6th International ICST Conference on Body Area Networks

Research Article

Does Feature Matter: Anomaly Detection in Sensor Networks

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  • @INPROCEEDINGS{10.4108/icst.bodynets.2011.247058,
        author={Rui Li and Kebin Liu and Yuan He and Jizhong Zhao},
        title={Does Feature Matter: Anomaly Detection in Sensor Networks},
        proceedings={6th International ICST Conference on Body Area Networks},
        keywords={anomaly detection wireless sensor network},
  • Rui Li
    Kebin Liu
    Yuan He
    Jizhong Zhao
    Year: 2012
    Does Feature Matter: Anomaly Detection in Sensor Networks
    DOI: 10.4108/icst.bodynets.2011.247058
Rui Li,*, Kebin Liu1, Yuan He1, Jizhong Zhao2
  • 1: Tsinghua University/ HKUST
  • 2: Xi'an Jiaotong University
*Contact email: rli@mail.xjtu.edu.cn


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.