Modeling Time-Variant User Mobility in Wireless Mobile Networks
Modeling Time-Variant User Mobility in Wireless Mobile Networks
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DOI：
10.1109/infcom.2007.94
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发表时间：
2007-05
期刊：
影响因子：
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通讯作者：
Wei-jen Hsu;T. Spyropoulos;K. Psounis;A. Helmy
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文献类型：
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作者：
Wei-jen Hsu;T. Spyropoulos;K. Psounis;A. Helmy
现实的移动性模型对于理解无线自组织网络中路由协议的性能非常重要，特别是当采用移动性辅助路由方案时，例如在容忍延迟网络（DTNs）中。在移动性辅助路由中，消息存储在移动节点中，并通过节点移动性在网络中传输。因此，消息传递中涉及的延迟与节点移动性的属性紧密耦合。目前常用的流动性模型是简单的随机i.i.d.模型，不能反映现实的流动性特征。本文提出了一种新的时变社区流动模型。在该模型中，我们定义了节点经常访问的社区，以捕获倾斜的位置访问偏好，并使用具有不同移动性参数的时间段来创建节点在同一位置的周期性重新出现。基于对经验WLAN轨迹的分析，我们已经清楚地观察到这两个特性。除了提出一个现实的流动性模型外，我们还推导出了分析表达式，以强调如果纳入这些流动性特征，对撞击时间和会议时间的影响。这些数量依次决定了移动辅助路由设置中的数据包传递延迟。仿真研究表明，我们的表达式误差始终在20%以下，80%的研究案例误差在10%以下。
Realistic mobility models are important to understand the performance of routing protocols in wireless ad hoc networks, especially when mobility-assisted routing schemes are employed, which is the case, for example, in delay-tolerant networks (DTNs). In mobility-assisted routing, messages are stored in mobile nodes and carried across the network with nodal mobility. Hence, the delay involved in message delivery is tightly coupled with the properties of nodal mobility. Currently, commonly used mobility models are simplistic random i.i.d. model that do not reflect realistic mobility characteristics. In this paper we propose a novel time-variant community mobility model. In this model, we define communities that are visited often by the nodes to capture skewed location visiting preferences, and use time periods with different mobility parameters to create periodical re-appearance of nodes at the same location. We have clearly observed these two properties based on analysis of empirical WLAN traces. In addition to the proposal of a realistic mobility model, we derive analytical expressions to highlight the impact on the hitting time and meeting times if these mobility characteristics are incorporated. These quantities in turn determine the packet delivery delay in mobility-assisted routing settings. Simulation studies show our expressions have error always under 20%, and in 80% of studied cases under 10%.