An Internet of Things QoE evaluation method based on multiple linear regression analysis
Abstract
With the development of the Internet of things(IoT), many various IoT applications continue to emerge. How to evaluate the approbate degree of users on IoT applications is an important problem in IoT research. To address this problem, this paper presents an IoT quality of experience(QoE) evaluation method based on multiple linear regression analysis. Firstly, this paper analyzes the type of IoT applications, determines the quality of service(QoS) parameters of QoE and collects the sample data. Then, it gets the principal components by principal component analysis, associates QoE to the principal components based on multiple regression analysis and builds the regression equation between QoE and principal components. Finally, it gets the functional model between QoE and QoS parameters on the basis of the regression equation. Experimental results show that this evaluation method can reduce the complexity of QoE evaluation and quantify functional relation between QoE and QoS parameters.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The authors in [40] do not highlight how these metrics impact the quality of IoT applications.
“However, the authors do not highlight how these metrics impact the quality of IoT applications.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
In [40], authors aim to ensure QoE through existing QoS parameters and develop a functional model between QoE and QoS using principal component analysis and regression.
“In [40], authors aim to ensure QoE through existing QoS parameters.In this direction, they develop a functional model between QoE and QoS.The model uses principal component analysis (PCA) to identify the principal components.Next, they deduce regression equation between QoE and principal components.Based on this regression equation, they obtain a functional model which connects QoE and QoS.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
In the device layer, metrics related to data producing IoT devices are considered, such as GPS accuracy of sensor devices or resolution of camera devices.
“In the device layer, metrics related to data producing IoT devices are considered such as, GPS accuracy of sensor devices or the resolution of camera devices.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
Physical metrics include factors that impact application quality and are mapped to IoT architecture; metaphysical metrics correspond to factors that impact application quality and are mapped to application requirements.
“Physical metrics include the factors that impact the application quality and are mapped to IoT architecture.Metaphysical metrics correspond to factors which impact the application quality and are mapped to application requirements.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
In [40], authors categorized metrics impacting application QoE into physical metrics and metaphysical metrics.
“In [40], the authors categorized the metrics which impact application QoE into two broad categories: physical metrics and metaphysical metrics.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
To organize quality metrics corresponding to IoT architecture, functions of IoT architecture are virtually divided into four layers: device, network, computing, and user interface.
“To organize the quality metrics corresponding to IoT architecture (physical metrics), functions of IoT architecture are virtually divided into four different layers and the quality metrics are defined for each layer.These layers are device, network, computing, and user interface.”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [40]
Cites
A Comparative Study on Architectures and Implementation Methodologies of Internet of Things
Chen, H., Cui, L., & Xie, K. (2014) · Chinese Journal of Computers · doi:10.3724/sp.j.1016.2013.00168 · Cited by 36 · see citing works
QoS Architecture in Wireless Sensor Network
Feng, R. (2009) · Chinese Journal of Computers · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
QoE in IoT: a vision, survey and future directions
Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D. (2021) · Discover Internet of Things · doi:10.1007/s43926-021-00006-7 · Cited by 107 · see citing works
“In [40], authors aim to ensure QoE through existing QoS parameters.”
BackgroundMeasuring QoE of a Teleconsultation App in Mental Health Using a Pentagram Model
de la Torre Díez, I., Alonso, S. G., Cruz, E. M., & Franco, M. A. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1342-1 · Cited by 10 · see citing works
- Authors
- Li, L., Rong, M., & Zhang, G.
- Affiliations
- Jinan University (CN)
- Chinese Academy of Sciences (CN)
- Venue
- 2015 10th International Conference on Computer Science & Education (ICCSE)
- Publisher
- IEEE
- Published
- 2015-07
- Pages
- 925-928
- OpenAlex
- W1509516573
- Cited by
- 37
- References
- 2
- Citations by year
- 2024: 2 · 2023: 5 · 2022: 3 · 2021: 3 · 2020: 8 · 2019: 7 · 2018: 4 · 2017: 4 · 2016: 1
- FWCI
- 1.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer Science & Education (ICCSE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- Internet of Things, quality of experience, QoE evaluation, multiple linear regression, principal component analysis, IoT applications, user satisfaction
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