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Crowdsensed QoE for the community - a concept to make QoE assessment accessible

Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-203748
  • In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existingIn recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.zeige mehrzeige weniger

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Metadaten
Autor(en): Florian MetzgerORCiD
URN:urn:nbn:de:bvb:20-opus-203748
Dokumentart:Arbeitspapier / Working Paper
Institute der Universität:Fakultät für Mathematik und Informatik / Institut für Informatik
Sprache der Veröffentlichung:Englisch
Erscheinungsjahr:2020
Seitenangabe:7
Allgemeine fachliche Zuordnung (DDC-Klassifikation):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Normierte Schlagworte (GND):Quality of Experience; Crowdsourcing
Freie Schlagwort(e):Crowdsensing; QoE
Datum der Freischaltung:06.05.2020
Anmerkungen:
Originally written in 2017, but never published.
Lizenz (Deutsch):License LogoCC BY-SA: Creative-Commons-Lizenz: Namensnennung, Weitergabe unter gleichen Bedingungen 4.0 International