Where Things Roam: Uncovering Cellular IoT/M2M Connectivity

TL;DR

This paper provides a large-scale analysis of IoT/M2M roaming, focusing on IoT SIMs in energy meters and the ecosystem, offering insights into deployment patterns and a method to distinguish IoT roamers from traditional ones.

Contribution

It presents the first large-scale study of deployed IoT SIMs, characterizes an operational M2M platform, and develops a classification approach to identify IoT roaming devices.

Findings

Quantifies IoT roaming adoption and its impact on MNOs.

Provides a dataset of device populations over three weeks.

Validates a classification method for IoT device identification.

Abstract

Support for things roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.