A Millimeter Wave based Sensor Data Broadcasting Scheme for Vehicular Communications


In recent years, vehicles are becoming smart with the aid of various onboard sensing, communication and computing capability, which is helpful to improve road safety and driving experiments. With data fusion technique, a vehicle can even increase the driving safety by obtaining sensor data from other vehicles. The millimeter Wave (mmWave) based Vehicle-to-Vehicle (V2V) communication technology has become a promising technology to transmit sensor data in huge size such as video streams. However, the high radio frequency of mmWave makes it vulnerable to obstacles. Furthermore, the directional propagation property is not efficient to broadcast information among vehicles. In this paper, we propose a broadcasting scheme to guarantee each vehicle to get the sensor data of all other vehicles. Head vehicles are selected to gather the information on the environment and decide those transmission vehicles and receiving vehicles in each time slot. A graph-based routing selection algorithm is proposed with relatively low complexity. Moreover, the upper bound of broadcasting delay for one dimensional platoon is analyzed based on the network calculus theory. Simulation results indicate that the proposed scheme has faster delivery rate compared to the traditional First-In-First-Out (FIFO) scheme. The maximum broadcasting delay of the proposed scheme is less than the traditional schemes about 30% in different scenarios.

Publication DOI: https://doi.org/10.1109/ACCESS.2019.2932428
Divisions: Engineering & Applied Sciences > Electrical, Electronic & Power Engineering
Engineering & Applied Sciences > Adaptive communications networks research group
Engineering & Applied Sciences
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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Related URLs: https://ieeexpl ... authors#authors (Publisher URL)
PURE Output Type: Article
Published Date: 2019-08-01
Accepted Date: 2019-08-01
Authors: Chen, Xiaosha
Leng, Supeng
Tang, Zuoyin ( 0000-0001-7094-999X)
Xiong, Kai
Qiao, Guanhua

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