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ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Authors :
Dun Cao
Yuchen Jiang
Jin Wang
Baofeng Ji
Osama Alfarraj
Amr Tolba
Xiaomin Ma
Yonghe Liu
Source :
Sensors, Vol 20, Iss 5, p 1338 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The proper utilization of road information can improve the performance of relay-node selection methods. However, the existing schemes are only applicable to a specific road structure, and this limits their application in real-world scenarios where mostly more than one road structure exists in the Region of Interest (RoI), even in the communication range of a sender. In this paper, we propose an adaptive relay-node selection (ARNS) method based on the exponential partition to implement message broadcasting in complex scenarios. First, we improved a relay-node selection method in the curved road scenarios through the re-definition of the optimal position considering the distribution of the obstacles. Then, we proposed a criterion of classifying road structures based on their broadcast characteristics. Finally, ARNS is designed to adaptively apply the appropriate relay-node selection method based on the exponential partition in realistic scenarios. Simulation results on a real-world map show that the end-to-end broadcast delay of ARNS is reduced by at least 13.8% compared to the beacon-based relay-node selection method, and at least 14.0% compared to the trinary partitioned black-burst-based broadcast protocol (3P3B)-based relay-node selection method. The broadcast coverage is increased by 3.6−7% in curved road scenarios, with obstacles benefitting from the consideration of the distribution of obstacles. Moreover, ARNS achieves a higher and more stable packet delivery ratio (PDR) than existing methods profiting from the adaptive selection mechanism.

Details

Language :
English
ISSN :
14248220
Volume :
20
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.37e6206f97224651b132ca0760c7845f
Document Type :
article
Full Text :
https://doi.org/10.3390/s20051338