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Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

机译：无线传感器网络中的独立噪声和异常检测：一种新颖的基于时间序列和自适应贝叶斯网络的方法

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摘要

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.
机译：无线传感器网络（WSN）由具有有限计算和通信功能的小型传感器组成。由于噪声，开放信号强度和入侵攻击等开放的环境因素，在WSN中读取数据并不总是可靠的。检测高噪声数据的过程称为异常或离群值检测。 WSN中噪声检测的挑战性方面与传感器的有限计算和通信能力有关。这项研究的目的是设计一种用于WSN的基于本地时间序列的数据噪声和异常检测方法。提出的局部离群值检测算法（LODA）是一种分散式噪声检测算法，它在每个传感器节点上单独运行，具有三个重要特征：消除非有效特征的归约机制，确定数据直方图的存储器大小以实现有效的可用存储器，和分类以预测噪声数据。自适应贝叶斯网络用作分类算法，用于局部预测和识别每个传感器节点中的离群值。我们的方法的结果与使用基准现实数据集的四种著名算法进行了比较，这表明LODA可以在真实感官数据中的离群值预测中实现更高（高达89％）的准确性。

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