An Approach to Data Fusion for Context Awareness | Zendy

Amir Padovitz | Zendy; Seng W. Loke | Zendy; Arkady Zaslavsky | Zendy; Bernard Burg | Zendy; Claudio Bartolini | Zendy

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An Approach to Data Fusion for Context Awareness

Author(s) -

Amir Padovitz,

Seng W. Loke,

Arkady Zaslavsky,

Bernard Burg,

Claudio Bartolini

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-26924-X

DOI - 10.1007/11508373_27

Subject(s) - computer science , heuristics , sensor fusion , context (archaeology) , set (abstract data type) , context model , fusion , data mining , artificial intelligence , context awareness , machine learning , data science , paleontology , linguistics , philosophy , object (grammar) , biology , programming language , operating system , phone

We propose and develop an approach modeled with multi-attribute utility theory for sensor fusion in context-aware environments. Our approach is distinguished from existing general purpose fusion techniques by a number of factors including a general underlying context model it is built upon and a set of heuristics it covers. The technique is developed for context-aware applications and we argue that it provides various advantages for data fusion in context-aware scenarios. We experimentally evaluate our approach with actual use cases using real sensors.Upprättat; 2005; 20080130 (ysko

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