Distance Based Method for Outlier Detection of Body Sensor Networks

Distance Based Method for Outlier Detection of Body Sensor Networks
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基于距离的人体传感器网络异常检测方法

DOI:
10.4108/eai.19-1-2016.151000
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发表时间:
2016-01
期刊:
EAI Endorsed Transactions on Wireless Systems
影响因子:
--
通讯作者:
Cheng Zhao
Cheng Zhao
中科院分区:
其他
文献类型:
--
作者:
Haibin Zhang;Jiajia Liu;Cheng Zhao

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提出了一种基于距离的人体传感器网络孤立点检测方法。首先,我们使用核密度估计(KDE)来计算诊断数据到k近邻的距离的概率。如果概率小于阈值,并且该数据到其左右邻居的距离大于预定值,则将被诊断的数据判定为异常值。此外,我们还形式化了一种基于滑动窗口的方法来提高孤立点检测的性能。最后,为了通过训练有误差的传感器读数来估计KDE,我们引入了一种基于隐马尔可夫模型(HMM)的方法来估计最有可能产生训练数据的地面真值。仿真结果表明,该方法具有较高的检测精度和较低的虚警率。2015年9月19日收到;2015年11月24日接受;2016年1月19日发布
We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE) to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM) based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate. Received on 19 September 2015; accepted on 24 November 2015; published on 19 January 2016
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