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

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.1 Throughout, we use flutes for smartphones, and cellos for laptops.

Learning the Relation Between Mobile Encounters and Web Traffic Patterns

“…Intuitively, there are significant differences between weekdays and weekends in user behavior, and consequently their mobility and traffic patterns. In [21], we found that numbers of user devices on campus drops significantly on weekends, but the remaining devices do not show significant differences in terms of flow size, packet count, and active duration. Here we identify and quantify the encounter-traffic correlation over )UDFWLRQRISDLUV weekdays/weekends for the first time.…”

“…We use NetFlow traces to analyze traffic behavior of user devices. In [21], we analyzed traffic on an individual level. We found cellos to generate 2x more flows than flutes, while the flute flows are 2.5x larger.…”

“…This is explained by observing significantly reduced mobility of devices on weekends. For example, median radius of gyration for cellos drops by 66%, and by 15% for flutes [21]. In addition to decreased mobility, most activity is clustered around several academic buildings with research labs (33% of APs handle no flute traffic on weekends, while 56% receive no cello traffic).…”

Where Are You Going Next?

“…Other entropy based studies include vehicular mobility [8][9][10], online social behavior [11,12], complex systems [13], cellular network traffic [14] and public transport utilization [15]. In addition, the devices' form factor affects the mode of usage and varied traffic profiles ( [16][17][18][19]), but these studies either do not consider predictability or do not account for different spatio-temporal resolutions. We have chosen our methods based on the literature to measure and compare both theoretical and practical limits of predictability for "on-the-go" Flutes and "sit-to-use" Cellos, with varying degrees of spatiotemporal granularity, while also looking at the correlation of prediction accuracy with mobility and network traffic profiles using extensive fine-granularity traces (based on our earlier work in [19]).…”

Route Selection and Trajectory Filling in Mobility Traces

“…In Chapter 2 of the dissertation, we present a review of the literature in terms of the usage of mobility traces and the issues faced when working with them. For the former, we highlight many studies ranging from analysis about how the mobility affects the connectivity of the network in large-scale urban environments [Alipour et al 2018, Cotta et al 2017, Xia et al 2017, Hou et al 2016, to the effects of mobility to the consumption of services in the network [Lu et al 2018, Ç atay andKeskin 2017], the mobility prediction of users [Sadri et al 2017, Qiao et al 2017b, to the general analysis of human mobility for various purposes [Wang et al 2018a, Wang et al 2018b, Garcia et al 2018, Yao 2018, Diniz et al 2017, Lu et al 2017, Qiao et al 2017a, Xia et al 2017.…”