• 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
Advanced Search
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

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).
More Information
  • Published Date: March 31, 2020
  • 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.
  • Related Articles

    [1]Zhang Xiaojian, Zhang Leilei, Zhang Zhizheng. Federated Learning Method Under User-Level Local Differential Privacy[J]. Journal of Computer Research and Development, 2025, 62(2): 472-487. DOI: 10.7544/issn1000-1239.202330167
    [2]Feng Xinyue, Yang Qiusong, Shi Lin, Wang Qing, Li Mingshu. Critical Memory Data Access Monitor Based on Dynamic Strategy Learning[J]. Journal of Computer Research and Development, 2019, 56(7): 1470-1487. DOI: 10.7544/issn1000-1239.2019.20180577
    [3]Yang Yatao, Zhang Yaze, Li Zichen, Zhang Fengjuan, Liu Boya. RAKA: New Authenticated Key Agreement Protocol Based on Ring-LWE[J]. Journal of Computer Research and Development, 2017, 54(10): 2187-2192. DOI: 10.7544/issn1000-1239.2017.20170477
    [4]HePan, TanChun, YuanYue, WuKaigui. Optimal Resources Allocation Algorithm for Optional Redundancy and Monitoring Strategies[J]. Journal of Computer Research and Development, 2016, 53(3): 682-696. DOI: 10.7544/issn1000-1239.2016.20148204
    [5]Peng Hu, Wu Zhijian, Zhou Xinyu, Deng Changshou. Bare-Bones Differential Evolution Algorithm Based on Trigonometry[J]. Journal of Computer Research and Development, 2015, 52(12): 2776-2788. DOI: 10.7544/issn1000-1239.2015.20140230
    [6]Fu Lingxiao, Peng Xin, and Zhao Wenyun. An Agent-Based Requirements Monitoring Framework for Internetware[J]. Journal of Computer Research and Development, 2013, 50(5): 1055-1065.
    [7]Zhu Jun, Guo Changguo, Wu Quanyuan. A Runtime Monitoring Web Services Interaction Behaviors Method Based on CPN[J]. Journal of Computer Research and Development, 2011, 48(12): 2277-2289.
    [8]Lu Zhaoxia, Zeng Guangzhou. A Cooperative Monitoring Model of Migrating Workflow[J]. Journal of Computer Research and Development, 2009, 46(3): 398-406.
    [9]Xu Jian, Zhang Kun, Liu Fengyu, Xu Manwu. An Approach to Immunity-Based Performance Monitoring and Evaluation for Computing Systems[J]. Journal of Computer Research and Development, 2007, 44(3).
    [10]Yu Wanjun, Liu Dayou, Liu Quan, Yang Bo. An Approach to Monitoring and Controlling Workflow Systems Based on the Instance State[J]. Journal of Computer Research and Development, 2006, 43(8): 1345-1353.

Catalog

    Article views PDF downloads Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return