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基于信息传输模型的车联网可靠性路边单元部署优化

Road side unit deployment optimization for the reliability of internet of vehicles based on information transmission model.

作者信息

Zhang Jun, Hu Guangtong

机构信息

College of Management and Engineering, Capital University of Economics and Business, Beijing, China.

出版信息

PLoS One. 2024 Dec 18;19(12):e0315716. doi: 10.1371/journal.pone.0315716. eCollection 2024.

DOI:10.1371/journal.pone.0315716
PMID:39693302
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11654949/
Abstract

The Internet of Vehicles (IoV) makes it possible to transmit information in real time between vehicles, providing a modern approach for autonomous driving, traffic safety, and other applications. Roadside units (RSUs) contribute to the enhancement of IoV's reliability and transmission efficiency, while mitigating the impact of low IoV penetration. The objective of RSU deployment optimization is to minimize the total cost with the premise of ensuring IoV reliability. We construct a distance-based reliability measure for IoV, which is expressed as the proportion of information transmitted in online mode to the total transmission distance. The distance distribution of the online and offline transmissions is computed using the information transmission model. A bi-objective optimization model is established with the objectives of minimizing the cost of RSU and maximizing the reliability of IoV. Meanwhile, based on variable probabilities of crossover and mutation, a nondomination level-based NSGA-II (NNSGA-II) is designed to improve the solving efficiency. Numerical results show the advantage of the proposed model over the models evaluated with the objective of reducing transmission time can be up to 18% in different traffic scenarios, and NNSGA-II is significantly more computationally efficient.

摘要

车联网(IoV)使车辆之间能够实时传输信息,为自动驾驶、交通安全及其他应用提供了一种现代方法。路边单元(RSU)有助于提高车联网的可靠性和传输效率,同时减轻车联网低普及率的影响。RSU部署优化的目标是在确保车联网可靠性的前提下使总成本最小化。我们构建了一种基于距离的车联网可靠性度量,它表示在线模式下传输的信息占总传输距离的比例。利用信息传输模型计算在线和离线传输的距离分布。建立了一个双目标优化模型,目标是使RSU成本最小化和车联网可靠性最大化。同时,基于交叉和变异的可变概率,设计了一种基于非支配水平的NSGA-II(NNSGA-II)来提高求解效率。数值结果表明,在不同交通场景下,所提模型相对于以减少传输时间为目标评估的模型的优势可达18%,并且NNSGA-II的计算效率显著更高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/de44/11654949/bbcdf3cfe3ae/pone.0315716.g009.jpg
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