Matrix analysis
ACM Computing Surveys (CSUR)
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
Model T: an empirical model for user registration patterns in a campus wireless LAN
Proceedings of the 11th annual international conference on Mobile computing and networking
Extracting places from traces of locations
ACM SIGMOBILE Mobile Computing and Communications Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Periodic properties of user mobility and access-point popularity
Personal and Ubiquitous Computing
Profile-cast: behavior-aware mobile networking
ACM SIGMOBILE Mobile Computing and Communications Review
Delay management in delay-tolerant networks
International Journal of Network Management
Using Context Annotated Mobility Profiles to Recruit Data Collectors in Participatory Sensing
LoCA '09 Proceedings of the 4th International Symposium on Location and Context Awareness
RENA: region-based routing in intermittently connected mobile network
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Human behavior and challenges of anonymizing WLAN traces
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
PROTECT: proximity-based trust-advisor using encounters for mobile societies
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Similarity analysis and modeling in mobile societies: the missing link
Proceedings of the 5th ACM workshop on Challenged networks
Data-driven co-clustering model of internet usage in large mobile societies
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Proximity-based trust-advisor using encounters
ACM SIGMOBILE Mobile Computing and Communications Review
Spectral analysis of periodicity and regularity for mobile encounters in delay tolerant networks
ACM SIGMOBILE Mobile Computing and Communications Review
Trace-based mobility modeling for multi-hop wireless networks
Computer Communications
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Gauging human mobility characteristics and its impact on mobile routing performance
International Journal of Sensor Networks
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Mining user similarity based on routine activities
Information Sciences: an International Journal
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
Empirical study of routine structure in university campus
OCSC'13 Proceedings of the 5th international conference on Online Communities and Social Computing
Analysing the mobility, predictability and evolution of WLAN users
International Journal of Autonomous and Adaptive Communications Systems
Discovering periodic patterns of nodal encounters in mobile networks
Pervasive and Mobile Computing
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Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore 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 and services. 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. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.