论文标题
机会网络中的人类流动性：特征，模型和预测方法
Human Mobility in Opportunistic Networks: Characteristics, Models and Prediction Methods
论文作者
论文摘要
机会性网络（OPPNET）是现代类型的间歇性连接网络，其中移动用户通过其短距离设备相互通信，以在有兴趣的观察者之间共享数据。在这种情况下，人类是移动设备的主要载体。因此，可以通过检索固有的用户习惯，兴趣和社交功能来利用这种移动性来模拟和评估各种情况。最近在文献中探讨了有关OPPNET中人类流动性的一些研究挑战。在本文中，我们对三个主要组中的人类流动性问题进行了详尽的调查（1）移动特征，（2）移动性模型和痕迹以及（3）移动性预测技术。首先，探索了人类运动的空间，时间和连通性。其次，总结了使用蓝牙/Wi-Fi技术或基于位置的社交网络捕获的实际移动轨迹。此外，基于仿真的移动性模型被分类，并且每个类别中的最新文章都被突出显示。第三，旨在预测人类流动性的三个方面的新的人类流动预测技术，即用户的下一次步行，停留持续时间和联系机会。总而言之，概述了一些主要的开放问题。
Opportunistic networks (OppNets) are modern types of intermittently connected networks in which mobile users communicate with each other via their short-range devices to share data among interested observers. In this setting, humans are the main carriers of mobile devices. As such, this mobility can be exploited by retrieving inherent user habits, interests, and social features for the simulation and evaluation of various scenarios. Several research challenges concerning human mobility in OppNets have been explored in the literature recently. In this paper, we present a thorough survey of human mobility issues in three main groups (1) mobility characteristics, (2) mobility models and traces, and (3) mobility prediction techniques. Firstly, spatial, temporal, and connectivity properties of human motion are explored. Secondly, real mobility traces which have been captured using Bluetooth/Wi-Fi technologies or location-based social networks are summarized. Furthermore, simulation-based mobility models are categorized and state-of-the art articles in each category are highlighted. Thirdly, new human mobility prediction techniques which aim to forecast the three aspects of human mobility, i.e., users' next walks, stay duration and contact opportunities are studied comparatively. To conclude, some major open issues are outlined.
