Mobile Data Offloading: How Much Can WiFi Deliver?

TLDR

This study quantitatively evaluates 3G mobile data offloading via WiFi and proposes a simulator and theoretical framework to estimate offloading performance. The authors collected two-and-a-half‑week WiFi usage traces from 97 iPhone users in metropolitan areas and used them in a trace‑driven simulation, including a distribution‑model simulator and theoretical framework, to analyze offloading and energy savings under various delay deadlines. The simulation shows WiFi can offload about 65% of traffic and save 55% of battery power without delay, while only marginal gains (<3%) occur with 100‑second delays, and gains rise to over 29% traffic and 20% energy savings when delays exceed one hour, contradicting earlier studies that reported 20–33% gains for 100‑second delays.

Abstract

This paper presents a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited 97 iPhone users from metropolitan areas and collected statistics on their WiFi connectivity during a two-and-a-half-week period in February 2010. Our trace-driven simulation using the acquired whole-day traces indicates that WiFi already offloads about 65% of the total mobile data traffic and saves 55% of battery power without using any delayed transmission. If data transfers can be delayed with some deadline until users enter a WiFi zone, substantial gains can be achieved only when the deadline is fairly larger than tens of minutes. With 100-s delays, the achievable gain is less than only 2%-3%, whereas with 1 h or longer deadlines, traffic and energy saving gains increase beyond 29% and 20%, respectively. These results are in contrast to the substantial gain (20%-33%) reported by the existing work even for 100-s delayed transmission using traces taken from transit buses or war-driving. In addition, a distribution model-based simulator and a theoretical framework that enable analytical studies of the average performance of offloading are proposed. These tools are useful for network providers to obtain a rough estimate on the average performance of offloading for a given WiFi deployment condition.