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.
Yoneki @cite_16 points out the importance of collecting real world data when modeling contact networks. Most available data sets that cover the short-range scale use Bluetooth or WiFi technologies to measure device proximity @cite_16 . @cite_24 extract mobility models from user traces, focusing on node localization and path tracing: the analyzed characteristics are node speeds and pause times that follow a log-normal distribution. @cite_5 present an experiment that involved about 40 participants at the Infocom 2005 conference, and report power-law distributions for the time intervals between node contacts.
@cite_24 @cite_5 @cite_16
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Understanding user mobility is critical for simula- tions of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17 . Pocket Switched Networks (PSN) make use of both human mobility and local global connectivity in order to transfer data between mobile users' devices. This falls under the Delay Tolerant Networking (DTN) space, focusing on the use of opportunistic networking. One key problem in PSN is in designing forwarding algorithms which cope with human mobility patterns. We present an experiment measuring forty-one humans' mobility at the Infocom 2005 conference. The results of this experiment are similar to our previous experiments in corporate and academic working environments, in exhibiting a power-law distrbution for the time between node contacts. We then discuss the implications of these results on the design of forwarding algorithms for PSN. The recently developed small wireless devices ranging from sensor boards to mobile phones provide a timely opportunity to gather unique data sets on complex human interactions, which in turn will support rich and meaningful modelling of the underlying networks. We are convinced that this approach will be fruitful and effective for tackling various issues such as infectious disease spread and beyond.Human social dynamics are far more complex than the currentsimplified models in network theory, and the data drivenmodelling with large-scale experimental results is essential for understanding and building systems that exploit real networks. An important issue to be addressed for taking empirical and heuristic approach is understanding the characteristics of data such as data collection methods, limitations, scale of noise, and so forth. This aspect has been many times neglected duringinference of data. We express the importance of data drivenapproach in this paper.