Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps
IEEE Transactions on Mobile Computing
Prediction Strategies for Proactive Management in Dynamic Distributed Systems
ICDT '06 Proceedings of the international conference on Digital Telecommunications
Context-for-wireless: context-sensitive energy-efficient wireless data transfer
Proceedings of the 5th international conference on Mobile systems, applications and services
Predicting intrusion goal using dynamic Bayesian network with transfer probability estimation
Journal of Network and Computer Applications
Predicting network availability using user context
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
| Hi-index | 0.00 |
Modern mobile devices are increasingly capable of simultaneously connecting to multiple access networks with different characteristics. Restricted coverage combined with user mobility will vary the availability of networks for a mobile device. Most proposed solutions for such an environment are reactive in nature, such as performing a vertical handover to the network that offers the highest bandwidth. But the cost of the handover may not be justified if that network is only available for a short time. Knowledge of future network availability and their capabilities are the basis for proactive schemes which will improve network selection and utilization. We have previously proposed a prediction model that can use any available context such as GSM Location Area, WLAN presence or even whether the power cable is plugged in, to predict network availability. As it may not be possible to sense all of the context variables that influence future network availability, in this paper we introduce a generic, new model incorporating a hidden variable to account for this. Specifically, we propose a Dynamic Bayesian Network based context prediction model to predict network availability. Predictions performed for WLAN availability with the real user data collected in our experiments show 20% or more improvement compared to both of our earlier proposals of order 1 and 2 semi-Markov models.