Resolving uncertainty in context integration and abstraction: [Context integration and abstraction]

Juan Ye, Susan McKeever, Lorcan Coyle, Steve Neely, Simon Dobson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Pervasive computing is typically highly sensor-driven, but sensors provide only evidence of fact rather than facts themselves. The uncertainty of sensor data will affect each component in a pervasive computing system, which may decrease the quality of its provided services. We provide a general model to represent semantics of uncertainty in different levels (e.g., sensor, lower-level context and higherlevel context). Within our model, fine-grained approaches are applied to evaluate and propagate uncertainties. They will help to resolve the uncertainty in each process of context management so that the effect of uncertainty on system services will be minimised.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Pervasive Services, ICPS 2008
Pages131-140
Number of pages10
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event5th International Conference on Pervasive Services, ICPS 2008 - Sorrento, Italy
Duration: 6 Jul 200810 Jul 2008

Publication series

NameProceedings of the 5th International Conference on Pervasive Services, ICPS 2008

Conference

Conference5th International Conference on Pervasive Services, ICPS 2008
Country/TerritoryItaly
CitySorrento
Period6/07/0810/07/08

Keywords

  • Bayes' Theorem
  • Context abstraction
  • Context integration
  • Context-aware computing
  • Uncertainty

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