How does enterprise IoT traffic evolve? Real-world evidence from a Finnish operator

TL;DR

This study analyzes two years of enterprise IoT traffic data from a Finnish operator, revealing growth patterns, industry differences, and mobility trends, and provides models for traffic forecasting and mobility predictability.

Contribution

It offers the first empirical analysis of long-term EIoT traffic evolution, industry comparisons, and predictive modeling of traffic and mobility patterns.

Findings

EIoT traffic per device increased threefold over two years

Only 2% of devices are LTE-enabled, with 30% still 2G only

Traffic patterns cluster into three main daily profiles

Abstract

The adoption of Internet of Things (IoT) technologies in businesses is increasing and thus enterprise IoT (EIoT) is seemingly shifting from hype to reality. However, the actual use of EIoT over significant timescales has not been empirically analyzed. In other words, the reality remains unexplored. Furthermore, despite the variety of EIoT verticals, the use of IoT across vertical industries has not been compared. This paper uses a two-year EIoT dataset from a major Finnish mobile network operator to investigate device use across industries, cellular traffic patterns, and mobility patterns. We present a variety of novel findings: EIoT traffic volume per device has increased three-fold over the last two years, the share of LTE-enabled devices has remained low at around 2% and that 30% of EIoT devices are still 2G only, and there are order of magnitude differences between different industries' EIoT traffic and mobility. We also show that daily traffic can be clustered into only three patterns, differing mainly in the presence and timing of a peak hour. Beyond these descriptive results, modeling and forecasting is conducted for both traffic and mobility. We forecast the total daily EIoT traffic through a temporal regression model and achieve an error of about 15% over medium-term (30 to 180 day) horizons. We also model device mobility through a Markov mixture model and quantify the upper bound of predictability for device mobility.