IMPACT：使用WLAN跟踪分析对大学校园移动用户模式的调查
IMPACT: Investigation of Mobile-user Patterns Across University Campuses using WLAN Trace Analysis
摘要：
我们进行了迄今为止最全面的WLAN痕迹研究。从四个主要的大学校园收集的测量进行了分析，其目的是在无线网络中发展的现实用户行为的基本理解。个人用户和节点间（组）的行为进行了调查，并设计了两类指标来捕捉这种行为的底层结构。对于个人用户的行为，我们观察到不同的模式，其中大多数用户是“上”的一小部分的时间，访问的接入点的数量是非常小的，整体在线用户的移动性是相当低的。我们明确区分重度用户和轻度用户。一般来说，用户在几天和几周内表现出高度的相似性。对于群体行为，我们定义了相遇模式和友谊的指标。令人惊讶的是，我们发现一个用户平均在一个月内遇到不到6%的网络用户群体，并且遇到和友谊关系是高度不对称的。我们建立的相遇次数遵循双帕累托分布，而友谊指数遵循指数分布。我们使用一个小的世界模型来捕捉相遇图，其特征在仅仅一天后就达到稳定状态。我们希望我们的研究在无线网络中的网络使用和移动模式的现实建模有很大的影响。
英文摘要：
We conduct the most comprehensive study of WLAN traces to date. Measurements collected from four major university campuses are analyzed with the aim of developing fundamental understanding of realistic user behavior in wireless networks. Both individual user and inter-node (group) behaviors are investigated and two classes of metrics are devised to capture the underlying structure of such behaviors. For individual user behavior we observe distinct patterns in which most users are 'on' for a small fraction of the time, the number of access points visited is very small and the overall on-line user mobility is quite low. We clearly identify categories of heavy and light users. In general, users exhibit high degree of similarity over days and weeks. For group behavior, we define metrics for encounter patterns and friendship. Surprisingly, we find that a user, on average, encounters less than 6% of the network user population within a month, and that encounter and friendship relations are highly asymmetric. We establish that number of encounters follows a biPareto distribution, while friendship indexes follow an exponential distribution. We capture the encounter graph using a small world model, the characteristics of which reach steady state after only one day. We hope for our study to have a great impact on realistic modeling of network usage and mobility patterns in wireless networks.
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