TACO: tunable approximate computation of outliers in wireless sensor networks

  • Authors:
  • Nikos Giatrakos;Yannis Kotidis;Antonios Deligiannakis;Vasilis Vassalos;Yannis Theodoridis

  • Affiliations:
  • University of Piraeus, Piraeus, Greece;Athens University of Economics and Business, Athens, Greece;Technical University of Crete, Crete, Greece;Athens University of Economics and Business, Athens, Greece;University of Piraeus, Piraeus, Greece

  • Venue:
  • Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2010

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Abstract

Wireless sensor networks are becoming increasingly popular for a variety of applications. Users are frequently faced with the surprising discovery that readings produced by the sensing elements of their motes are often contaminated with outliers. Outlier readings can severely affect applications that rely on timely and reliable sensory data in order to provide the desired functionality. As a consequence, there is a recent trend to explore how techniques that identify outlier values can be applied to sensory data cleaning. Unfortunately, most of these approaches incur an overwhelming communication overhead, which limits their practicality. In this paper we introduce an in-network outlier detection framework, based on locality sensitive hashing, extended with a novel boosting process as well as efficient load balancing and comparison pruning mechanisms. Our method trades off bandwidth for accuracy in a straightforward manner and supports many intuitive similarity metrics.