95,115 research outputs found
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
Homesick L\'evy walk: A mobility model having Ichi-go Ichi-e and scale-free properties of human encounters
In recent years, mobility models have been reconsidered based on findings by
analyzing some big datasets collected by GPS sensors, cellphone call records,
and Geotagging. To understand the fundamental statistical properties of the
frequency of serendipitous human encounters, we conducted experiments to
collect long-term data on human contact using short-range wireless
communication devices which many people frequently carry in daily life. By
analyzing the data we showed that the majority of human encounters occur
once-in-an-experimental-period: they are Ichi-go Ichi-e. We also found that the
remaining more frequent encounters obey a power-law distribution: they are
scale-free. To theoretically find the origin of these properties, we introduced
as a minimal human mobility model, Homesick L\'evy walk, where the walker
stochastically selects moving long distances as well as L\'evy walk or
returning back home. Using numerical simulations and a simple mean-field
theory, we offer a theoretical explanation for the properties to validate the
mobility model. The proposed model is helpful for evaluating long-term
performance of routing protocols in delay tolerant networks and mobile
opportunistic networks better since some utility-based protocols select nodes
with frequent encounters for message transfer.Comment: 8 pages, 10 figure
Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling
In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods
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
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
We report on a data-driven investigation aimed at understanding the dynamics
of message spreading in a real-world dynamical network of human proximity. We
use data collected by means of a proximity-sensing network of wearable sensors
that we deployed at three different social gatherings, simultaneously involving
several hundred individuals. We simulate a message spreading process over the
recorded proximity network, focusing on both the topological and the temporal
properties. We show that by using an appropriate technique to deal with the
temporal heterogeneity of proximity events, a universal statistical pattern
emerges for the delivery times of messages, robust across all the data sets.
Our results are useful to set constraints for generic processes of data
dissemination, as well as to validate established models of human mobility and
proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data
diffusion in ad-hoc networks, Ad Hoc Netw. (2011
- …