论文标题
用于监视大型物联网网络中准周期流量的机器学习方法
Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks
论文作者
论文摘要
大规模物联网（IoT）部署的主要问题之一是监视大量链接的状态。通过链接传输的流量的不规则性加剧了问题，因为交通间歇性可以伪装成链接故障，反之亦然。在这项工作中，我们为运行准周期应用程序的物联网设备提供了流量模型，并提出了有监督的和无监督的机器学习方法，用于监视具有准周期性报告的物联网部署的网络性能，例如智能计量，环境监测和农业监测。无监督的方法基于Lomb-Scargle期间图，这是天文学家开发的一种方法，用于估计不均采样时间序列的光谱密度。
One of the central problems in massive Internet of Things (IoT) deployments is the monitoring of the status of a massive number of links. The problem is aggravated by the irregularity of the traffic transmitted over the link, as the traffic intermittency can be disguised as a link failure and vice versa. In this work we present a traffic model for IoT devices running quasi-periodic applications and we present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. The unsupervised methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series.
