Analysis of wireless network access logs for a hierarchical characterization of user mobility
Francisco Talavera, Isaac Lera, Carlos Guerrero

TL;DR
This paper introduces a hierarchical user mobility model derived from Wi-Fi access logs, enabling scalable and adaptable simulation of user movement patterns in large-scale scenarios.
Contribution
It proposes a hierarchical modeling approach using clustering and transition matrices to better represent user mobility from Wi-Fi data.
Findings
Hierarchical model reduces complexity compared to non-hierarchical model.
Good accuracy in transition matrices, but time vector needs improvement.
Lower complexity achieved with multiple levels of granularity.
Abstract
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…
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