Energy-efficient classification for anomaly detection: The wireless channel as a helper
Energy-efficient classification for anomaly detection: The wireless channel as a helper
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DOI：
10.1109/icc.2016.7510770
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Kiril Ralinovski;Mario Goldenbaum;S. Stańczak
中科院分区：
文献类型：
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作者：
Kiril Ralinovski;Mario Goldenbaum;S. Stańczak
异常检测具有各种应用，包括状态监测和故障诊断。目的是感知环境，了解正常系统状态，然后定期对瞬时状态分类是否偏离正常状态。无线传感器网络为监视系统状态提供了一种灵活且具有成本效益的方式。在传统的无线传感器网络中，传感器使用某种干扰避免通道访问方法对其观察结果进行编码并将其传输到融合中心。 Fusion Center解码所有数据，然后对相应的系统状态进行分类。由于这种方法通常是高效效率低下的，因此本文提出了一种传输方案，该方案并没有避免干扰，而是利用它直接在空中进行异常检测。换句话说，无线通道可帮助融合中心立即从通道输出中检索所寻求的分类结果。为此，选择的学习模型是线性支持向量机。在证明了拟议方案的可靠性之后，与使用时间划分多重访问的策略相比，提出了数值示例，以证明其可将异常检测能源消耗降低高达53％的能力。
Anomaly detection has various applications including condition monitoring and fault diagnosis. The objective is to sense the environment, learn the normal system state, and then periodically classify whether the instantaneous state deviates from the normal one or not. Wireless sensor networks provide a flexible and cost-effective way of monitoring a system state. In traditional wireless sensor networks, sensors encode their observations and transmit them to a fusion center using some interference avoiding channel access method. The fusion center decodes all the data and then classifies the corresponding system state. As this approach is in general highly inefficient, this paper proposes a transmission scheme that, instead of avoiding the interference, exploits it for carrying out the anomaly detection directly in the air. In other words, the wireless channel helps the fusion center to retrieve the sought classification outcome immediately from the channel output. To achieve this, the chosen learning model is a linear support vector machine. After proving the reliability of the proposed scheme, numerical examples are presented that demonstrate its ability to reduce the energy consumption for anomaly detection by up to 53 % compared to a strategy that uses time division multiple-access.