Machine Learning Methods for Monitoring of Quasiperiodic Traffic in Massive IoT Networks

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 unsupervised, parametric machine learning methods for online monitoring of the network performance of individual devices in IoT deployments with quasi-periodic reporting, such as smart-metering, environmental monitoring and agricultural monitoring. Two clustering methods are based on the Lomb-Scargle periodogram, an approach developed by astronomers for estimating the spectral density of unevenly sampled time series. We present probabilistic performance results for each of the proposed methods based on simulated data and compare the performance to a naïve network monitoring approach. The results show that the proposed methods are more reliable at detecting both hard and soft faults than the naïve-approach, especially when the network outage is high. Furthermore, we test the methods on real-world data from a smart metering deployment. The methods, in particular the clustering method, are shown to be applicable and useful in a real-world scenario.

Intelligent energy prediction techniques for fog computing networks

“…As the IoT devices are rapidly increasing in number, they are generating a huge amount of data. To process this large data, nodes with high computational capacity such as cloud servers are needed [5,6,7,8,9,10,11,12,13]. Cloud computing can reduce the computation burden on the IoT nodes, however, the latency of the computation tasks may be increased due to long-distance transmission of tasks between the IoT devices and the cloud.…”