Maximizing quality of experience through context-aware mobile application scheduling in cloudlet infrastructure

2016 ◽  
Vol 46 (11) ◽  
pp. 1525-1545 ◽  
Author(s):  
Md. Redowan Mahmud ◽  
Mahbuba Afrin ◽  
Md. Abdur Razzaque ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi ◽  
...  
Keyword(s):  
Mobile Application ◽  
Quality Of Experience ◽  
Context Aware

Context-Aware Content Delivery

2015 ◽  
pp. 474-499
Author(s):  
Hassnaa Moustafa ◽  
V. Srinivasa Somayazulu ◽  
Yiting Liao
Keyword(s):  
Quality Of Experience ◽  
Service Providers ◽  
Content Delivery ◽  
Context Aware ◽  
Video Delivery ◽  
Network Resources ◽  
Personalized Services ◽  
Over The Top ◽  
Required Quality

The huge changes in multimedia and video consumption styles are leading to different challenges for the current Internet architecture in order to support the required quality of experience. A comprehensive solution to these would help the service providers and over-the-top players (OTT) to differentiate their services and the network operators to handle ever growing demands on network resources in an era of slower growth in revenues. This chapter discusses the requirements for and approaches to enhanced content delivery architectures, video delivery standards and current and future content transport mechanisms. The chapter also discusses the Quality of Experience (QoE) metrics and management for video content and introduces context-awareness in the video delivery chain. It also provides several examples for context-aware content delivery and personalized services.

scholarly journals QoE Evaluation: The TRIANGLE Testbed Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Almudena Díaz Zayas ◽  
Laura Panizo ◽  
Janie Baños ◽  
Carlos Cárdenas ◽  
Michael Dieudonne
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Quality Of Experience ◽  
Quality Score ◽  
Project Approach ◽  
End To End

This paper presents the TRIANGLE testbed approach to score the Quality of Experience (QoE) of mobile applications, based on measurements extracted from tests performed on an end-to-end network testbed. The TRIANGLE project approach is a methodology flexible enough to generalize the computation of the QoE for any mobile application. The process produces a final TRIANGLE mark, a quality score, which could eventually be used to certify applications.

A conceptual model for quality of experience management to provide context-aware eHealth services

2019 ◽  
Vol 101 ◽  
pp. 1041-1061 ◽  
Author(s):  
Madalena Pereira da Silva ◽  
Alexandre Leopoldo Gonçalves ◽  
Mário Antônio Ribeiro Dantas
Keyword(s):  
Conceptual Model ◽  
Quality Of Experience ◽  
Context Aware ◽  
Ehealth Services

Psychometric evaluation of the Mobile Application Rating Scale (MARS) (Preprint)

2020 ◽  
Author(s):  
Yannik Terhorst ◽  
Paula Philippi ◽  
Lasse Sander ◽  
Dana Schultchen ◽  
Sarah Paganini ◽  
...  
Keyword(s):  
Health Care ◽  
Construct Validity ◽  
Mobile Application ◽  
Concurrent Validity ◽  
Rating Scale ◽  
Psychometric Evaluation ◽  
Confirmatory Factor ◽  
Health Domains ◽  
Psychometric Quality

BACKGROUND Mobile health apps (MHA) have the potential to improve health care. The commercial MHA market is rapidly growing, but the content and quality of available MHA are unknown. Consequently, instruments of high psychometric quality for the assessment of the quality and content of MHA are highly needed. The Mobile Application Rating Scale (MARS) is one of the most widely used tools to evaluate the quality of MHA in various health domains. Only few validation studies investigating its psychometric quality exist with selected samples of MHAs. No study has evaluated the construct validity of the MARS and concurrent validity to other instruments. OBJECTIVE This study evaluates the construct validity, concurrent validity, reliability, and objectivity, of the MARS. METHODS MARS scoring data was pooled from 15 international app quality reviews to evaluate the psychometric properties of the MARS. The MARS measures app quality across four dimensions: engagement, functionality, aesthetics and information quality. App quality is determined for each dimension and overall. Construct validity was evaluated by assessing related competing confirmatory models that were explored by confirmatory factor analysis (CFA). A combination of non-centrality (RMSEA), incremental (CFI, TLI) and residual (SRMR) fit indices was used to evaluate the goodness of fit. As a measure of concurrent validity, the correlations between the MARS and 1) another quality assessment tool called ENLIGHT, and 2) user star-rating extracted from app stores were investigated. Reliability was determined using Omega. Objectivity was assessed in terms of intra-class correlation. RESULTS In total, MARS ratings from 1,299 MHA covering 15 different health domains were pooled for the analysis. Confirmatory factor analysis confirmed a bifactor model with a general quality factor and an additional factor for each subdimension (RMSEA=0.074, TLI=0.922, CFI=0.940, SRMR=0.059). Reliability was good to excellent (Omega 0.79 to 0.93). Objectivity was high (ICC=0.82). The overall MARS rating was positively associated with ENLIGHT (r=0.91, P<0.01) and user-ratings (r=0.14, P<0.01). CONCLUSIONS he psychometric evaluation of the MARS demonstrated its suitability for the quality assessment of MHAs. As such, the MARS could be used to make the quality of MHA transparent to health care stakeholders and patients. Future studies could extend the present findings by investigating the re-test reliability and predictive validity of the MARS.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

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