The design and applications of a context service
ACM SIGMOBILE Mobile Computing and Communications Review
Modelling and Using Sensed Context Information in the Design of Interactive Applications
EHCI '01 Proceedings of the 8th IFIP International Conference on Engineering for Human-Computer Interaction
Modelling and Using Imperfect Context Information
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
Resolving uncertainty in context integration and abstraction: context integration and abstraction
Proceedings of the 5th international conference on Pervasive services
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Using Dempster-Shafer theory of evidence for situation inference
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Activity recognition using temporal evidence theory
Journal of Ambient Intelligence and Smart Environments
A formal model of reliable sensor perception
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
Optimizing the quality of dynamic context subscriptions for scarce network resources
Proceedings of the 1st European Workshop on AppRoaches to MObiquiTous Resilience
Network aware dynamic context subscription management
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Much research on context quality in context-aware systems divides into two strands: (1) the qualitative identification of quality measures and (2) the use of uncertain reasoning techniques. In this paper, we combine these two strands, exploring the problem of how to identify and propagate quality through the different context layers in order to support the context reasoning process. We present a generalised, structured context quality model that supports aggregation of quality from sensor up to situation level. Our model supports reasoning processes that explicitly aggregate context quality, by enabling the identification and quantification of appropriate quality parameters. We demonstrate the efficacy of our model using an experimental sensor data set, gaining a significant improvement in situation recognition for our voting based reasoning algorithm.