A DBN approach for network availability prediction

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A DBN approach for network availability prediction

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International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems archive
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems table of contents

Tenerife, Canary Islands, Spain

SESSION: Prediction table of contents

Pages 181-187

Year of Publication: 2009

ISBN:978-1-60558-616-8

Authors

Upendra Rathnayake	The University of NSW, Sydney, Australia
Maximilian Ott	NICTA, Sydney, Australia
Aruna Seneviratne	The University of NSW, Sydney, Australia

Sponsor

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ACM New York, NY, USA

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ABSTRACT

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. When the predictions were performed for WLAN availability with the real user data collected in our experiments, this model shows 20% or more improvement than both of our earlier proposals of order 1 and 2 Semi-Markov models.

REFERENCES

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