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蜂群无人机自组网多优先级自适应退避算法

刘炜伦 张衡阳 郑博 高维廷

刘炜伦, 张衡阳, 郑博, 等 . 蜂群无人机自组网多优先级自适应退避算法[J]. 北京航空航天大学学报, 2019, 45(2): 325-332. doi: 10.13700/j.bh.1001-5965.2018.0305
引用本文: 刘炜伦, 张衡阳, 郑博, 等 . 蜂群无人机自组网多优先级自适应退避算法[J]. 北京航空航天大学学报, 2019, 45(2): 325-332. doi: 10.13700/j.bh.1001-5965.2018.0305
LIU Weilun, ZHANG Hengyang, ZHENG Bo, et al. An adaptive backoff algorithm for FANETs based on multiple priority[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 325-332. doi: 10.13700/j.bh.1001-5965.2018.0305(in Chinese)
Citation: LIU Weilun, ZHANG Hengyang, ZHENG Bo, et al. An adaptive backoff algorithm for FANETs based on multiple priority[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2): 325-332. doi: 10.13700/j.bh.1001-5965.2018.0305(in Chinese)

蜂群无人机自组网多优先级自适应退避算法

doi: 10.13700/j.bh.1001-5965.2018.0305
基金项目: 

国家自然科学基金 61701521

中国博士后科学基金 2016M603044

航空科学基金 20161996010

陕西省自然科学基金 2018JQ6074

详细信息
    作者简介:

    刘炜伦  男, 硕士研究生。主要研究方向:机载自组网

    张衡阳  男, 博士, 副教授, 硕士生导师。主要研究方向:移动Ad hoc网络

    郑博  男, 博士, 讲师。主要研究方向:移动Ad hoc网络

    通讯作者:

    张衡阳, E-mail: hareed@163.com

  • 中图分类号: V19;TP393

An adaptive backoff algorithm for FANETs based on multiple priority

Funds: 

National Natural Science Foundation of China 61701521

China Postdoctoral Science Foundation 2016M603044

Aeronautical Science Foundation of China 20161996010

Natural Science Foundation of Shaanxi Province, China 2018JQ6074

More Information
  • 摘要:

    针对现有媒质接入控制(MAC)协议退避算法无法为蜂群无人机自组网(FANETs)提供区分服务,且在重负载时性能严重恶化等问题,提出一种多优先级自适应退避算法。采用忙闲因子自适应机制和最优竞争窗自适应机制,根据信道忙闲程度和网络状态参数自适应实时更新各优先级竞争窗口(CW)长度,从而使每次退避的竞争窗口可快速收敛到最佳状态,并实现了多业务区分服务,得到了最优的系统性能。通过建立不同优先级退避过程的三维Markov链模型求解得到了饱和吞吐量下的最优竞争窗自适应因子,并且理论推导了系统吞吐量和平均MAC时延的数学表达式。仿真结果表明,所提算法在重负载时能够实现多优先级区分服务并有效提高系统的吞吐量性能,相比区分业务优先级的自适应退避(PAB)算法和支持QoS的自适应竞争窗口退避算法(Q-ABACW),性能均有较大提升。

     

  • 图 1  MPABA原理

    Figure 1.  Principle of MPABA

    图 2  优先级r分组退避状态的三维Markov链模型

    Figure 2.  Three-dimensional Markov chain model of backoff stage for priority r traffic

    图 3  βCWIGtraM的关系

    Figure 3.  Relation of βCWI with Gtra and M

    图 4  信道负载对MPABA性能的影响

    Figure 4.  Influence of channel loads on performance of MPABA

    图 5  MPABA、PAB算法与Q-ABACW性能对比

    Figure 5.  Comparison of performance among MPABA, PAB algorithm and Q-ABACW

    表  1  仿真参数设置

    Table  1.   Simulation parameter setting

    参数 数值
    节点数量 50
    信道数量 10
    信道传输速率/(Mbit·s-1) 3
    分组长度/bit 1000
    编码效率 1/3
    单位时隙/μs 100
    各优先级最大退避次数 10
    分组拆分突发数 28
    分组成功接收所需最低突发数 14
    下载: 导出CSV

    表  2  各业务类型相关参数

    Table  2.   Related parameters of each priority type

    优先级 信息种类 lr Athr/(packet·s-1)
    1 武器协同信息
    2 态势感知信息 20 18750
    3 网络管理信息 30 5745
    4 天气、环境信息 40 1875
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-28
  • 录用日期:  2018-08-24
  • 刊出日期:  2019-02-20

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