@article{Seufert2021,
  author    = {Seufert, Michael},
  title     = {Statistical methods and models based on quality of experience distributions},
  series = {Quality and User Experience},
  volume    = {6},
  journal   = {Quality and User Experience},
  issn      = {2366-0139},
  doi       = {10.1007/s41233-020-00044-z},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235733},
  year      = {2021},
  abstract  = {Due to biased assumptions on the underlying ordinal rating scale in subjective Quality of Experience (QoE) studies, Mean Opinion Score (MOS)-based evaluations provide results, which are hard to interpret and can be misleading. This paper proposes to consider the full QoE distribution for evaluating, reporting, and modeling QoE results instead of relying on MOS-based metrics derived from results based on ordinal rating scales. The QoE distribution can be represented in a concise way by using the parameters of a multinomial distribution without losing any information about the underlying QoE ratings, and even keeps backward compatibility with previous, biased MOS-based results. Considering QoE results as a realization of a multinomial distribution allows to rely on a well-established theoretical background, which enables meaningful evaluations also for ordinal rating scales. Moreover, QoE models based on QoE distributions keep detailed information from the results of a QoE study of a technical system, and thus, give an unprecedented richness of insights into the end users' experience with the technical system. In this work, existing and novel statistical methods for QoE distributions are summarized and exemplary evaluations are outlined. Furthermore, using the novel concept of quality steps, simulative and analytical QoE models based on QoE distributions are presented and showcased. The goal is to demonstrate the fundamental advantages of considering QoE distributions over MOS-based evaluations if the underlying rating data is ordinal in nature.},
  language  = {en}
}
@article{SeufertSchroederSeufert2021,
  author    = {Seufert, Anika and Schr{\"o}der, Svenja and Seufert, Michael},
  title     = {Delivering User Experience over Networks: Towards a Quality of Experience Centered Design Cycle for Improved Design of Networked Applications},
  series = {SN Computer Science},
  volume    = {2},
  journal   = {SN Computer Science},
  number    = {6},
  issn      = {2661-8907},
  doi       = {10.1007/s42979-021-00851-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-271762},
  year      = {2021},
  abstract  = {To deliver the best user experience (UX), the human-centered design cycle (HCDC) serves as a well-established guideline to application developers. However, it does not yet cover network-specific requirements, which become increasingly crucial, as most applications deliver experience over the Internet. The missing network-centric view is provided by Quality of Experience (QoE), which could team up with UX towards an improved overall experience. By considering QoE aspects during the development process, it can be achieved that applications become network-aware by design. In this paper, the Quality of Experience Centered Design Cycle (QoE-CDC) is proposed, which provides guidelines on how to design applications with respect to network-specific requirements and QoE. Its practical value is showcased for popular application types and validated by outlining the design of a new smartphone application. We show that combining HCDC and QoE-CDC will result in an application design, which reaches a high UX and avoids QoE degradation.},
  language  = {en}
}
@article{HirthSeufertLangeetal.2021,
  author    = {Hirth, Matthias and Seufert, Michael and Lange, Stanislav and Meixner, Markus and Tran-Gia, Phuoc},
  title     = {Performance evaluation of hybrid crowdsensing and fixed sensor systems for event detection in urban environments},
  series = {Sensors},
  volume    = {21},
  journal   = {Sensors},
  number    = {17},
  issn      = {1424-8220},
  doi       = {10.3390/s21175880},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245245},
  year      = {2021},
  abstract  = {Crowdsensing offers a cost-effective way to collect large amounts of environmental sensor data; however, the spatial distribution of crowdsensing sensors can hardly be influenced, as the participants carry the sensors, and, additionally, the quality of the crowdsensed data can vary significantly. Hybrid systems that use mobile users in conjunction with fixed sensors might help to overcome these limitations, as such systems allow assessing the quality of the submitted crowdsensed data and provide sensor values where no crowdsensing data are typically available. In this work, we first used a simulation study to analyze a simple crowdsensing system concerning the detection performance of spatial events to highlight the potential and limitations of a pure crowdsourcing system. The results indicate that even if only a small share of inhabitants participate in crowdsensing, events that have locations correlated with the population density can be easily and quickly detected using such a system. On the contrary, events with uniformly randomly distributed locations are much harder to detect using a simple crowdsensing-based approach. A second evaluation shows that hybrid systems improve the detection probability and time. Finally, we illustrate how to compute the minimum number of fixed sensors for the given detection time thresholds in our exemplary scenario.},
  language  = {en}
}