2,522 research outputs found
Why It Takes So Long to Connect to a WiFi Access Point
Today's WiFi networks deliver a large fraction of traffic. However, the
performance and quality of WiFi networks are still far from satisfactory. Among
many popular quality metrics (throughput, latency), the probability of
successfully connecting to WiFi APs and the time cost of the WiFi connection
set-up process are the two of the most critical metrics that affect WiFi users'
experience. To understand the WiFi connection set-up process in real-world
settings, we carry out measurement studies on million mobile users from
representative cities associating with million APs in billion WiFi
sessions, collected from a mobile "WiFi Manager" App that tops the Android/iOS
App market. To the best of our knowledge, we are the first to do such large
scale study on: how large the WiFi connection set-up time cost is, what factors
affect the WiFi connection set-up process, and what can be done to reduce the
WiFi connection set-up time cost. Based on the measurement analysis, we develop
a machine learning based AP selection strategy that can significantly improve
WiFi connection set-up performance, against the conventional strategy purely
based on signal strength, by reducing the connection set-up failures from
to and reducing time costs of the connection set-up
processes by more than times.Comment: 11pages, conferenc
On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study
Mobility and network traffic have been traditionally studied separately.
Their interaction is vital for generations of future mobile services and
effective caching, but has not been studied in depth with real-world big data.
In this paper, we characterize mobility encounters and study the correlation
between encounters and web traffic profiles using large-scale datasets (30TB in
size) of WiFi and NetFlow traces. The analysis quantifies these correlations
for the first time, across spatio-temporal dimensions, for device types grouped
into on-the-go Flutes and sit-to-use Cellos. The results consistently show a
clear relation between mobility encounters and traffic across different
buildings over multiple days, with encountered pairs showing higher traffic
similarity than non-encountered pairs, and long encounters being associated
with the highest similarity. We also investigate the feasibility of learning
encounters through web traffic profiles, with implications for dissemination
protocols, and contact tracing. This provides a compelling case to integrate
both mobility and web traffic dimensions in future models, not only at an
individual level, but also at pairwise and collective levels. We have released
samples of code and data used in this study on GitHub, to support
reproducibility and encourage further research
(https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3
adds GitHub lin
MoMo: a group mobility model for future generation mobile wireless networks
Existing group mobility models were not designed to meet the requirements for
accurate simulation of current and future short distance wireless networks
scenarios, that need, in particular, accurate, up-to-date informa- tion on the
position of each node in the network, combined with a simple and flexible
approach to group mobility modeling. A new model for group mobility in wireless
networks, named MoMo, is proposed in this paper, based on the combination of a
memory-based individual mobility model with a flexible group behavior model.
MoMo is capable of accurately describing all mobility scenarios, from
individual mobility, in which nodes move inde- pendently one from the other, to
tight group mobility, where mobility patterns of different nodes are strictly
correlated. A new set of intrinsic properties for a mobility model is proposed
and adopted in the analysis and comparison of MoMo with existing models. Next,
MoMo is compared with existing group mobility models in a typical 5G network
scenario, in which a set of mobile nodes cooperate in the realization of a
distributed MIMO link. Results show that MoMo leads to accurate, robust and
flexible modeling of mobility of groups of nodes in discrete event simulators,
making it suitable for the performance evaluation of networking protocols and
resource allocation algorithms in the wide range of network scenarios expected
to characterize 5G networks.Comment: 25 pages, 17 figure
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