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Structural Analysis of User Association Patterns in University Campus Wireless LANs

机译：高校校园无线局域网中用户关联模式的结构分析

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Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition. This method provides an efficient way to reduce important spatiotemporal dynamics in user mobility into a few eigen-behavior vectors. This also facilitates nodes to exchange their mobility summaries and determine their mutual similarity locally. Without any assumption on the properties of user population, we use unsupervised learning (clustering) techniques to classify WLAN users. Such a user grouping scheme based on learned user behavior is crucial for applications relying on the usage context of each mobile device (e.g., participatory sensing, social-relationship-aware message forwarding). In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses to showcase its efficacy. While our findings partly validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities and striking consistency of user behavior from the two universities. We discover multimodal user behavior for more than 60 percent of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distributio- .
机译：无线网络和个性化移动设备已深度集成并嵌入我们的生活中。如此广泛地采用新技术将影响用户行为，进而影响网络性能。必须刻画无线用户行为的基本结构，以建模，管理，利用和设计高效的移动网络。表征用户行为的一个主要挑战来自用户行为数据的巨大规模和复杂性。如果不进行总结和减少维度，那么庞大的数据量将无法提供很多有用的信息。本文的主要贡献是基于移动性偏好及其分解的矩阵表示的新颖相似性度量。此方法提供了一种有效的方法，可以将用户移动性中的重要时空动态降低为几个特征行为向量。这也便于节点交换其移动性摘要并在本地确定其相互相似性。在不对用户群体的性质进行任何假设的情况下，我们使用无监督学习（聚类）技术对WLAN用户进行分类。这样的基于学习到的用户行为的用户分组方案对于依赖于每个移动设备的使用上下文（例如，参与感测，社交关系感知消息转发）的应用是至关重要的。在这项研究中，我们使用系统的TRACE方法，通过广泛挖掘来自两个主要大学校园的无线网络日志来分析无线用户的行为模式，以展示其功效。尽管我们的发现部分验证了直观的重复性行为趋势和用户分组，但令人惊讶的是，从两所大学中发现了质的共性和用户行为的惊人一致性。我们发现超过60％的用户使用多模式用户行为，并且两个园区中都有数百个具有独特行为模式的不同群体。主要群体的规模遵循幂律分布。

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