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The sociable traveller: human travelling patterns in social-based mobility

机译:社交旅行者:基于社交的出行方式中的人类出行方式

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摘要

Understanding how humans move is a key factor for the design and evaluation of networking protocols and mobility management solutions in mobile networks. This is particularly true for mobile scenarios in which conventional singlehop access to the infrastructure is not always possible, and multi-hop wireless forwarding is a must. We specifically focus on one of the most recent mobile networking paradigms, i.e., opportunistic networks. In this paradigm the communication takes place directly between the personal devices (e.g., smartphones and PDAs) that the users carry with them during their daily activities, without any assumption about pre-existing infrastructures. Among all mobility characteristics that may affect the performance of opportunistic networks, the usersu27 travelling patterns have recently gained a lot of attention due to their impact on the spreading of both viruses and messages in such a network. In this paper we consider a social-based mobility model (HCMM) and we extend this model to account for the typical travelling behaviour of users. To the best of our knowledge, the resulting mobility model is the first model in which movements driven by social relations also match statistical features of travelling patterns as measured in reality. Finally, we evaluate our proposal through simulations over a wide range of scenarios, emphasizing the effect of finite sampling on the obtained results.
机译:了解人类的活动方式是设计和评估移动网络中的网络协议和移动性管理解决方案的关键因素。对于移动场景而言尤其如此,在这种情况下,对基础结构的常规单跳访问并非总是可能的,因此必须进行多跳无线转发。我们特别关注于最新的移动网络范例之一,即机会网络。在该范例中,通信直接在用户在日常活动中随身携带的个人设备(例如,智能手机和PDA)之间进行,而无需任何关于现有基础设施的假设。在可能影响机会网络性能的所有移动性特征中,由于用户的出行方式对此类网络中病毒和消息的传播都产生了影响,因此最近引起了广泛关注。在本文中,我们考虑了一种基于社交的移动性模型(HCMM),并将其扩展为考虑用户的典型出行行为。据我们所知,由此产生的流动性模型是第一个模型,其中由社会关系驱动的运动也与实际测量的出行方式的统计特征相匹配。最后,我们通过在各种情况下进行仿真来评估我们的建议,强调有限采样对所获得结果的影响。

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