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

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.