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An enhanced community-based mobility model for distributed mobile social networks

机译:用于分布式移动社交网络的基于社区的增强型移动性模型

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Simulation is fundamental tool for the evaluation and validation of the applications and protocols in Mobile Social Networks. However, the limited number of real user traces available and the imposed restrictions of the specific scenarios, make generalization very hard. Therefore, the need has been created for synthetic mobility models. The widely used Random Way-Point Mobility Model has been proven unable to capture characteristics of human mobility such as the social attraction. Consequently, in recent years mobility models based on social network theory, able to capture the temporal and spatial dependencies of mobile social networks, are being designed. In this paper the Enhanced Community Mobility Model (ECMM) is introduced. It follows preceding community-based approaches, that map communities to a topological space. Its main contribution is the introduction of new features, such as pause periods and group mobility encouragement, lacking for previous community-based mobility models. Additionally, ECMM enables researchers to arbitrarily select a social model as the trace generation process input, while at the same time generates traces with high conformance to that social network. A comparison between synthetic traces, generated by ECMM, other community-based models and a number of real ones is provided for validation.
机译:仿真是评估和验证移动社交网络中的应用程序和协议的基本工具。但是,可用的实际用户跟踪数量有限,并且对特定方案施加的限制使泛化非常困难。因此,已经产生了对综合机动模型的需求。事实证明,广泛使用的随机路径点移动性模型无法捕获人类移动性的特征,例如社会吸引力。因此,近年来,正在设计基于社交网络理论的移动性模型,该模型能够捕获移动社交网络的时间和空间依赖性。本文介绍了增强型社区流动模型(ECMM)。它遵循先前的基于社区的方法,该方法将社区映射到拓扑空间。它的主要贡献是引入了新功能,例如暂停期和鼓励团体流动性,而以前的基于社区的流动性模型则缺乏这些功能。此外,ECMM使研究人员可以任意选择一种社交模型作为跟踪生成过程的输入,同时生成与该社交网络高度一致的跟踪。由ECMM,其他基于社区的模型和许多实际模型生成的合成跟踪之间的比较可用于验证。

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