  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.
