A community based mobility model for ad hoc network research
REALMAN '06 Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Human behavior and challenges of anonymizing WLAN traces
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Locating emergencies in a campus using wi-fi access point association data
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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The next frontier for sensor networks is sensing the human society. Several mobile societies are emerging, especially with wide deployment of wireless LANs (WLANs) on campuses. WLAN traces can provide much insight into mobile user behavior. Such insight is essential to develop realistic models and to design better networks, and analyze effects of social attributes on mobile network usage. The most extensive libraries of wireless traces are collected from university campuses, are anonymized and do not provide affiliation, gender or preference information explicitly. Hence, it becomes a challenge to analyze network usage characteristics for social groups using the existing traces. In this paper, we present two novel scientific techniques to classify WLAN users into social groups. The first technique uses mapping of the traces into buildings (e.g., dept. buildings, libraries, sororities and fraternities) to extract affiliation and gender information based on network usage statistics. The second technique utilizes directory information that can be linked to WLAN users to extract useful information. For example, usernames of the WLAN users (if available) can be used to find user's gender based on first name and databases. As a case study we perform classification and behavior analysis of users by gender. Extensive WLAN traces from two major universities are collected over three years and analyzed. Results from both the methods are cross-validated and show more than 90% correspondence. Results of gender classification are then used to examine usage patterns and preferences across gender groups, including spatio-temporal distribution of wireless on-line activity, study majors and vendor preference. In some cases these metrics are equal across genders, however, there are several interesting cases that clearly indicate statistically significant and consistent effects of gender; e.g., males have longer on-line sessions in Engineering and Music, while females have longer sessions in Social Sciences and Sports areas. At one university female groups consistently preferred Apple computers. These findings can have a great impact on several mobile networking applications; they can be directly used for realistic modeling of wireless user on-line behavior, mobility and virus susceptibility, and for designing socially-aware protocols and class-based or gender-based services, to name a few.