A probabilistic context-aware approach for quality of experience measurement in pervasive systems

Authors:
Karan Mitra;Arkady Zaslavsky;Christer Åhlund
Affiliations:
Monash University, Caulfield East Victoria, Australia;Luleå University of Technology, Luleå, Sweden;Division of Mobile Networking and Computing, Skellefteå, Sweden
Venue:
Proceedings of the 2011 ACM Symposium on Applied Computing
Year:
2011

Citing 4
Cited 2

Artificial Intelligence: A Modern Approach

Artificial Intelligence: A Modern Approach
Quality of experience in distributed interactive multimedia environments: toward a theoretical framework

MM '09 Proceedings of the 17th ACM international conference on Multimedia
User measures of quality of experience: why being objective and quantitative is important

IEEE Network: The Magazine of Global Internetworking - Special issue on improving quality of experience for network services
An approach to data fusion for context awareness

CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context

Dynamic bayesian networks for sequential quality of experience modelling and measurement

NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
A personalized QoE-aware handover decision based on distributed reinforcement learning

Wireless Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we develop a novel context-aware approach for quality of experience (QoE) modeling, reasoning and inferencing in mobile and pervasive computing environments. The proposed model is based upon a state-space approach and Bayesian networks for QoE modeling and reasoning. We further extend this context model to incorporate influence diagrams for efficient QoE inferencing. Our approach accommodates user, device and quality of service (QoS) related context parameters to determine the overall QoE of the user. This helps in user-related media, network and device adaptation. We perform experimentation to validate the proposed approach and the results verify its modeling and inferencing capabilities.