ACM Computing Surveys (CSUR)
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Distributed mediation of ambiguous context in aware environments
Proceedings of the 15th annual ACM symposium on User interface software and technology
Context-Aware Resource Management in Multi-Inhabitant Smart Homes: A Nash H-Learning based Approach
PERCOM '06 Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications
CAPS: Energy-Efficient Processing of Continuous Aggregate Queries in Sensor Networks
PERCOM '06 Proceedings of the Fourth Annual IEEE International Conference on Pervasive Computing and Communications
Routing Correlated Data with Fusion Cost in Wireless Sensor Networks
IEEE Transactions on Mobile Computing
A Middleware Framework for Ambiguous Context Mediation in Smart Healthcare Application
WIMOB '07 Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications
Human-Computer Interaction
An approach to data fusion for context awareness
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
EcoSense: a context and semantics driven framework for eco-aware ambient environments
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Security management in Wireless Sensor Networks for healthcare
International Journal of Mobile Communications
An ambient intelligence framework for large-scale eco-aware systems
International Journal of Autonomous and Adaptive Communications Systems
Context-aware hybrid reasoning framework for pervasive healthcare
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
Ubiquitous (or smart) healthcare applications envision sensor rich computing and networking environments that can capture various types of contexts of patients (or inhabitants of the environment), such as their location, activities and vital signs. Such context information is useful in providing health related and wellness management services in an intelligent way so as to promote independent living. However, in reality, both sensed and interpreted contexts may often be ambiguous, leading to fatal decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for healthcare applications is the ability to deal with ambiguous contexts to prevent hazardous situations. In this paper, we propose a quality assured ontology-driven context mediation framework, based on efficient context-aware data fusion using resource constrained sensor network. The proposed framework provides a systematic approach based on dynamic Bayesian network to derive context fragments and deal with context ambiguity in a probabilistic manner. It has the ability to represent contexts according to the applications' ontology and easily composable ontological rules to mediate ambiguous contexts. We have also implemented a demonstration of the use of our model using semantic web language. Through simulation, we demonstrate the effectiveness of our proposed framework.