Finding Symbolic Bug Patterns in Sensor Networks

  • Authors:

  • Mohammad Maifi Khan;Tarek Abdelzaher;Jiawei Han;Hossein Ahmadi

  • Affiliations:

  • Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA;Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

  • Venue:
  • DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
  • Year:
  • 2009

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Abstract

This paper presents a failure diagnosis algorithm for summarizing and generalizing patterns that lead to instances of anomalous behavior in sensor networks. Often multiple seemingly different event patterns lead to the same type of failure manifestation. A hidden relationship exists, in those patterns, among event attributes that is somehow responsible for the failure. For example, in some system, a message might always get corrupted if the sender is more than two hops away from the receiver (which is a distance relationship) irrespective of the senderId and receiverId. To uncover such failure-causing relationships, we present a new symbolic pattern extraction technique that identifies and symbolically expresses relationships correlated with anomalous behavior. Symbolic pattern extraction is a new concept in sensor network debugging that is unique in its ability to generalize over patterns that involve different combinations of nodes or message exchanges by extracting their common relationship. As a proof of concept, we provide synthetic traffic scenarios where we show that applying symbolic pattern extraction can uncover more complex bug patterns that are crucial to the understanding of real causes of problems. We also use symbolic pattern extraction to diagnose a real bug and show that it generates much fewer and more accurate patterns compared to previous approaches.