"Scenario"-adaptivity for e-service management in heterogeneous networks

A "Situation-Location" aware system has the main task of handling the set of distributed information and resources available within heterogeneous networks to provide a user with seamless and optimized network access to the desired service from any location. A suitable description of Situation&Location and its whole set of variables can be a powerful means to trade-off among user requirements, application constraints, and underlying network conditions. We propose a Multi Agent System able to capture the concept of Situation&Location and perform the task highlighted above. We follow a general-purpose approach with the aim of guaranteeing the highest versatility is possible. The main strength of our proposed system is its "adaptability" that can be achieved without the use of either new terminals or novel technologies. The only requirement is the introduction of a middleware functionality into already existing terminals and into some network servers.

[1]  Benjamin N. Grosof,et al.  An Approach to Using XML and a Rule-Based Content Language with an Agent Communication Language , 2000, Issues in Agent Communication.

[2]  Henk Eertink,et al.  Ubiquitous Attentiveness - Enabling Context-Aware Mobile Applications and Services , 2003, EUSAI.

[3]  Alfred Kobsa,et al.  Generic User Modeling Systems , 2001, User modeling and user-adapted interaction.

[4]  Aaron Kershenbaum,et al.  Mobile Agents: Are They a Good Idea? , 1996, Mobile Object Systems.

[5]  T. Nakamura,et al.  Context-Aware Construction of Ubiquitous Services , 2001 .

[6]  Michael J. Pazzani,et al.  A Framework for Collaborative, Content-Based and Demographic Filtering , 1999, Artificial Intelligence Review.

[7]  Antonio Iera,et al.  Adaptively controlling the QoS of multimedia wireless applications through "user profiling" techniques , 2003, IEEE J. Sel. Areas Commun..

[8]  Wolfgang Kellerer,et al.  I-centric communications: personalization, ambient awareness, and adaptability for future mobile services , 2004, IEEE Communications Magazine.

[9]  Naohiro Ishii,et al.  Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents , 1998, CIA.

[10]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[11]  Thomas Magedanz,et al.  A self-adaptive service provisioning framework for 3G+/4G mobile applications , 2004, IEEE Wireless Communications.

[12]  Fabio Dovis,et al.  A hybrid positioning algorithm for cellular radio networks by using a common rake receiver architecture , 2004, 2004 IEEE 15th International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE Cat. No.04TH8754).

[13]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[14]  Timothy W. Finin,et al.  KQML as an agent communication language , 1994, CIKM '94.

[15]  Giovanni Vigna,et al.  Understanding Code Mobility , 1998, IEEE Trans. Software Eng..

[16]  Theodore Zahariadis Trends in the path to 4G , 2003 .