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.
翻译:无线传感器网络(WSNs)最近因其在监测、通信和报告具体物理现象方面的实用性而引起全世界更多的注意。WSNS所收集的数据由于不可避免的环境因素而往往不准确,这些环境因素可能包括噪音、信号弱点或入侵攻击,其中可能包括噪音、信号弱点或根据具体情况进行的入侵攻击。发送高噪音数据不仅对数据准确性和网络可靠性产生消极影响,而且对基地站的决策过程也产生消极影响。异常探测或异常探测是在所描述的环境下探测噪音数据的过程。文献在WSNs中含有相对较少的噪音探测技术,特别是用于应用时间序列分析的外部探测算法,这种算法考虑到有效的邻居以确保进行全球协作检测。因此,本文提出的研究旨在设计和实施全球外部探测方法,从而使我们能够根据时间序列分析和催化技术,找到和选择适当的邻居,以确保进行适应性的合作检测。拟议方法采用随机森林算法来确定最佳结果,特别是用于采用超导测算法,即考虑有效邻居确保进行全球协作检测。因此,通过模拟的实验性研究,没有进行模拟数据结果。随后通过模拟实验室分析,可以提供一种随机测算结果。