@ARTICLE{MitraetaQoETMC15, 
author={Mitra, K. and Zaslavsky, A. and Ahlund, C.}, 
journal={Mobile Computing, IEEE Transactions on}, 
title={Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems}, 
year={2015}, 
month={May}, 
volume={14}, 
number={5}, 
pages={920-936}, 
abstract={Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.}, 
keywords={belief networks;mobile computing;quality of experience;quality of service;utility theory;Bayesian networks;CaQoEM;QoS;context-aware QoE measurement;context-aware QoE modelling;context-aware QoE prediction;decision-theoretic approach;human user-related metrics;mobile computing systems;quality of experience;quality of service;range-defined QoE;utility theory;vertical handoffs;wireless network congestion;wireless signal fading;Bayes methods;Computational modeling;Context;Context modeling;Delays;Mobile computing;Predictive models;Bayesian networks;Context-Awareness;Decision Theory;Prototyping;Quality of Experience;Simulations;User tests;context-awareness;decision theory;prototyping;quality of experience;simulations;user tests}, 
doi={10.1109/TMC.2013.155}, 
ISSN={1536-1233},}