless and Mobile Access
CountryChina
CityBeijing
Period6/10/076/10/07

Keywords

  • Data aggregation
  • Outliers
  • Sensor networks

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Software

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