Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

Wang Pi; Pengtao Yang; Dongliang Duan; Chen Chen; Xiang Cheng; Liuqing Yang

doi:10.1109/WCNC45663.2020.9120552

Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

Wang Pi, Pengtao Yang, Dongliang Duan, Chen Chen, Xiang Cheng, Liuqing Yang

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

Abstract

The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

Original language	English (US)
Title of host publication	2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728131061
DOIs	https://doi.org/10.1109/WCNC45663.2020.9120552
State	Published - May 2020
Externally published	Yes
Event	2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of Duration: May 25 2020 → May 28 2020

Publication series

Name	IEEE Wireless Communications and Networking Conference, WCNC
Volume	2020-May
ISSN (Print)	1525-3511

Conference

Conference	2020 IEEE Wireless Communications and Networking Conference, WCNC 2020
Country/Territory	Korea, Republic of
City	Seoul
Period	5/25/20 → 5/28/20

Bibliographical note

Funding Information:
This work was in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, Guandong Key R&D Project under Grant 2019B010153003, the open research fund of Key Laboratory of Wireless Sensor Network & Communication under Grant 2017003, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, and the National Science Foundation under Grants CNS-1932413 and CNS-1932139.

Publisher Copyright:
© 2020 IEEE.

UN SDGs

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

Publisher link

10.1109/WCNC45663.2020.9120552

Find It

Find It at U of M Libraries

Cite this

Pi, W., Yang, P., Duan, D., Chen, C., Cheng, X., & Yang, L. (2020). Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking. In 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings Article 9120552 (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC45663.2020.9120552

Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking. / Pi, Wang; Yang, Pengtao; Duan, Dongliang et al.
2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. 9120552 (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2020-May).

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

Pi, W, Yang, P, Duan, D, Chen, C, Cheng, X & Yang, L 2020, Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking. in 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings., 9120552, IEEE Wireless Communications and Networking Conference, WCNC, vol. 2020-May, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020, Seoul, Korea, Republic of, 5/25/20. https://doi.org/10.1109/WCNC45663.2020.9120552

Pi W, Yang P, Duan D, Chen C, Cheng X, Yang L. Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking. In 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. 9120552. (IEEE Wireless Communications and Networking Conference, WCNC). doi: 10.1109/WCNC45663.2020.9120552

@inproceedings{80829f5331f649fdb500af59567b0f3a,

title = "Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking",

abstract = "The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.",

author = "Wang Pi and Pengtao Yang and Dongliang Duan and Chen Chen and Xiang Cheng and Liuqing Yang",

note = "Funding Information: This work was in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, Guandong Key R&D Project under Grant 2019B010153003, the open research fund of Key Laboratory of Wireless Sensor Network & Communication under Grant 2017003, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, and the National Science Foundation under Grants CNS-1932413 and CNS-1932139. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 ; Conference date: 25-05-2020 Through 28-05-2020",

year = "2020",

month = may,

doi = "10.1109/WCNC45663.2020.9120552",

language = "English (US)",

series = "IEEE Wireless Communications and Networking Conference, WCNC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings",

}

TY - GEN

T1 - Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

AU - Pi, Wang

AU - Yang, Pengtao

AU - Duan, Dongliang

AU - Chen, Chen

AU - Cheng, Xiang

AU - Yang, Liuqing

N1 - Funding Information: This work was in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, Guandong Key R&D Project under Grant 2019B010153003, the open research fund of Key Laboratory of Wireless Sensor Network & Communication under Grant 2017003, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, and the National Science Foundation under Grants CNS-1932413 and CNS-1932139. Publisher Copyright: © 2020 IEEE.

PY - 2020/5

Y1 - 2020/5

N2 - The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

AB - The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

UR - http://www.scopus.com/inward/record.url?scp=85087272162&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85087272162&partnerID=8YFLogxK

U2 - 10.1109/WCNC45663.2020.9120552

DO - 10.1109/WCNC45663.2020.9120552

M3 - Conference contribution

AN - SCOPUS:85087272162

T3 - IEEE Wireless Communications and Networking Conference, WCNC

BT - 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020

Y2 - 25 May 2020 through 28 May 2020

ER -

Dynamic Model Based Malicious Collaborator Detection in Cooperative Tracking

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Publisher link

Find It

Other files and links

Fingerprint

Cite this