PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Wireless Access Network Selection Enabled by Semantic Technologies
Carolina Fortuna, B. Ivan, Luka Bradesko, Blaz Fortuna and M. Mohorcic
In: ISWC 2009, 25-29 Oct 2009, Washington, USA.

Abstract

Service oriented access in a multi-application, multi-access network environment is faced with the problem of cross-layer interoperability among technologies. In this demo, we present a knowledge base (KB) which contains local (user terminal specific) knowledge that enables pro-active network selection by translating technology specific parameters to higher-level, more abstract parameters. We implemented a prototype which makes use of semantic technology (namely ResearchCyc) for creating the elements of the KB and uses reasoning to determine the best access network. The system implements technology-specific parameter mapping according to the IEEE 802.21 draft standard recommendation.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:6460
Deposited By:Jan Rupnik
Deposited On:08 March 2010