Ad-Hoc网络中数据扩散的人类邻近动力学研究
On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks
摘要：
我们报告的数据驱动的调查，旨在了解在现实世界中的人类接近的动态网络的消息传播的动态。我们使用通过可穿戴传感器的接近传感网络收集的数据，这些传感器部署在三个不同的社交聚会上，同时涉及数百人。我们模拟的消息传播过程中记录的邻近网络，专注于拓扑和时间属性。我们表明，通过使用适当的技术来处理邻近事件的时间异质性，出现了一个通用的统计模式的消息的传递时间，在所有的数据集强大。我们的研究结果是有用的数据传播的通用过程设置约束，以及验证已建立的模型，经常用于模拟现实的行为，人类的流动性和接近。
英文摘要：
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.
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