大型无线局域网中的行为群挖掘
Mining Behavioral Groups in Large Wireless LANs
摘要：
未来无线网络的一个愿景是，它们将深深地融入我们的生活，并将涉及个性化移动设备的使用。在这样的网络中的用户行为必然会影响网络性能。为了对移动网络进行建模、管理、利用和设计，必须研究和描述无线用户行为的基本结构。同样重要的是，要根据从现有部署的无线网络收集的广泛测量结果，尽可能真实地进行此类研究。在这项研究中，使用我们的系统跟踪方法，我们分析了无线用户的行为模式，广泛挖掘无线网络日志从两个主要的大学校园。我们使用位置偏好向量来表示数据，并利用无监督学习（聚类）来使用新的相似性度量对用户行为的趋势进行分类。矩阵分解技术用于识别（和区分）主要模式。虽然我们的研究结果验证了直观的重复行为趋势和用户分组，但令人惊讶的是，两所大学的用户行为存在定性的共性。我们发现超过60%的用户具有多模式用户行为，并且在两个校园中有数百个不同的群体具有独特的行为模式。主要群体的规模遵循幂律分布。我们的方法和研究结果提供了一个重要的一步，网络管理和行为感知网络协议和应用程序，仅举几例。
英文摘要：
One vision of future wireless networks is that they will be deeply integrated and embedded in our lives and will involve the use of personalized mobile devices. User behavior in such networks is bound to affect the network performance. It is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. It is also important to make such study as realistic as possible, based on extensive measurements collected from existing deployed wireless networks. 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. We represent the data using location preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. While our findings validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities of user behaviors from the two universities. We discover multi-modal user behavior for more than 60% 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 distribution. Our methods and findings provide an essential step towards network management and behavior-aware network protocols and applications, to name a few.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
