A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs

Bingyi Liu; Jing Qin; Dongxiao Deng; Ze Wang; Shengwu Xiong; Luyao Ye

doi:10.1007/978-981-15-7670-6_33

A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs

Bingyi Liu^*, Jing Qin, Dongxiao Deng, Ze Wang, Shengwu Xiong, Luyao Ye

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In recent years, the advances of vehicular communication networks (VCNs) have greatly promoted the development of intelligent transportation systems (ITSs). Moreover, by the aid of the vehicular communication and computing technologies, the driving safety and traffic efficiency can be substantially improved. However, the contention-based nature and inability to handle hidden-terminal problems of existed vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications may incur high packet collision probability. To solve this problem, we develop a fog-assisted communication framework for message dissemination in VCNs. Specifically, we propose a centralized time division multiple access (TDMA)-based multiple access control (MAC) protocol that combines the advantages of fog computing to predict and avoid the packet collisions in advance, so that the channel utilization and packet transmission can be improved significantly. Simulation results confirm the efficiency of the proposed fog-oriented TDMA-based MAC protocol in urban scenarios.

Original language	English
Title of host publication	Neural Computing for Advanced Applications - First International Conference, NCAA 2020, Proceedings
Editors	Haijun Zhang, Zhao Zhang, Zhou Wu
Publisher	Springer
Pages	395-407
ISBN (Electronic)	9789811576706
ISBN (Print)	9789811576690
DOIs	https://doi.org/10.1007/978-981-15-7670-6_33
Publication status	Published - Jul 2020
Event	1st International Conference on Neural Computing for Advanced Applications (NCAA 2020) - Shenzhen, China Duration: 3 Jul 2020 → 5 Jul 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1265
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	1st International Conference on Neural Computing for Advanced Applications (NCAA 2020)
Place	China
City	Shenzhen
Period	3/07/20 → 5/07/20

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

UN SDGs

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

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Research Keywords

Collisions avoidance
Fog computing
Intelligent Transportation Systems (ITSs)
Medium Access Control (MAC)
Vehicular communication networks (VCNs)

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10.1007/978-981-15-7670-6_33

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Liu, B., Qin, J., Deng, D., Wang, Z., Xiong, S., & Ye, L. (2020). A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs. In H. Zhang, Z. Zhang, & Z. Wu (Eds.), Neural Computing for Advanced Applications - First International Conference, NCAA 2020, Proceedings (pp. 395-407). (Communications in Computer and Information Science; Vol. 1265). Springer . https://doi.org/10.1007/978-981-15-7670-6_33

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title = "A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs",

abstract = "In recent years, the advances of vehicular communication networks (VCNs) have greatly promoted the development of intelligent transportation systems (ITSs). Moreover, by the aid of the vehicular communication and computing technologies, the driving safety and traffic efficiency can be substantially improved. However, the contention-based nature and inability to handle hidden-terminal problems of existed vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications may incur high packet collision probability. To solve this problem, we develop a fog-assisted communication framework for message dissemination in VCNs. Specifically, we propose a centralized time division multiple access (TDMA)-based multiple access control (MAC) protocol that combines the advantages of fog computing to predict and avoid the packet collisions in advance, so that the channel utilization and packet transmission can be improved significantly. Simulation results confirm the efficiency of the proposed fog-oriented TDMA-based MAC protocol in urban scenarios.",

keywords = "Collisions avoidance, Fog computing, Intelligent Transportation Systems (ITSs), Medium Access Control (MAC), Vehicular communication networks (VCNs), Collisions avoidance, Fog computing, Intelligent Transportation Systems (ITSs), Medium Access Control (MAC), Vehicular communication networks (VCNs), Collisions avoidance, Fog computing, Intelligent Transportation Systems (ITSs), Medium Access Control (MAC), Vehicular communication networks (VCNs)",

author = "Bingyi Liu and Jing Qin and Dongxiao Deng and Ze Wang and Shengwu Xiong and Luyao Ye",

