A Practical User Mobility Prediction Algorithm for Supporting Adaptive QoS in Wireless Networks
ICON '99 Proceedings of the 7th IEEE International Conference on Networks
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
Model T++: an empirical joint space-time registration model
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Mining call and mobility data to improve paging efficiency in cellular networks
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Mining behavioral groups in large wireless LANs
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
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With the proliferation of numerous light weight devices along with the widespread use of Wireless Local Area Networks WLANs in many public places, we are now connected on-the-go more than ever. Such change, in device technology and coverage ubiquity, results in unexplored dynamics and raises several challenging questions. How are these changes affecting the behaviour of mobile users? And how do these changes affect mobile user predictability and the networking protocols that utilise it? To shed light on the changes and how protocols involving the mobility of users can change, we follow a systematic analysis methodology. First, using a three-year long network trace, we study user mobility and its effects on predictability of regular and ultra-mobile users, by analysing the contrast between the mobility of the WLAN users, and four carefully selected sets of ultra-mobile users across various mobility metrics. We also investigate how these differences in mobility affect the predictability of such users' next locations. Then, we study the evolution of user mobility using extensive network traces over five years, and also investigate a series of prediction methods to analyse the evolution of prediction accuracy of these WLAN users. This study of user mobility and predictability paves the way for better understanding of present day mobile users, and gives us insight into the potential evolution of network users' behaviour in the coming future.