The abnormal nodes of the Internet of Things (IoT) are closely related to the reliability of the network, but it is always a difficulty to predict the characteristics of the induced signals that threaten reliability. In this article, a feature analysis method for the abnormal signal inducing the unreliable anomaly in IoT based on chaos attractor is proposed to make phase space reconstruction of the abnormal signal that the nodes of IoT can induce unreliability. The phase space structure is constructed by the sequence of abnormal signals. The chaotic attractor is used to analyze the phase space characteristics and reflect the phase space characteristics of unreliable signals induced by the network from the geometric point of view. The mutual information method is used to solve the time delay of the unreliable abnormal signal of IoT, calculate the embedding dimension, find out the chaotic attractors under different characteristics, and judge the reliability change characteristics by analyzing the chaotic attractors. The experimental results show that the method has a better time slot consumption, throughput, and idle energy indexes. Furthermore, our experiments show that the chaos attractor method can effectively predict the reliability trend of IoT, and better predict the reliability anomalies of IoT.

中文翻译：

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具有异常诱导信号特征的物联网网络的可靠性

物联网（IoT）的异常节点与网络的可靠性密切相关，但预测威胁可靠性的感应信号的特征始终是一个难题。本文提出了一种基于混沌吸引子的异常信号诱发物联网不可靠异常的特征分析方法，对物联网节点诱发不可靠性的异常信号进行相空间重构。相空间结构由异常信号序列构成。混沌吸引子用于分析相空间特性，从几何角度反映网络诱发的不可靠信号的相空间特性。采用互信息法解决物联网不可靠异常信号的时延问题，计算嵌入维数，找出不同特征下的混沌吸引子，通过分析混沌吸引子判断可靠性变化特征。实验结果表明，该方法具有较好的时隙消耗、吞吐量和空闲能量指标。此外，我们的实验表明，混沌吸引子方法可以有效预测物联网的可靠性趋势，更好地预测物联网的可靠性异常。