note = "Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).; 1st International Conference on Neural Computing for Advanced Applications (NCAA 2020) ; Conference date: 03-07-2020 Through 05-07-2020",

year = "2020",

month = jul,

doi = "10.1007/978-981-15-7670-6_33",

language = "English",

isbn = "9789811576690",

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Liu, B, Qin, J, Deng, D, Wang, Z, Xiong, S & Ye, L 2020, A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs. in H Zhang, Z Zhang & Z Wu (eds), Neural Computing for Advanced Applications - First International Conference, NCAA 2020, Proceedings. Communications in Computer and Information Science, vol. 1265, Springer , pp. 395-407, 1st International Conference on Neural Computing for Advanced Applications (NCAA 2020), Shenzhen, China, 3/07/20. https://doi.org/10.1007/978-981-15-7670-6_33

A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs. / Liu, Bingyi; Qin, Jing; Deng, Dongxiao et al.
Neural Computing for Advanced Applications - First International Conference, NCAA 2020, Proceedings. ed. / Haijun Zhang; Zhao Zhang; Zhou Wu. Springer , 2020. p. 395-407 (Communications in Computer and Information Science; Vol. 1265).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs

AU - Liu, Bingyi

AU - Qin, Jing

AU - Deng, Dongxiao

AU - Wang, Ze

AU - Xiong, Shengwu

AU - Ye, Luyao

N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

PY - 2020/7

Y1 - 2020/7

N2 - In recent years, the advances of vehicular communication networks (VCNs) have greatly promoted the development of intelligent transportation systems (ITSs). Moreover, by the aid of the vehicular communication and computing technologies, the driving safety and traffic efficiency can be substantially improved. However, the contention-based nature and inability to handle hidden-terminal problems of existed vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications may incur high packet collision probability. To solve this problem, we develop a fog-assisted communication framework for message dissemination in VCNs. Specifically, we propose a centralized time division multiple access (TDMA)-based multiple access control (MAC) protocol that combines the advantages of fog computing to predict and avoid the packet collisions in advance, so that the channel utilization and packet transmission can be improved significantly. Simulation results confirm the efficiency of the proposed fog-oriented TDMA-based MAC protocol in urban scenarios.

AB - In recent years, the advances of vehicular communication networks (VCNs) have greatly promoted the development of intelligent transportation systems (ITSs). Moreover, by the aid of the vehicular communication and computing technologies, the driving safety and traffic efficiency can be substantially improved. However, the contention-based nature and inability to handle hidden-terminal problems of existed vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications may incur high packet collision probability. To solve this problem, we develop a fog-assisted communication framework for message dissemination in VCNs. Specifically, we propose a centralized time division multiple access (TDMA)-based multiple access control (MAC) protocol that combines the advantages of fog computing to predict and avoid the packet collisions in advance, so that the channel utilization and packet transmission can be improved significantly. Simulation results confirm the efficiency of the proposed fog-oriented TDMA-based MAC protocol in urban scenarios.

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KW - Intelligent Transportation Systems (ITSs)

KW - Medium Access Control (MAC)

KW - Vehicular communication networks (VCNs)

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KW - Fog computing

KW - Intelligent Transportation Systems (ITSs)

KW - Medium Access Control (MAC)

KW - Vehicular communication networks (VCNs)

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U2 - 10.1007/978-981-15-7670-6_33

DO - 10.1007/978-981-15-7670-6_33

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9789811576690

T3 - Communications in Computer and Information Science

SP - 395

EP - 407

BT - Neural Computing for Advanced Applications - First International Conference, NCAA 2020, Proceedings

A2 - Zhang, Haijun

A2 - Zhang, Zhao

A2 - Wu, Zhou

PB - Springer

T2 - 1st International Conference on Neural Computing for Advanced Applications (NCAA 2020)

Y2 - 3 July 2020 through 5 July 2020

ER -

A Novel Collision-Avoidance TDMA MAC Protocol for Fog-Assisted VCNs

Abstract

Publication series

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Bibliographical note

UN SDGs

Research Keywords

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