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城市车联网中具有时延和成本限制的 RSU 部署

Delay-Bounded and Cost-Limited RSU Deployment in Urban Vehicular Ad Hoc Networks.

机构信息

School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China.

出版信息

Sensors (Basel). 2018 Aug 22;18(9):2764. doi: 10.3390/s18092764.

Abstract

As an auxiliary facility, roadside units (RSUs) can well improve the shortcomings incurred by ad hoc networks and promote network performance in a vehicular ad hoc network (VANET). However, deploying a large number of RSUs will lead to high installation and maintenance costs. Therefore, trying to find the best locations is a key issue when deploying RSUs with the set delay and budget. In this paper, we study the delay-bounded and cost-limited RSU deployment (DBCL) problem in urban VANET. We prove it is non-deterministic polynomial-time hard (NP-hard), and a binary differential evolution scheme is proposed to maximize the number of roads covered by deploying RSUs. Opposite-based learning is introduced to initialize the first generation, and a binary differential mutation operator is designed to obtain binary coding. A random variable is added to the traditional crossover operator to increase population diversity. Also, a greedy-based individual reparation and promotion algorithm is adopted to repair infeasible solutions violating given constraints, and to gain optimal feasible solutions with the compromise of given limits. Moreover, after selection, a solution promotion algorithm is executed to promote the best solution found in generation. Simulation is performed on analog trajectories sets, and results show that our proposed algorithm has a higher road coverage ratio and lower packet loss compared with other schemes.

摘要

作为辅助设施,路侧单元 (RSU) 可以很好地弥补自组织网络的不足,并提高车载自组织网络 (VANET) 的网络性能。然而,部署大量的 RSU 会导致高昂的安装和维护成本。因此,在设置的延迟和预算下,尝试找到最佳的 RSU 部署位置是一个关键问题。本文研究了城市 VANET 中具有延迟约束和成本限制的 RSU 部署(DBCL)问题。我们证明了它是 NP 难问题,并提出了一种二进制差分进化方案,以最大化部署 RSU 所覆盖的道路数量。引入基于对的学习来初始化第一代,并设计了二进制差分突变算子来获取二进制编码。在传统的交叉算子中添加一个随机变量来增加种群多样性。此外,采用基于贪婪的个体修复和提升算法来修复违反给定约束的不可行解,并在给定限制的折衷下获得最优可行解。而且,在选择之后,执行解决方案提升算法来提升在生成中找到的最佳解决方案。在模拟轨迹集上进行了仿真,结果表明,与其他方案相比,我们提出的算法具有更高的道路覆盖率和更低的丢包率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f090/6163701/2998e3abd8b7/sensors-18-02764-g001.jpg

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