Access and mobility of wireless PDA users
ACM SIGMOBILE Mobile Computing and Communications Review
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Model T++: an empirical joint space-time registration model
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Context-for-wireless: context-sensitive energy-efficient wireless data transfer
Proceedings of the 5th international conference on Mobile systems, applications and services
Network Resource Awareness and Control in Mobile Applications
IEEE Internet Computing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Xensible interruptions from your mobile phone
Proceedings of the 9th international conference on Human computer interaction with mobile devices and services
Clustering and prediction of mobile user routes from cellular data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Predicting mobility events on personal devices
Pervasive and Mobile Computing
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Over the years, personal mobile devices have obtained increasing capabilities to concurrently connect to surrounding networks and nearby other devices. Good knowledge on the dynamics in the availability of these heterogeneous entities constitutes essential input for various data communication problems, ranging from the adaptation of applications on mobile devices to multi-homed situations, to the optimization of routing protocols for delay tolerant networks. In this paper, we focus on a method for the prediction in time of the loss in visibility of currently in-range network entities, as observed on a personal mobile device. We are interested in estimating the full probability density function of the time of these out-of-range events, as this allows us to ask arbitrary questions such as: what is the probability of losing connection X in the next Y minutes? To do so, we model the mobility of the user by applying kernel density estimation on previously observed mobility traces collected during a user experiment we ran with 12 participants in a six week period, logging cellular, 802.11 wireless LAN, Bluetooth, and various other events on the participant's mobile device.