高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

融合深度调整和自适应转发的水声网络机会路由

金志刚 梁嘉伟 羊秋玲

金志刚, 梁嘉伟, 羊秋玲. 融合深度调整和自适应转发的水声网络机会路由[J]. 电子与信息学报, 2024, 46(1): 49-57. doi: 10.11999/JEIT230026
引用本文: 金志刚, 梁嘉伟, 羊秋玲. 融合深度调整和自适应转发的水声网络机会路由[J]. 电子与信息学报, 2024, 46(1): 49-57. doi: 10.11999/JEIT230026
JIN Zhigang, LIANG Jiawei, YANG Qiuling. Opportunistic Routing in Underwater Acoustic Networks Fusing Depth Adjustment and Adaptive Forwarding[J]. Journal of Electronics & Information Technology, 2024, 46(1): 49-57. doi: 10.11999/JEIT230026
Citation: JIN Zhigang, LIANG Jiawei, YANG Qiuling. Opportunistic Routing in Underwater Acoustic Networks Fusing Depth Adjustment and Adaptive Forwarding[J]. Journal of Electronics & Information Technology, 2024, 46(1): 49-57. doi: 10.11999/JEIT230026

融合深度调整和自适应转发的水声网络机会路由

doi: 10.11999/JEIT230026
基金项目: 国家自然科学基金(52171337, 61862020)
详细信息
    作者简介:

    金志刚:男,博士,教授,研究方向为水下网络、传感器网络、网络安全、社交网络与大数据

    梁嘉伟:男,硕士生,研究方向为水下传感器网络、水下路由协议

    羊秋玲:女,博士,教授,研究方向为水下传感器网络、网络空间安全、水下传感器网络与网络信息安全

    通讯作者:

    金志刚 zgjin@tju.edu.cn

  • 中图分类号: TN91; TP393

Opportunistic Routing in Underwater Acoustic Networks Fusing Depth Adjustment and Adaptive Forwarding

Funds: The National Natural Science Foundation of China (52171337, 61862020)
  • 摘要: 针对水声传感器网络路由过程中的空洞问题和数据传输中能效低下的问题,该文提出了融合深度调整和自适应转发的水声网络机会路由(OR-DAAF)。针对路由空洞,区别于传统绕路策略,OR-DAAF提出一种基于拓扑控制的空洞恢复模式算法—利用剩余能量对空洞节点分级,先后调整空洞节点到新的深度以克服路由空洞,恢复网络联通。针对数据传输中的能效低下问题,OR-DAAF提出了转发区域划分机制,通过转发区域的选择自适应转发面积以抑制冗余包,并提出基于加权推进距离,能量和链路质量的多跳多目标路由决策指标,综合考虑区域能量,链路质量和推进距离实现能效平衡。实验数据表明,相比DVOR协议,OR-DAAF的包投递率和生命周期分别提高10%和48.7%,端到端时延减少22%。
  • 图  1  网络模型

