Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks

Author:

Yoo Joon1ORCID

Affiliation:

1. School of Computing, Gachon University, 1342, Sujeong-gu, Seongnam-daero, Seongnam-si 13120, Republic of Korea

Abstract

Vehicular ad hoc networks consist of self-organizing nodes using multi-hop wireless links for communication without any infrastructure support. Traditionally, ad hoc routing protocols use the minimum hop count for their routing metric since a smaller number of transmissions is typically equivalent to a higher throughput, lower delay, and minimal power consumption. However, with the muti-rate capability of emerging radio interfaces, e.g., 802.11ax/be standards, the min-hop metric no longer results in high throughput. For instance, if the higher data rate links are selected for the route, it could result in a higher throughput even if the route takes more hop counts. In this paper, we propose a high throughput routing scheme, called MARV, which makes two key contributions. MARV searches for high throughput paths using an on-demand route searching algorithm so that the routing overhead is smaller compared to other multi-rate-aware routing schemes. MARV also searches for multiple paths to maintain both min-hop and high-throughput paths to select the adequate path depending on the data packet size. We conduct simulations to demonstrate that MARV outperforms not only min-hop path metrics but also previously proposed high-throughput metrics.

Funder

National Research Foundation of Korea

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference36 articles.

1. Networking and communications in autonomous driving: A survey;Wang;IEEE Commun. Surv. Tutor.,2018

2. Communication challenges in infrastructure-vehicle cooperative autonomous driving: A field deployment perspective;Liu;IEEE Wirel. Commun.,2022

3. A review on autonomous electric vehicle communication networks-progress, methods and challenges;Venkitaraman;World J. Adv. Res. Rev.,2022

4. Uses of Artificial Intelligence in Autonomous Driving and V2X communication;Namatherdhala;Int. Res. J. Mod. Eng. Technol. Sci.,2022

5. Angle of arrival-based cooperative positioning for smart vehicles;Fascista;IEEE Trans. Intell. Transp. Syst.,2017

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3