↓ Skip to main content

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm

Overview of attention for article published in SpringerPlus, July 2016
Altmetric Badge

Readers on

mendeley
17 Mendeley
Title
QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm
Published in
SpringerPlus, July 2016
DOI 10.1186/s40064-016-2459-z
Pubmed ID
Authors

Ying Bao, Weimin Lei, Wei Zhang, Yuzhuo Zhan

Abstract

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Researcher 2 12%
Student > Doctoral Student 2 12%
Lecturer > Senior Lecturer 1 6%
Student > Master 1 6%
Other 1 6%
Unknown 5 29%
Readers by discipline Count As %
Computer Science 5 29%
Engineering 4 24%
Linguistics 1 6%
Social Sciences 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 0 0%
Unknown 5 29%