Does feature matter: anomaly detection in sensor networks

  • Authors:

  • Rui Li;Kebin Liu;Yuan He;Jizhong Zhao

  • Affiliations:

  • Xi'an Jiaotong University, China;Tsinghua University, China and Hong Kong University of Science and Technology, China;Tsinghua University, China and Hong Kong University of Science and Technology, China;Xi'an Jiaotong University, China

  • Venue:
  • Proceedings of the 6th International Conference on Body Area Networks
  • Year:
  • 2011

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Abstract

Anomaly detection, for uncovering faults and failures, is a crucial task for wireless sensor networks (WSNs). There have been substantive research efforts in this field such as source-level troubleshooting, rule-based inference, and time sequence event analysis. Most existing approaches, however, rely on the collection of a large amount of information. Due to the lack of management on information features, the redundancy of collected information greatly degrades the efficiency of diagnosis in large-scale WSNs. To address this issue, we propose RFS (Ranking-based Feature Selection), a three-stage approach to efficiently select representative feature sets for diagnostic tasks and effectively characterize the network status. RFS is a compatible component that can be integrated with most state-of-the-art diagnosis approaches. We conduct extensive experiments based on a large-scale outdoor WSN system, GreenOrbs, to examine the performance of RFS. The results demonstrate that RFS achieves effective anomaly detection in a large-scale WSN with low overhead.