A novel data quality assessment framework for vehicular network testbeds

Daxin Tian; Yukai Zhu; Jianshan Zhou; Xuting Duan; Yunpeng Wang; Jeungeun Song; Hui Rong; Peng Guo

doi:10.4108/eai.28-9-2017.2273211

A novel data quality assessment framework for vehicular network testbeds

Daxin Tian
, Yukai Zhu
, Jianshan Zhou
, Xuting Duan^*
, Yunpeng Wang
, Jeungeun Song
, Hui Rong
, Peng Guo

^*Corresponding author for this work

School of Transportation Science and Engineering

China Automotive Technology and Research Center Co. Ltd
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies
Beihang University
Huazhong University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect.

Original language	English
Title of host publication	TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities
Editors	Victor C. M. Leung, Yin Zhang, Giancarlo Fortino, Min Chen, Adlen Ksentini, Kai Lin
Publisher	Springer Verlag
ISBN (Electronic)	9781631901577
DOIs	https://doi.org/10.4108/eai.28-9-2017.2273211
State	Published - 8 Jan 2018
Event	12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017 - Dalian, China Duration: 28 Sep 2017 → 29 Sep 2017

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	2017-September
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017
Country/Territory	China
City	Dalian
Period	28/09/17 → 29/09/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Connected vehicles
Data fusion
Data quality assessment
Machine learning
Vehicular network

Access to Document

10.4108/eai.28-9-2017.2273211

Cite this

Tian, D., Zhu, Y., Zhou, J., Duan, X., Wang, Y., Song, J., Rong, H., & Guo, P. (2018). A novel data quality assessment framework for vehicular network testbeds. In V. C. M. Leung, Y. Zhang, G. Fortino, M. Chen, A. Ksentini, & K. Lin (Eds.), TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 2017-September). Springer Verlag. https://doi.org/10.4108/eai.28-9-2017.2273211

Tian, Daxin ; Zhu, Yukai ; Zhou, Jianshan et al. / A novel data quality assessment framework for vehicular network testbeds. TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. editor / Victor C. M. Leung ; Yin Zhang ; Giancarlo Fortino ; Min Chen ; Adlen Ksentini ; Kai Lin. Springer Verlag, 2018. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{0b58ca62d9b7476e86e5cb461b4230ee,

title = "A novel data quality assessment framework for vehicular network testbeds",

abstract = "Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect.",

keywords = "Connected vehicles, Data fusion, Data quality assessment, Machine learning, Vehicular network",

author = "Daxin Tian and Yukai Zhu and Jianshan Zhou and Xuting Duan and Yunpeng Wang and Jeungeun Song and Hui Rong and Peng Guo",

note = "Publisher Copyright: Copyright {\textcopyright} 2018 EAI.; 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017 ; Conference date: 28-09-2017 Through 29-09-2017",

year = "2018",

month = jan,

day = "8",

doi = "10.4108/eai.28-9-2017.2273211",

language = "英语",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

editor = "Leung, \{Victor C. M.\} and Yin Zhang and Giancarlo Fortino and Min Chen and Adlen Ksentini and Kai Lin",

booktitle = "TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities",

address = "德国",

}

Tian, D, Zhu, Y, Zhou, J , Duan, X , Wang, Y, Song, J, Rong, H & Guo, P 2018, A novel data quality assessment framework for vehicular network testbeds. in VCM Leung, Y Zhang, G Fortino, M Chen, A Ksentini & K Lin (eds), TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 2017-September, Springer Verlag, 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017, Dalian, China, 28/09/17. https://doi.org/10.4108/eai.28-9-2017.2273211

A novel data quality assessment framework for vehicular network testbeds. / Tian, Daxin; Zhu, Yukai; Zhou, Jianshan et al.
TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. ed. / Victor C. M. Leung; Yin Zhang; Giancarlo Fortino; Min Chen; Adlen Ksentini; Kai Lin. Springer Verlag, 2018. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 2017-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A novel data quality assessment framework for vehicular network testbeds

AU - Tian, Daxin

AU - Zhu, Yukai

AU - Zhou, Jianshan

AU - Duan, Xuting

AU - Wang, Yunpeng

AU - Song, Jeungeun

AU - Rong, Hui

AU - Guo, Peng

PY - 2018/1/8

Y1 - 2018/1/8

N2 - Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect.

AB - Big data technique is considered as a powerful tool to exploit all the potential of the Internet of Things and the smart cities. The development of internet of Vehicles (IoV) and wireless communication technologies have boosted diverse applications related to smart cities and Cyber-Physical Systems, but the data quality of vehicular sensors is an important issue due to the high-speed mobile wireless communication environment and physical sensor noise. This paper presents our experiences for big data analytics based on a vehicular network testbed, in terms of sensors data management, multi-dimension data fusion and data quality assessment for the vehicular sensor data. The proposed data quality assessment framework consist of feature extraction based on multi-sensor data fusion and multi-level wavelet transform, as well as a semi-supervised learning based classification algorithm. The comparison experiment shows that the proposed framework and approaches can extract feasible features and solve the unbalanced label problems, which achieve a better assessment effect.

KW - Connected vehicles

KW - Data fusion

KW - Data quality assessment

KW - Machine learning

KW - Vehicular network

UR - https://www.scopus.com/pages/publications/85051022512

U2 - 10.4108/eai.28-9-2017.2273211

DO - 10.4108/eai.28-9-2017.2273211

M3 - 会议稿件

AN - SCOPUS:85051022512

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

BT - TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities

A2 - Leung, Victor C. M.

A2 - Zhang, Yin

A2 - Fortino, Giancarlo

A2 - Chen, Min

A2 - Ksentini, Adlen

A2 - Lin, Kai

PB - Springer Verlag

T2 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TRIDENTCOM 2017

Y2 - 28 September 2017 through 29 September 2017

ER -

Tian D, Zhu Y, Zhou J , Duan X , Wang Y, Song J et al. A novel data quality assessment framework for vehicular network testbeds. In Leung VCM, Zhang Y, Fortino G, Chen M, Ksentini A, Lin K, editors, TRIDENTCOM 2017 - 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities. Springer Verlag. 2018. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.4108/eai.28-9-2017.2273211

A novel data quality assessment framework for vehicular network testbeds

Abstract

Publication series

Conference

UN SDGs

Keywords

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