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BCDP：车载自组织网络中混合路边单元的预算受限和延迟受限放置

BCDP: Budget constrained and delay-bounded placement for hybrid roadside units in vehicular ad hoc networks.

作者信息

Li Peng, Huang Chuanhe, Liu Qin

机构信息

Computer School, Wuhan University, Luo-Jia-Shan Road 16, Wuhan 430072, China.

出版信息

Sensors (Basel). 2014 Nov 27;14(12):22564-94. doi: 10.3390/s141222564.

DOI:10.3390/s141222564
PMID:25436656
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4299028/
Abstract

In vehicular ad hoc networks, roadside units (RSUs) placement has been proposed to improve the the overall network performance in many ITS applications. This paper addresses the budget constrained and delay-bounded placement problem (BCDP) for roadside units in vehicular ad hoc networks. There are two types of RSUs: cable connected RSU (c-RSU) and wireless RSU (w-RSU). c-RSUs are interconnected through wired lines, and they form the backbone of VANETs, while w-RSUs connect to other RSUs through wireless communication and serve as an economical extension of the coverage of c-RSUs. The delay-bounded coverage range and deployment cost of these two cases are totally different. We are given a budget constraint and a delay bound, the problem is how to find the optimal candidate sites with the maximal delay-bounded coverage to place RSUs such that a message from any c-RSU in the region can be disseminated to the more vehicles within the given budget constraint and delay bound. We first prove that the BCDP problem is NP-hard. Then we propose several algorithms to solve the BCDP problem. Simulation results show the heuristic algorithms can significantly improve the coverage range and reduce the total deployment cost, compared with other heuristic methods.

摘要

在车载自组织网络中，已有人提出通过部署路边单元（RSU）来提升许多智能交通系统（ITS）应用中的整体网络性能。本文探讨了车载自组织网络中路边单元的预算受限且延迟受限的部署问题（BCDP）。路边单元有两种类型：有线连接的路边单元（c-RSU）和无线路边单元（w-RSU）。c-RSU通过有线线路相互连接，构成车联网的骨干，而w-RSU通过无线通信连接到其他RSU，并作为c-RSU覆盖范围的经济扩展。这两种情况下的延迟受限覆盖范围和部署成本完全不同。给定一个预算限制和一个延迟界限，问题在于如何找到具有最大延迟受限覆盖范围的最优候选站点来部署RSU，以便在给定的预算限制和延迟界限内，将来自该区域内任何c-RSU的消息传播给更多车辆。我们首先证明BCDP问题是NP难问题。然后我们提出了几种算法来解决BCDP问题。仿真结果表明，与其他启发式方法相比，这些启发式算法能够显著提高覆盖范围并降低总部署成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/bd6a322c13fe/sensors-14-22564f15.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/6682011a50b1/sensors-14-22564f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/a3d7c34d3964/sensors-14-22564f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/94184c973bf5/sensors-14-22564f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/2a23ed783537/sensors-14-22564f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/3a2e28731c82/sensors-14-22564f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/9461b7ed53aa/sensors-14-22564f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dbbc/4299028/f9f412a43b19/sensors-14-22564f14.jpg
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