Modeling IoT Traffic Patterns: Insights from a Statistical Analysis of an MTC Dataset

TL;DR

This paper analyzes IoT machine-type communication traffic patterns using statistical tests on a real dataset, identifying models that accurately represent periodic and event-driven traffic with low error rates.

Contribution

It provides a comprehensive statistical analysis of an IoT MTC dataset and evaluates models that effectively characterize different traffic types, aiding traffic management.

Findings

Poisson model best fits event-driven traffic with <11% error

Quasi-periodic model fits periodic traffic with <7% error

Statistical tests validate the accuracy of the proposed models

Abstract

The Internet-of-Things (IoT) is rapidly expanding, connecting numerous devices and becoming integral to our daily lives. As this occurs, ensuring efficient traffic management becomes crucial. Effective IoT traffic management requires modeling and predicting intrincate machine-type communication (MTC) dynamics, for which machine-learning (ML) techniques are certainly appealing. However, obtaining comprehensive and high-quality datasets, along with accessible platforms for reproducing ML-based predictions, continues to impede the research progress. In this paper, we aim to fill this gap by characterizing the Smart Campus MTC dataset provided by the University of Oulu. Specifically, we perform a comprehensive statistical analysis of the MTC traffic utilizing goodness-of-fit tests, including well-established tests such as Kolmogorov-Smirnov, Anderson-Darling, chi-squared, and root mean square error. The analysis centers on examining and evaluating three models that accurately represent the two most significant MTC traffic types: periodic updating and event-driven, which are also identified from the dataset. The results demonstrate that the models accurately characterize the traffic patterns. The Poisson point process model exhibits the best fit for event-driven patterns with errors below 11%, while the quasi-periodic model fits accurately the periodic updating traffic with errors below 7%.