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Global Outliers Detection in Wireless Sensor Networks: A Novel Approach Integrating Time-Series Analysis, Entropy, and Random Forest-based Classification
arXiv - CS - Networking and Internet Architecture Pub Date : 2021-07-21 , DOI: arxiv-2107.10135
Mahmood Safaei, Maha Driss, Wadii Boulila, Elankovan A Sundararajan, Mitra Safaei

Wireless Sensor Networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high-noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision-making processes in the base station. Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier-detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global-collaborative detection. Hence, the research presented in this paper is intended to design and implement a global outlier-detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time-series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research lab has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.

中文翻译:

无线传感器网络中的全局异常值检测:一种集成时间序列分析、熵和基于随机森林的分类的新方法

无线传感器网络 (WSN) 最近因其在监测、通信和报告特定物理现象方面的实用性而在全世界引起了更大的关注。由于不可避免的环境因素,WSN 收集的数据通常不准确,这些因素可能包括噪声、信号弱或入侵攻击,具体取决于具体情况。发送高噪声数据不仅会对数据准确性和网络可靠性产生负面影响,还会对基站的决策过程产生负面影响。异常检测或异常值检测是在如此描述的上下文中检测噪声数据的过程。文献在 WSN 的背景下包含相对较少的噪声检测技术,特别是对于应用时间序列分析的异常值检测算法,它考虑了有效邻居以确保全局协作检测。因此,本文中提出的研究旨在设计和实施一种全局异常值检测方法,该方法使我们能够找到并选择合适的邻居,以确保基于时间序列分析和熵技术的自适应协作检测。所提出的方法应用随机森林算法来识别最佳结果。为了衡量所提出方法的有效性和效率,我们对英特尔伯克利研究实验室提供的综合真实场景进行了模拟。噪声数据已随机注入收集的数据中。从实验中获得的结果然后进行实验表明,我们的方法可以以高达 99% 的准确度检测异常。
更新日期:2021-07-22
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