Large-Scale Measurement and Characterization of Cellular Machine-to-Machine Traffic

TLDR

Cellular network‑based machine‑to‑machine communication is rapidly becoming a market‑changing force for diverse applications such as telematics, smart metering, POS terminals, and home automation. The study asks whether M2M traffic imposes new requirements and challenges for cellular network design and management. We analyze a week‑long traffic trace from a tier‑1 US cellular network, characterizing M2M traffic in terms of temporal dynamics, device mobility, application usage, and network performance, and comparing it to smartphone traffic. M2M traffic differs from smartphone traffic with a higher uplink‑to‑downlink ratio, distinct diurnal patterns, bursty synchronized bursts, and lower mobility, yet competes for resources in the same regions, indicating a need for improved protocols, spectrum allocation, and pricing.

Abstract

Cellular network-based machine-to-machine (M2M) communication is fast becoming a market-changing force for a wide spectrum of businesses and applications such as telematics, smart metering, point-of-sale terminals, and home security and automation systems. In this paper, we aim to answer the following important question: Does traffic generated by M2M devices impose new requirements and challenges for cellular network design and management? To answer this question, we take a first look at the characteristics of M2M traffic and compare it to traditional smartphone traffic. We have conducted our measurement analysis using a week-long traffic trace collected from a tier-1 cellular network in the US. We characterize M2M traffic from a wide range of perspectives, including temporal dynamics, device mobility, application usage, and network performance. Our experimental results show that M2M traffic exhibits significantly different patterns than smartphone traffic in multiple aspects. For instance, M2M devices have a much larger ratio of uplink-to-downlink traffic volume, their traffic typically exhibits different diurnal patterns, they are more likely to generate synchronized traffic resulting in bursty aggregate traffic volumes, and are less mobile compared to smartphones. On the other hand, we also find that M2M devices are generally competing with smartphones for network resources in co-located geographical regions. These and other findings suggest that better protocol design, more careful spectrum allocation, and modified pricing schemes may be needed to accommodate the rise of M2M devices.