Paper Info
Title
Toward personalized and adaptive QoS assessments via context awareness.
Abstract
Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context-aware quality learning model, realized via a learning-enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.
Year
DOI
Venue
2018
10.1111/coin.12129
COMPUTATIONAL INTELLIGENCE
Keywords
Field
DocType
context awareness,personalization,quality value learning,service behavior change
Mobile QoS,Computer science,Quality of service,Service provider,Context awareness,Adaptive quality of service multi-hop routing,Artificial intelligence,Machine learning,Personalization
Journal
Volume
Issue
ISSN
34.0
2.0
0824-7935
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Lina Barakat1558.00
Phillip Taylor285.60
Nathan Griffiths338834.25
Adel Taweel49819.86
Michael Luck53440275.97
Simon Miles61599109.29