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Towards connectivity management adaptability: context awareness in policy representation and end-to-end evaluation algorithm
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Source Mobile and Ubiquitous Multimedia; Vol. 83 archive
Proceedings of the 3rd international conference on Mobile and ubiquitous multimedia table of contents
College Park, Maryland
Pages: 85 - 92  
Year of Publication: 2004
ISBN:1-58113-981-0
Authors
Jun-Zhao Sun  University of Oulu, Finland
Jukka Riekki  University of Oulu, Finland
Jaakko Sauvola  University of Oulu, Finland
Marko Jurmu  University of Oulu, Finland
Sponsor
UMIADCS : University of Maryland, Institute for Advanced Computer Studies
Publisher
ACM  New York, NY, USA
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ABSTRACT

An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging pervasive and ubiquitous computing paradigm, in which the optimal management of diverse networking resources is a challenging problem. This paper presents a context-aware policy mechanism and related end-to-end evaluation algorithm for adaptive connectivity management of multi-access wireless networks. A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as the maintenance of on-going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network QoS condition. The decision of the best access networks to be used is made on the basis of an end-to-end evaluation process. The decision can be made in both Master-Slave and Peer-to-Peer modes. The paper focuses on the methods for policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision-making algorithm in the adaptive management of heterogeneous networking resources.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Jun-Zhao Sun: colleagues
Jukka Riekki: colleagues
Jaakko Sauvola: colleagues
Marko Jurmu: colleagues