research-article

A DBN approach for network availability prediction

Authors:

Upendra Rathnayake,

Maximilian Ott,

Aruna SeneviratneAuthors Info & Claims

MSWiM '09: Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

Pages 181 - 187

https://doi.org/10.1145/1641804.1641836

Published: 26 October 2009 Publication History

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

[1]

A. Rahmati and L. Zhong, "Context-for-Wireless: Context-Sensitive Energy-Efficient Wireless Data Transfer", in Proc. ACM/USENIX MobiSys, June, 2007.

Digital Library

[2]

Y. Vanrompay, P. Rigole, Y. Berbers "Predicting network connectivity for context-aware pervasive systems with localized network availability", in WoSSIoT'07, a workshop of EuroSys, March, 2007.

[3]

Upendra Rathnayake and Max Ott, "Predicting Network Availability Using User Context", in Proceedings of ACM MobiQuitous '08, Dublin, Ireland, July, 2008.

Digital Library

[4]

C. Doss, R., A. Jennings, N. Shenoy, "A Review of Current work on Mobility Prediction in Wireless Networks", ACM AMOC,Thailand, 2004.

[5]

L. Song, D. Kotz, R. Jain and X. He, "Evaluating location predictors with extensive Wi-Fi mobility data", In Proceedings of the 23rd Annual Conference INFOCOM, pages 1414--1424, March, 2004.

[6]

M. Kim and D. Kotz and S. Kim, "Extracting a mobility model from real user traces", In Proceedings of the 25th Annual Conference of INFOCOM, Barcelona, Spain, April, 2006.

[7]

E. Exposito, R. Malaney, X. Wei, D. Nghia, "Using the XQoS Platform for designing and developing the QoS-Seeker System", In the proceeding of the 3rd International IEEE Conference on Industrial Informatics (INDIN), Perth, Australia, 2005.

[8]

N. Samaan and A. Karmouch, "A Mobility Prediction Architecture Based on Contextual Knowledge and Spatial Conceptual Maps", IEEE Transaction on Mobile Computing 4(6): 537--551 Nov/Dec 2005

Digital Library

[9]

F. Erbas, J. Steuer, D.Eggeiseker, K. Kyamakya and K. Jobmann, "A Regular Path Recognition Method and Prediction of User Movements in Wireless Networks", Proceedings of Vehicular Technology Conference, VTC, October 2001.

[10]

Z. R. Zaidi and B. L. Mark, "Mobility Estimation for Wireless Networks Based on an Autoregressive Model," in Proc. IEEE Globecom 2004, Dallas, Texas, December 2004.

[11]

Michael I. Jordan, Christopher M. Bishop. "An Introduction to Graphical Models" (Book Draft)

[12]

Lawrence R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proc. IEEE, vol. 77, No. 2 Feb., 1989, pp. 257--286.

[13]

Murphy, K., "Dynamic Bayesian Networks: Representation, inference and Learning", Phd Thesis, UC Berkeley, Computer Science Division, July 2002.

Digital Library

[14]

M. Zaharia and S. Keshav, "Fast and Optimal Scheduling Over Multiple Network Interfaces",University of Waterloo Technical Report CS-2007-36, October 2007.

[15]

J. Ding, X. Li, N. Jiang, Kramer, B.J., Davoli, F., "Prediction Strategies for Proactive Management in Dynamic Distributed Systems", International Conference on Digital Telecommunications, 2006. 29-31 Aug. 2006.

Digital Library

[16]

Li Feng, Wei Wang, Lina Zhu, and Yi Zhang, "Predicting intrusion goal using dynamic Bayesian network with transfer probability estimation", Journal of Network and Computer Applications, Vol. 32, Issue 3, May 2009, pp. 721--732.

