Chapter
Jul 2, 2019

A Bio-Inspired Video Transmission Routing Protocol for VANETs in City

Publication: CICTP 2019

Abstract

With the development of vehicle ad hoc networks (VANETs), video transmission in internet of vehicles (IoVs) is an emerging technology. Because of the high mobility of VANETs and complexity of city traffic network, traditional multi-hop routing protocols are not adaptive to video transmission in IoVs. We propose a bio-inspired video transmission routing protocol for VANETs in city (BIVT). We consider vehicle motion conditions, traffic conditions, and shelter effect of urban buildings on V2V communication, and utilize the extended attractor selection model (EASM) to select next-hop node by mapping the expression of different genes in cells to selection of different next-hop nodes. We present a real-time feedback process that evaluates performance of next-hop selections, which can improve self-adaptability and robustness of BIVT. Our simulation study compares BIVT with other routing protocols under different city traffic conditions, and the results demonstrate improved performance over other methods.

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Go to CICTP 2019
CICTP 2019
Pages: 5721 - 5732

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Published online: Jul 2, 2019

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Daxin Tian
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou # 2, Nanjing 210096, China.
Chuang Zhang
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China.
Xuting Duan [email protected]
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou # 2, Nanjing, 210096, China. E-mail: [email protected]
Yunpeng Wang
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou # 2, Nanjing 210096, China.
Jianshan Zhou
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Beijing 100191, China.
Wei Hao
School of Traffic and Transportation Engineering, Changsha Univ. of Science and Technology, Changsha 410114, China.

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