• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
高级检索

公交数据驱动的城市车联网转发机制

唐晓岚, 顼尧, 陈文龙

唐晓岚, 顼尧, 陈文龙. 公交数据驱动的城市车联网转发机制[J]. 计算机研究与发展, 2020, 57(4): 723-735. DOI: 10.7544/issn1000-1239.2020.20190876
引用本文: 唐晓岚, 顼尧, 陈文龙. 公交数据驱动的城市车联网转发机制[J]. 计算机研究与发展, 2020, 57(4): 723-735. DOI: 10.7544/issn1000-1239.2020.20190876
Tang Xiaolan, Xu Yao, Chen Wenlong. Bus-Data-Driven Forwarding Scheme for Urban Vehicular Networks[J]. Journal of Computer Research and Development, 2020, 57(4): 723-735. DOI: 10.7544/issn1000-1239.2020.20190876
Citation: Tang Xiaolan, Xu Yao, Chen Wenlong. Bus-Data-Driven Forwarding Scheme for Urban Vehicular Networks[J]. Journal of Computer Research and Development, 2020, 57(4): 723-735. DOI: 10.7544/issn1000-1239.2020.20190876
唐晓岚, 顼尧, 陈文龙. 公交数据驱动的城市车联网转发机制[J]. 计算机研究与发展, 2020, 57(4): 723-735. CSTR: 32373.14.issn1000-1239.2020.20190876
引用本文: 唐晓岚, 顼尧, 陈文龙. 公交数据驱动的城市车联网转发机制[J]. 计算机研究与发展, 2020, 57(4): 723-735. CSTR: 32373.14.issn1000-1239.2020.20190876
Tang Xiaolan, Xu Yao, Chen Wenlong. Bus-Data-Driven Forwarding Scheme for Urban Vehicular Networks[J]. Journal of Computer Research and Development, 2020, 57(4): 723-735. CSTR: 32373.14.issn1000-1239.2020.20190876
Citation: Tang Xiaolan, Xu Yao, Chen Wenlong. Bus-Data-Driven Forwarding Scheme for Urban Vehicular Networks[J]. Journal of Computer Research and Development, 2020, 57(4): 723-735. CSTR: 32373.14.issn1000-1239.2020.20190876

公交数据驱动的城市车联网转发机制

基金项目: 国家重点研发计划项目(2018YFB1800403);国家自然科学基金项目(61872252);北京市自然科学基金项目(4202012);北京市教委科技计划一般项目(KM201810028017)
详细信息
  • 中图分类号: TP393

Bus-Data-Driven Forwarding Scheme for Urban Vehicular Networks

Funds: This work was supported by the National Key Research and Development Program of China (2018YFB1800403), the National Natural Science Foundation of China (61872252), Beijing Natural Science Foundation (4202012), and the Science & Technology Project of Beijing Municipal Commission of Education (KM201810028017).
  • 摘要: 在城市车联网中,由于交通状况复杂多变和出行路线多样性等特点,网络拓扑动态变化,车辆之间通信链路不稳定,影响着车联网数据传输性能.作为城市中重要的公共交通设施,公交车具有固定的行驶路线和发车时间,且公交线路广泛覆盖城市街道.与私家车相比,公交车是更好的数据携带者和转发者,有助于实现更可靠的车车通信.为此,提出公交数据驱动的城市车联网转发机制,简称BUF,旨在通过分析公交线路数据,选择合适的公交车做为转发节点,提高城市车联网数据传输效率.首先构建公交站点拓扑图,以目标场景中所有公交站点为顶点,在公交线路连续通过的站点之间连边,依据2个站点之间的预期公交车数量和距离计算边权值,进而使用迪杰斯特拉算法计算由源站点到目的站点的最优传输路径.为保证数据沿最优路径传输,优先选择与最优路径的后续站点重合度大于零的邻居骨干公交做为转发节点,且重合度越大越优先转发;当不存在骨干公交时,选择后续将经过期望的下一站点的邻居公交为转发节点,称为候补公交.针对不存在骨干公交和候补公交的场景,利用私家车建立多跳链路来寻找合适的公交转发节点,从而加速数据转发.使用北京市真实路网和公交线路数据的实验结果表明:与其他方案相比,BUF机制实现了更高的数据传输率和更短的传输时延.
    Abstract: In urban vehicular ad hoc networks, due to the complex and dynamic traffic conditions and the diversity of driving routes, the network topology changes quickly and the communication links between vehicles are unstable, which affect the data forwarding performance of the vehicular networks. As an important public transportation facility in cities, buses have regular driving routes and departure time, and bus lines cover urban streets widely. Compared with private cars, buses are better data carriers and forwarders, and are helpful to achieve more reliable vehicle-to-vehicle communication. This paper proposes a bus-data-driven forwarding scheme for urban vehicular networks, called BUF, which aims to improve the transmission efficiency of urban vehicular networks by analyzing bus line data and selecting appropriate buses as forwarding nodes. First, a bus stop topology graph is constructed, in which all bus stops in the scenario are vertices and an edge links two vertices if there exist bus lines continuously passing through these two stops. The cost of an edge is computed based on the expected number of buses and the distance between two stops. Then the optimal forwarding path from the source stop to the destination stop is calculated by using Dijkstra algorithm. Moreover, in order to ensure that the data is forwarded along the optimal path, the neighbor backbone buses, whose overlapping degrees of subsequent stops with the optimal path are greater than zero, take priority to be selected as the forwarding nodes; and the greater the overlapping degree is, the higher priority the bus has to forward data. When no backbone bus exists, the neighbor buses, which will pass the expected next stop, called the supplement buses, are selected as relays. In the scenarios without backbone or supplement buses, private cars are used to establish a multi-hop link to find a suitable bus forwarder, in order to accelerate data forwarding. Experimental results with real Beijing road network and bus line data show that compared with other schemes, our BUF scheme achieves higher data delivery rate and shorter delay.
  • 期刊类型引用(6)

    1. 徐雪峰,郭广伟,黄余. 改进全卷积神经网络的遥感图像小目标检测. 机械设计与制造. 2024(10): 38-42 . 百度学术
    2. 刘雯雯,汪皖燕,程树林. 融合项目热门惩罚因子改进协同过滤推荐方法. 计算机技术与发展. 2023(03): 15-19 . 百度学术
    3. 冯勇,刘洋,王嵘冰,徐红艳,张永刚. 面向用户需求的生成对抗网络多样性推荐方法. 小型微型计算机系统. 2023(06): 1192-1197 . 百度学术
    4. 冯晨娇,宋鹏,张凯涵,梁吉业. 融合社交网络信息的长尾推荐方法. 模式识别与人工智能. 2022(01): 26-36 . 百度学术
    5. 韩迪,陈怡君,廖凯,林坤玲. 推荐系统中的准确性、新颖性和多样性的有效耦合与应用. 南京大学学报(自然科学). 2022(04): 604-614 . 百度学术
    6. 甘亚男,耿生玲,郝立. 超贝叶斯图模型及其联结树的构建. 青海师范大学学报(自然科学版). 2021(02): 42-48 . 百度学术

    其他类型引用(9)

计量
  • 文章访问数:  1032
  • HTML全文浏览量:  1
  • PDF下载量:  310
  • 被引次数: 15
出版历程
  • 发布日期:  2020-03-31

目录

    /

    返回文章
    返回