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车载自组织网络中停放车辆合作的多媒体内容分发
引用本文:朱金奇,孙华志,刘念伯,马春梅.车载自组织网络中停放车辆合作的多媒体内容分发[J].电子科技大学学报(自然科学版),2018,47(4):566.
作者姓名:朱金奇  孙华志  刘念伯  马春梅
作者单位:1.天津师范大学计算机与信息工程学院 天津 西青区 300387
基金项目:国家自然科学基金61472068国家自然科学基金61572113国家自然科学基金61662042国家自然科学基金61602345中国博士后基金2014M550466中国博士后基金2014M5623082天津市自然科学基金17JCYBJC16400天津市自然科学基金15JCQNJ才400天津市国际科技合作项目14RCGFGX00847
摘    要:车载自组织网络(VANETs)中,如何实现车辆间多媒体文件的高效分发成为研究热点。基于城市区域长时间拥有大量地面停放车辆的情况,该文提出停放车辆合作的内容分发策略(CPVC),该策略不需要配置任何地面基础设施(RSUs),而是把地面停放车辆作为天然基础设施为车辆用户提供内容存储和内容分发服务。为此首先把每条道路的路边和非路边停放车辆组成停车簇;其次提出停车簇合作进行内容分发的方式并设计相应内容分发算法。基于真实城市地图和交通数据的实验结果表明,CPVC能以较低的下载延迟和网络开销获得较高的数据传输成功率,尤其在交通流量密度稀疏和多请求者下载情况下。

关 键 词:内容分发    多媒体    停放车辆    车载自组织网络
收稿时间:2016-01-10

Collaborative Parked Vehicle Based Multimedia Content Distribution in Vehicular Ad Hoc Networks
Affiliation:1.School of Computer and Information Engineering, Tianjin Normal University Xiqing Tianjin 3003872.School of Computer Science and Engineering, University of Electronic Science and Technology of China Chengdu 611731
Abstract:Effective large content files distribution in vehicular ad hoc networks (VANETs) is very challenging. Motivated by the fact that there are large amount of long time outside parked vehicles in urban areas, this paper proposes a collaborative parked vehicle based multimedia content distribution strategy, which does not need any roadside infrastructure, but leverages outside parked vehicles as the natural infrastructures to maintain content and provide content distribution services for moving vehicular users. At first, to each road, parked vehicles both at roadside and off-street are grouped into a cluster. Secondly, content distribution patterns based on parking clusters are presented and algorithms are designed to efficiently distribute content over VANETs. Simulation results based on a real city map and realistic traffic situations show that collaborative parked vehicle based multimedia content distribution (CPVC) achieves the highest data delivery ratio with low delivery delay and network overhead in comparison with other comparison algorithms, especially in sparse traffic and multiple subscribers conditions.
Keywords:
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