Analyzing Mobility-Traffic Correlations in Large WLAN Traces: Flutes vs. Cellos

TL;DR

This study provides a comprehensive, data-driven analysis of mobility and traffic patterns in large WLAN traces, highlighting their interplay and informing future mobile network modeling and benchmarking.

Contribution

It offers the first detailed integrated analysis of mobility and traffic patterns using large-scale, multidimensional datasets, advancing the realism of mobile network models.

Findings

Mobility and traffic patterns are highly correlated across different device types.

Existing models often overlook the interplay between mobility and traffic.

Insights inform the development of more realistic mobile network models and benchmarks.

Abstract

Two major factors affecting mobile network performance are mobility and traffic patterns. Simulations and analytical-based performance evaluations rely on models to approximate factors affecting the network. Hence, the understanding of mobility and traffic is imperative to the effective evaluation and efficient design of future mobile networks. Current models target either mobility or traffic, but do not capture their interplay. Many trace-based mobility models have largely used pre-smartphone datasets (e.g., AP-logs), or much coarser granularity (e.g., cell-towers) traces. This raises questions regarding the relevance of existing models, and motivates our study to revisit this area. In this study, we conduct a multidimensional analysis, to quantitatively characterize mobility and traffic spatio-temporal patterns, for laptops and smartphones, leading to a detailed integrated mobility-traffic analysis. Our study is data-driven, as we collect and mine capacious datasets (with 30TB, 300k devices) that capture all of these dimensions. The investigation is performed using our systematic (FLAMeS) framework. Overall, dozens of mobility and traffic features have been analyzed. The insights and lessons learnt serve as guidelines and a first step towards future integrated mobility-traffic models. In addition, our work acts as a stepping-stone towards a richer, more-realistic suite of mobile test scenarios and benchmarks.