Context-Aware QoE Modelling, Measurement, and Prediction in Mobile Computing Systems
Open accessAbstract
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.
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Till-date the notion of network-, cloud-edge and IoT-based application performance hinges upon QoS assurances that are typically guaranteed via service level agreements (SLAs).
“Till-date the notion of network-, cloud-edge and IoT-based application performance hinges upon QoS assurances that are typically guaranteed via service level agreements (SLAs) [12].”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [12]
Recently Minovski et al. [19,20] and Mitra et al. [12] have argued for the need to understand quality in an IoT ecosystem; within an IoT ecosystem a user can be both human and/or other systems and IoT devices.
“Recently Minovski et al. [19,20] and Mitra et al. [12] have argued for the need to understand quality in an IoT ecosystem; within an IoT ecosystem a user can be both human and/or other systems and IoT devices.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [12]
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- Authors
- Mitra, K., Zaslavsky, A., & Ahlund, C.
- Affiliations
- Luleå University of Technology (SE)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2015-05
- Volume
- 14
- Issue
- 5
- Pages
- 920-936
- ISSN
- 1536-1233 (ISSN-L)
- OpenAlex
- W2124542511
- Cited by
- 144
- References
- 39
- Citations by year
- 2025: 8 · 2024: 13 · 2023: 9 · 2022: 13 · 2021: 10 · 2020: 8 · 2019: 15 · 2018: 19 · 2017: 18 · 2016: 13 · 2015: 14 · 2014: 4
- FWCI
- 14.61
- Type
- journal article
- Language
- en
- Open access
- green
- urn.kb.se
- Locations
- IEEE Transactions on Mobile Computing (publishedVersion)
- Epubl LTU (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- quality of experience, QoE prediction, QoE measurement, Bayesian networks, utility theory, decision-theoretic approach, user satisfaction, bipolar scales, vertical handover, network congestion, mobile computing
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