Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine Learning Methods for Monitoring of Quasi-Periodic Traffic in
  Massive IoT Networks   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.

9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: stat
18
The categories of this paper are: econ
2
The categories of this paper are: math
7
The categories of this paper are: hep-th