Congestion field detection for Service Quality improvement using Kernel density estimation

Yuki Shitara, Tatsuya Yamazaki, Kyoko Yamori, Takumi Miyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.

LanguageEnglish
Title of host publication18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9784885523045
DOIs
StatePublished - 2016 Nov 7
Event18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016 - Kanazawa, Japan
Duration: 2016 Oct 52016 Oct 7

Other

Other18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016
CountryJapan
CityKanazawa
Period16/10/516/10/7

Fingerprint

Quality of service
Video streaming
Communication
Probability density function
Kernel density estimation
Service quality
Quality improvement
Congestion
Degradation
Experiments
Sourcing
Estimator
Kernel density

Keywords

  • cognition field
  • Kernel density estimation
  • mobile device
  • Quality of Experience (QoE)

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Information Systems
  • Management of Technology and Innovation
  • Computer Networks and Communications
  • Hardware and Architecture

Cite this

Shitara, Y., Yamazaki, T., Yamori, K., & Miyoshi, T. (2016). Congestion field detection for Service Quality improvement using Kernel density estimation. In 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings [7737246] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/APNOMS.2016.7737246

Congestion field detection for Service Quality improvement using Kernel density estimation. / Shitara, Yuki; Yamazaki, Tatsuya; Yamori, Kyoko; Miyoshi, Takumi.

18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7737246.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shitara, Y, Yamazaki, T, Yamori, K & Miyoshi, T 2016, Congestion field detection for Service Quality improvement using Kernel density estimation. in 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings., 7737246, Institute of Electrical and Electronics Engineers Inc., 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016, Kanazawa, Japan, 16/10/5. DOI: 10.1109/APNOMS.2016.7737246
Shitara Y, Yamazaki T, Yamori K, Miyoshi T. Congestion field detection for Service Quality improvement using Kernel density estimation. In 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings. Institute of Electrical and Electronics Engineers Inc.2016. 7737246. Available from, DOI: 10.1109/APNOMS.2016.7737246
Shitara, Yuki ; Yamazaki, Tatsuya ; Yamori, Kyoko ; Miyoshi, Takumi. / Congestion field detection for Service Quality improvement using Kernel density estimation. 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016.
@inproceedings{3fc4e3359b8e447a815ec88c68096641,
title = "Congestion field detection for Service Quality improvement using Kernel density estimation",
abstract = "Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.",
keywords = "cognition field, Kernel density estimation, mobile device, Quality of Experience (QoE)",
author = "Yuki Shitara and Tatsuya Yamazaki and Kyoko Yamori and Takumi Miyoshi",
year = "2016",
month = "11",
day = "7",
doi = "10.1109/APNOMS.2016.7737246",
language = "English",
booktitle = "18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

TY - GEN

T1 - Congestion field detection for Service Quality improvement using Kernel density estimation

AU - Shitara,Yuki

AU - Yamazaki,Tatsuya

AU - Yamori,Kyoko

AU - Miyoshi,Takumi

PY - 2016/11/7

Y1 - 2016/11/7

N2 - Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.

AB - Owing to proliferation of smart phones, communication services such as a video streaming service are common in a mobile situation. For these services, quality evaluation and communication control based on Quality of Experience (QoE), which is the degree of a user's subjective satisfaction, is very important because the final goal of delivering high-quality service is improving user's satisfaction. QoE tends to be affected by several factors including Quality of Service (QoS). Therefore, collecting QoS data from the users who use the mobile application has become one of promising schemes to meet the QoE, which is called the crowd sourcing data. The crowd sourcing data are, however, apt to be affected by sensing errors and low accuracy. In this study, we propose to estimate some densities just from the records of application use and its location information to remove the sensing errors and low accuracy. The Kernel density estimator is used to derive a continuous density function from discrete distributed sample data such as the collected QoS data. From the viewpoint of QoS, it is important to extract high-density fields of the application use to find out QoS degradation. After estimating the Kernel density estimator, we determine the borderline between the high-density field and the other field by using the reference value that can be determined from the observed data. Simulated experiments verify effectiveness of the proposed method.

KW - cognition field

KW - Kernel density estimation

KW - mobile device

KW - Quality of Experience (QoE)

UR - http://www.scopus.com/inward/record.url?scp=85006341243&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85006341243&partnerID=8YFLogxK

U2 - 10.1109/APNOMS.2016.7737246

DO - 10.1109/APNOMS.2016.7737246

M3 - Conference contribution

BT - 18th Asia-Pacific Network Operations and Management Symposium, APNOMS 2016: Management of Softwarized Infrastructure - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -