Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks

• URL: http://arxiv.org/abs/2002.01552v2
• Date: Thu, 27 Feb 2020 15:25:07 GMT
• Title: Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in Massive IoT Networks
• Authors: Ren\'e Brandborg S{\o}rensen, Jimmy Jessen Nielsen, Petar Popovski
• Abstract summary: We present a traffic model for IoT devices running quasi-periodic applications. We present both supervised and unsupervised machine learning methods for monitoring the network performance of IoT deployments.
• Score: 30.71111490861155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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