This paper presents a method that generates a hierarchical user mobility model from the analysis of the data available from Wi-Fi connections. The data obtained from the Wi-Fi infrastructure is defined in terms of the coverage areas of the access points that the users move through. These access points are recursively grouped into different levels of granularity based on their geospatial features. The track of a user is defined as a sequence of Wi-Fi access points, which is enough to simulate user mobility in, for example, fog scenarios. The hierarchical definition of the region under study is proposed to reduce the complexity of the model in high-scale scenarios and to increase the adaptability between scenarios with different geospatial features. The model creation is based on a user profiling method that uses a clustering algorithm and each user type is defined with a transition matrix between coverage areas and a time length vector for the areas. The method is applied to the case of the campus of the University of the Balearic Islands. From the analysis of the mean square error of the results, we determined that the proposed method obtains good results for the transition matrices, but that the time vector definition should be improved. The results also show lower complexity in the case of the hierarchical model, with one area for each building and three levels, in regard to a non-hierarchical model, with only one area and one level for the whole campus.

翻译：本文提出一种方法，通过分析Wi-Fi连接中的可用数据生成层次化用户移动性模型。从Wi-Fi基础设施获取的数据以用户经过的接入点覆盖区域来定义。这些接入点根据其地理空间特征被递归划分为不同粒度的层级。用户轨迹被定义为Wi-Fi接入点序列，这足以在雾计算等场景中模拟用户移动性。为降低大规模场景下模型复杂度，并增强不同地理空间特征场景间的适应性，本文提出了研究区域的层次化定义方案。模型构建基于用户特征提取方法，该方法采用聚类算法，每个用户类型通过覆盖区域间的转移矩阵和区域驻留时间向量来定义。该方法已应用于巴利阿里群岛大学校园案例。通过均方误差分析，我们确定所提方法在转移矩阵方面取得了良好效果，但时间向量的定义有待改进。结果表明，与覆盖整个校园的单区域单层级非层次化模型相比，采用每个建筑一个区域、共三层级的层次化模型复杂度更低。