Abstract
Wireless sensor networks (WSNs) have been increasingly available for monitoring the traffic, weather, pollution, etc. Outlier detection in WSNs is an essential step for many important applications, such as abnormal event detection, fraud analysis, etc. While existing efforts focus on identifying individual outliers from sensory data, the unsupervised high semantic outlier detection in WSNs is more challenging and has received far less attentions. In addition, the correlation between multi-dimensional sensory data has not yet been considered when detecting outliers in WSNs. In this paper, based on multi-dimensional Hidden Markov Models, we propose a trajectory-based outlier detection algorithm by model training and model-based likelihood estimation. Our data preprocessing, clustering, model training and model updating schemes are developed to reduce the computational complexity and enhance the detecting performance. We also explore the possibility and feasibility of adapting the proposed algorithm to real-time outlier detections. Experimental results show that our methods achieve good performance on detecting various kinds of abnormal trajectories composed of multi-dimensional data.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grants 61202460 and 61271226; by the Fok Ying Tung Education Foundation under Grant 132036; by the Fundamental Research Funds for the Central Universities under Grants 2014XJGH003, 2014QN158 and 2014QN164; by the Program for New Century Excellent Talents in University under Grant NCET-10-408 (State Education Ministry); and by the CCF-Tencent Foundation under Grant CCF-TencentAGR20130101.
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Wang, C., Lin, H. & Jiang, H. Trajectory-based multi-dimensional outlier detection in wireless sensor networks using Hidden Markov Models. Wireless Netw 20, 2409–2418 (2014). https://doi.org/10.1007/s11276-014-0757-3
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DOI: https://doi.org/10.1007/s11276-014-0757-3