dc.contributor.author |
Demestichas, KP |
en |
dc.contributor.author |
Koutsorodi, AA |
en |
dc.contributor.author |
Adamopoulou, EF |
en |
dc.contributor.author |
Theologou, ME |
en |
dc.date.accessioned |
2014-03-01T01:28:47Z |
|
dc.date.available |
2014-03-01T01:28:47Z |
|
dc.date.issued |
2008 |
en |
dc.identifier.issn |
1022-0038 |
en |
dc.identifier.uri |
https://dspace.lib.ntua.gr/xmlui/handle/123456789/18971 |
|
dc.subject |
Access selection |
en |
dc.subject |
Bayesian networks |
en |
dc.subject |
Heterogeneous networks |
en |
dc.subject |
Preference modelling |
en |
dc.subject |
User profiling |
en |
dc.subject.classification |
Computer Science, Information Systems |
en |
dc.subject.classification |
Engineering, Electrical & Electronic |
en |
dc.subject.classification |
Telecommunications |
en |
dc.subject.other |
Bayesian networks |
en |
dc.subject.other |
Computer architecture |
en |
dc.subject.other |
Inference engines |
en |
dc.subject.other |
Learning algorithms |
en |
dc.subject.other |
Access network (Anet) |
en |
dc.subject.other |
access selection |
en |
dc.subject.other |
Bayesian |
en |
dc.subject.other |
Business media |
en |
dc.subject.other |
Greedy algorithms |
en |
dc.subject.other |
Heterogeneous environments |
en |
dc.subject.other |
management architectures |
en |
dc.subject.other |
Optimal allocation |
en |
dc.subject.other |
Quality levels |
en |
dc.subject.other |
simulation results |
en |
dc.subject.other |
Springer (CO) |
en |
dc.subject.other |
Usage context |
en |
dc.subject.other |
Usage patterns |
en |
dc.subject.other |
User preferences |
en |
dc.subject.other |
Network architecture |
en |
dc.title |
Modelling user preferences and configuring services in B3G devices |
en |
heal.type |
journalArticle |
en |
heal.identifier.primary |
10.1007/s11276-007-0044-7 |
en |
heal.identifier.secondary |
http://dx.doi.org/10.1007/s11276-007-0044-7 |
en |
heal.language |
English |
en |
heal.publicationDate |
2008 |
en |
heal.abstract |
This paper discusses a management architecture for devices operating in heterogeneous environments, that enables access network selection through terminal-controlled, preference-based mechanisms. In this domain two problems are identified, mathematically formulated and solved: Intelligent Access Selection (IAS) and Modelling and Adaptation to User Preferences (MAUP). Their objective is to compute the optimal allocation of services to access networks and quality levels, and to dynamically determine user preferences according to the usage context, respectively. A greedy algorithm is proposed for the IAS problem, while the MAUP problem is handled through the construction of a Bayesian network that allows inference and learning of profile and usage patterns. Extensive simulation results of the proposed methods and algorithms are also presented. © 2007 Springer Science+Business Media, LLC. |
en |
heal.publisher |
SPRINGER |
en |
heal.journalName |
Wireless Networks |
en |
dc.identifier.doi |
10.1007/s11276-007-0044-7 |
en |
dc.identifier.isi |
ISI:000257394500011 |
en |
dc.identifier.volume |
14 |
en |
dc.identifier.issue |
5 |
en |
dc.identifier.spage |
699 |
en |
dc.identifier.epage |
713 |
en |