Digital Library

Cited By

Meng QFang XYue WMeng YWei J(2018)Bayesian Network Prediction of Mobile User Throughput in 5G Wireless Networks2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)10.1109/ICCCAS.2018.8768972(291-295)Online publication date: Dec-2018
https://doi.org/10.1109/ICCCAS.2018.8768972
Alawi MAlsaqour RIsmail MSundararajan EAbdelhaq M(2018)Vehicular Wi-Fi Offloading in Heterogeneous Vehicular NetworksMobile Networks and Applications10.1007/s11036-017-0916-823:3(560-579)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11036-017-0916-8
Rathnayake UPetander HOtt MSeneviratne A(2018)EMUNEMobile Networks and Applications10.1007/s11036-011-0332-417:2(216-233)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s11036-011-0332-4
Show More Cited By

Index Terms

A DBN approach for network availability prediction
1. Mathematics of computing
  1. Probability and statistics
    1. Multivariate statistics
2. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

MSWiM '09: Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems

October 2009

438 pages

ISBN:9781605586168

DOI:10.1145/1641804

General Chairs:
Nael Abu-Ghazaleh
State University of New York at Binghamton, USA
,
Luciano Bononi
University of Bologna, Italy
,
Program Chairs:
Brahim Bensaou
The Hong Kong University of Science and Technology, Hong Kong
,
Violet R. Syrotiuk
Arizona State University, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2009

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MSWiM '09: The 12th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

October 26 - 29, 2009

Tenerife, Canary Islands, Spain

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Overall Acceptance Rate 398 of 1,577 submissions, 25%

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Cited By

Meng QFang XYue WMeng YWei J(2018)Bayesian Network Prediction of Mobile User Throughput in 5G Wireless Networks2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)10.1109/ICCCAS.2018.8768972(291-295)Online publication date: Dec-2018
https://doi.org/10.1109/ICCCAS.2018.8768972
Alawi MAlsaqour RIsmail MSundararajan EAbdelhaq M(2018)Vehicular Wi-Fi Offloading in Heterogeneous Vehicular NetworksMobile Networks and Applications10.1007/s11036-017-0916-823:3(560-579)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s11036-017-0916-8
Rathnayake UPetander HOtt MSeneviratne A(2018)EMUNEMobile Networks and Applications10.1007/s11036-011-0332-417:2(216-233)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s11036-011-0332-4
Erman BYiu S(2016)Modeling 5G wireless network service reliability prediction with bayesian network2016 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR 2016)10.1109/CQR.2016.7501417(1-6)Online publication date: May-2016
https://doi.org/10.1109/CQR.2016.7501417
Pedrasa JSeneviratne A(2012)Distributed network resource discovery on a mobile deviceTENCON 2012 IEEE Region 10 Conference10.1109/TENCON.2012.6412337(1-6)Online publication date: Nov-2012
https://doi.org/10.1109/TENCON.2012.6412337
Seneviratne APedrasa JRathnayake U(2011)Network availability prediction: Can it be done?Global Information Infrastructure Symposium - GIIS 201110.1109/GIIS.2011.6026700(1-6)Online publication date: Aug-2011
https://doi.org/10.1109/GIIS.2011.6026700
Pedrasa JSeneviratne A(2011)Determining network availability on the moveThe 17th Asia Pacific Conference on Communications10.1109/APCC.2011.6152823(301-306)Online publication date: Oct-2011
https://doi.org/10.1109/APCC.2011.6152823
Rathnayake UIftikhar MOtt MSeneviratne A(2011)Realistic data transfer scheduling with uncertaintyComputer Communications10.1016/j.comcom.2010.02.01234:9(1055-1065)Online publication date: Jun-2011
https://doi.org/10.1016/j.comcom.2010.02.012
Rathnayake UPetander HOtt MSeneviratne A(2010)Protocol support for bulk transfer architecture2010 IEEE International Conference on Wireless Communications, Networking and Information Security10.1109/WCINS.2010.5545343(598-602)Online publication date: Jun-2010
https://doi.org/10.1109/WCINS.2010.5545343
Rathnayake UIftikhar MOtt MSeneviratne A(2010)Mobile Data Transfer Scheduling with Uncertainty2010 IEEE International Conference on Communications10.1109/ICC.2010.5502368(1-6)Online publication date: May-2010
https://doi.org/10.1109/ICC.2010.5502368

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