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无人机使能的无线传感网总能耗优化方法

李敏 包富瑜 王恒

李敏, 包富瑜, 王恒. 无人机使能的无线传感网总能耗优化方法. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220914
引用本文: 李敏, 包富瑜, 王恒. 无人机使能的无线传感网总能耗优化方法. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220914
Li Min, Bao Fu-Yu, Wang Heng. Optimization of total energy consumption for unmanned aerial vehicle-enabled wireless sensor networks. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220914
Citation: Li Min, Bao Fu-Yu, Wang Heng. Optimization of total energy consumption for unmanned aerial vehicle-enabled wireless sensor networks. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c220914

无人机使能的无线传感网总能耗优化方法

doi: 10.16383/j.aas.c220914
基金项目: 国家自然科学基金 (92267106, 61972061), 重庆英才计划基础研究与前沿探索项目 (cstc2021ycjh-bgzxm0017) 资助
详细信息
    作者简介:

    李敏:重庆邮电大学自动化学院教授. 2014年获得重庆大学博士学位. 主要研究方向为无线传感器网络, 无人机和无线功率传输. 本文通信作者. E-mail: limin@cqupt.edu.cn

    包富瑜:重庆邮电大学自动化学院硕士研究生. 主要研究方向为无线传感器网络和无人机. E-mail: baofuyu1218@163.com

    王恒:重庆邮电大学自动化学院教授. 2010年获得重庆大学博士学位. 主要研究方向为工业物联网, 无线传感器网络和时间同步. E-mail: wangheng@cqupt.edu.cn

Optimization of Total Energy Consumption for Unmanned Aerial Vehicle-enabled Wireless Sensor Networks

Funds: Supported by National Natural Science Foundation of China (92267106, 61972061) and Fundamental Research and Frontier Exploration Program of Chongqing, China (cstc2021ycjh-bgzxm0017)
More Information
    Author Bio:

    LI Min Professor at the College of Automation, Chongqing University of Posts and Telecommunications. She received her Ph. D. degree from Chongqing University in 2014. Her research interest covers wireless sensor networks, unmanned aerial vehicle, and wireless power transfer. Corresponding author of this paper

    BAO Fu-Yu Master student at the College of Automation, Chongqing University of Posts and Telecommunications. His research interest covers wireless sensor networks and unmanned aerial vehicle

    WANG Heng Professor at the College of Automation, Chongqing University of Posts and Telecommunications. He received his Ph. D. degree from Chongqing University in 2010. His research interest covers industrial internet of things, wireless sensor networks, and clock synchronization

  • 摘要: 为降低无人机(Unmanned aerial vehicle, UAV)使能的无线传感网的能量消耗, 延长网络生命周期, 该文提出一种在地面节点能量预算下系统总能耗优化方法. 首先, 提出地面节点聚类方法, 利用目标函数确定最优簇数, 改进模糊C均值算法构建能量均衡的集群, 采用退避定时器机制根据隶属度和能量值选择各集群的最优簇头, 减少地面节点的能耗. 其次, 根据已选簇头位置, 利用遗传算法规划UAV的飞行轨迹, 减小UAV能耗. 最后, 通过单纯形搜索算法和连续凸逼近算法联合优化簇头发射功率和UAV悬停位置, 减小数据采集时系统的总能耗. 仿真结果表明, 所提方法优于所比较的方案.
  • 图  1  系统模型

    Fig.  1  System model

    图  2  不同簇头数量下系统总能耗

    Fig.  2  Total energy consumption of the system with different numbers of cluster head

    图  3  集群规模变化

    Fig.  3  Variation in size of clusters

    图  4  集群内距离成本

    Fig.  4  Cost of the intra-cluster distance

    图  5  节点存活数

    Fig.  5  The number of alive nodes

    图  6  网络剩余能量

    Fig.  6  Residual energy of network

    图  7  系统能量消耗

    Fig.  7  System energy consumption

    图  8  UAV飞行轨迹

    Fig.  8  UAV flight trajectory

    图  9  不同簇成员个数对系统能耗的影响

    Fig.  9  Effect of different number of cluster members on system energy consumption

    图  10  不同簇头能量预算对系统能耗的影响

    Fig.  10  Impact of different cluster head energy budgets on system energy consumption

    表  1  仿真参数

    Table  1  Simulation parameter

    参数符号参数值参数符号参数值
    $\alpha$0.03${{v}_{v}}$10 m/s
    $\beta$10${{E}_{cap}}$50 J
    $\eta LoS$3 dB$l$1 Mb
    $\eta NLoS$13 dB${{\alpha }_{1}}$,${{\alpha }_{2}}$0.5
    ${{d}_{0}}$1 m$\phi $1 000
    ${{\sigma }^{2}}$−174 dBm/Hz${{v}_{u}}$15 m/s
    下载: 导出CSV

    表  2  不同算法的VSC比较

    Table  2  VSC comparison of different algorithms

    实验次数OCM-FCM算法IEECP算法SHM-FCM算法
    1428.4052.8548.50
    2362.3549.0546.70
    3271.1566.5557.65
    4254.2051.7543.45
    5272.4058.6550.50
    6387.5052.9031.75
    7329.1549.3543.54
    8289.4558.4562.55
    9290.2555.8055.20
    10319.1546.7537.50
    下载: 导出CSV

    表  3  网络稳定性比较

    Table  3  Comparison of network stability

    聚类算法FNDHNDLNDWFND
    OCM-FCM1751540.0065
    IEECP21042260.0089
    SHM-FCM91764160.0220
    下载: 导出CSV
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  • 收稿日期:  2022-11-24
  • 录用日期:  2023-04-04
  • 网络出版日期:  2023-04-28

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