In-network approximate computation of outliers with quality guarantees

  • Authors:

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

  • Affiliations:

  • Department of Informatics, University of Piraeus, Central Building, 80 Karaoli & Dimitriou St., GR-18534 Piraeus, Greece;Department of Informatics, Athens University of Economics and Business, 76 Patission St., GR-10434 Athens, Greece;Department of Electronic and Computer Engineering, Technical University of Crete, University Campus., GR-73100 Chania, Greece;Department of Informatics, Athens University of Economics and Business, 76 Patission St., GR-10434 Athens, Greece;Department of Informatics, University of Piraeus, Central Building, 80 Karaoli & Dimitriou St., GR-18534 Piraeus, Greece

  • Venue:
  • Information Systems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

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 based on their similarity to other readings in the network 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. Our experiments demonstrate that our framework can reliably identify outlier readings using a fraction of the bandwidth and energy that would otherwise be required.