Issue No. 11 - Nov. (2012 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2011.254
Wei-Jen Hsu , University of Florida, Gainesville
Debojyoti Dutta , Cisco Systems, San Jose
Ahmed Helmy , University of Florida, Gainesville
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 distribution.
Wireless LAN, Mobile computing, Measurement, Sensors, Mobile handsets, Educational institutions, Mobile communication, wireless communication, Clustering analysis, mobile environments, mobile user behavior, network monitoring
D. Dutta, W. Hsu and A. Helmy, "Structural Analysis of User Association Patterns in University Campus Wireless LANs," in IEEE Transactions on Mobile Computing, vol. 11, no. , pp. 1734-1748, 2012.