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.
This paper presents a pioneering context-aware approach for quality of experience (QoE) measurement and prediction. The proposed approach incorporates an intuitive contextaware framework and decision theory. It is capable of incorporating several QoE related classes and context information to correctly measure and predict the overall QoE on a single scale. Our approach can be used in measuring and predicting QoE in both lab and living-lab settings based on user, device and network related context parameters. The predicted QoE can be beneficial for network operators to minimize network churn and can help application developers to build smart usercentric applications. We perform extensive experimentation and the results validate our approach.Index Terms-Bayesian network, context-awareness, decision theory, quality of experience (QoE), quality of service (QoS)
Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.
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