    图  2  OR-DAAF算法流程图

    图  3  转发区域划分机制

    图  4  决策指标计算

    图  5  拓扑消息的获取

    图  6  不同协议PDR

    图  7  不同协议端到端时延

    图  8  不同协议能量消耗

    图  9  不同协议网络生命周期

    图  10  网络流量对OR-DAAF的影响

    表  1  符号集

    符号含义
    ${n_i}$i个节点
    $ {\text{PF}}{{\text{A}}_i} $, $ {\text{AF}}{{\text{A}}_i} $ni的主、辅助转发区域
    ${\text{N}}{{\text{e}}_i}$, ${\text{Nsn}}{{\text{r}}_i}$ni的能量、信噪比候选集
    ${\text{Npf}}{{\text{a}}_i}$, ${\text{Naf}}{{\text{a}}_i}$ni的主、辅助转发候选集
    $G({n_i})$, $E({n_i})$ni的邻居数、剩余能量
    $\varOmega , \;\varPhi$ ni的两跳邻居集、深度集
    $E{{\text{(}}{n_i}{\text{)}}^{{\text{NF}}}}$ni的邻居集能量
    $ {\text{TF}}{{\text{A}}_i} $ni的综合转发区域
    $d_{ {{jk} } }^{ {\text{NF} } }$nj的所有邻居节点nknj的距离
    ${(d'_{ {{jk} } })^{ {\text{NF} } } }$nj的所有邻居节点nk到平面P的距离
    下载: 导出CSV
    算法1: 构建候选集
    1: for each node ${n_j} \in {N_i}(t)$
    2:  if ${n_j} \in {\text{PF}}{{\text{A}}_i}$
    3:  then add ${n_j} \to {\text{Npf}}{{\text{a}}_i}$
    4: if $E({n_j}) \ge \displaystyle\sum\limits_k {E({n_k})/2G({n_i})}$
    5:   then add $n_{j} \rightarrow {\rm{N} }e_{i}$
    6:  if ${\text{SNR} }({n_i},{n_j}) \ge \displaystyle\sum\limits_k { {\text{SNR} }({n_i},{n_k})/2{{G} }({n_i})}$
    7:  then add $n_{j} \rightarrow {\rm{Nsnr} }_{i}$
    8: if $[{n_j} \in {\text{Npf} }{ {\text{a} }_i}] \vee [{n_j} \in {\text{N} }{ {{e} }_i}] \vee [{n_j} \in {\text{Nsn} }{ {\text{r} }_i}]$
    9:  then add $ n_{j} \rightarrow C(i) $
    10: end for
    11: if $C(i) = \varnothing $ the forwarding area is $ {\text{PF}}{{\text{A}}_i} $
    12:  then replace $ {\text{PF}}{{\text{A}}_i} $ to $ {\text{TF}}{{\text{A}}_i} $
    13:  and switch to Algorithm 1 again
    14: else if $C(i) = \varnothing $ the forwarding area is $ {\text{TF}}{{\text{A}}_i} $
    15: switch to the Algorithm 2
    下载: 导出CSV
    算法 2: 空洞恢复算法
     1: if $|\varOmega| > 0$
     2:  for $n_{k} \in \varOmega$
     3:   if $ E(n)<0.5 E_{\text {mat }} $
     4:    then wait $ 2R/{v_0} $ and switch to Algorithm 2
     5:   else if
        $ \left\{\begin{array}{c}\left(x_{D}-x_{i}\right)\left(x_{k}-x_{i}\right)+\left(y_{D}-y_{i}\right)\left(y_{k}-y_{i}\right) \\+\left(z_{D}-z_{i}^{*}\right)\left(z_{k}-z_{i}^{*}\right)>0 \\0<\sqrt{\left(x_{k}-x_{i}\right)^{2}+\left(y_{k}-y_{i}\right)^{2}+\left(z_{k}-z_{i}^{*}\right)^{2}} \\\leq R\end{array}\right\} $
     6:    then $z_{i}^{*} \rightarrow \varPhi$
     7:   end if
     8:  end for
     9: $\hat z = \arg {\min _{\forall z_i^* \in \varPhi } }\{ |z_i^* - {z_i}|\}$
    下载: 导出CSV

    表  2  实验参数设置

    参数取值
    通信范围1.5 km
    发送功率2 W
    接收功率0.75 W
    待机功率0.008 W
    深度移动能耗1.2 J/m
    数据包大小100 Byte
    下载: 导出CSV
  • [1] SHEN Zhongwei, YIN Hongxi, JING Lianyou, et al. A cooperative routing protocol based on Q-learning for underwater optical-acoustic hybrid wireless sensor networks[J]. IEEE Sensors Journal, 2022, 22(1): 1041–1050. doi: 10.1109/JSEN.2021.3128594
    [2] 黄沛烁, 王易因, 关新平, 等. 面向水声传感网的自主水下航行器辅助定位动态路径规划[J]. 电子与信息学报, 2022, 44(6): 1927–1936. doi: 10.11999/JEIT211432

    HUANG Peishuo, WANG Yiyin, GUAN Xinping, et al. Dynamic path planning for autonomous underwater vehicle assisted localization of underwater acoustic aensor networks[J]. Journal of Electronics &Information Technology, 2022, 44(6): 1927–1936. doi: 10.11999/JEIT211432
    [3] WANG Qingwen, LI Jianghui, QI Qian, et al. An adaptive-location-based routing protocol for 3-D underwater acoustic sensor networks[J]. IEEE Internet of Things Journal, 2021, 8(8): 6853–6864. doi: 10.1109/JIOT.2020.3034880
    [4] HAO Kun, DING Yuying, LI Cheng, et al. An energy-efficient routing void repair method based on an autonomous underwater vehicle for UWSNs[J]. IEEE Sensors Journal, 2021, 21(4): 5502–5511. doi: 10.1109/JSEN.2020.3030019
    [5] STOJANOVIC M and PREISIG J. Underwater acoustic communication channels: Propagation models and statistical characterization[J]. IEEE Communications Magazine, 2009, 47(1): 84–89. doi: 10.1109/MCOM.2009.4752682
    [6] SU Yishan, GUO Lei, JIN Zhigang, et al. A voronoi-based optimized depth adjustment deployment scheme for underwater acoustic sensor networks[J]. IEEE Sensors Journal, 2020, 20(22): 13849–13860. doi: 10.1109/JSEN.2020.3005685
    [7] NOH Y, LEE U, WANG P, et al. VAPR: Void-aware pressure routing for underwater sensor networks[J]. IEEE Transactions on Mobile Computing, 2013, 12(5): 895–908. doi: 10.1109/TMC.2012.53
    [8] GUAN Quansheng, JI Fei, LIU Yun, et al. Distance-vector-based opportunistic routing for underwater acoustic sensor networks[J]. IEEE Internet of Things Journal, 2019, 6(2): 3831–3839. doi: 10.1109/JIOT.2019.2891910
    [9] WANG Zhuo, HAN Guangjie, QIN Hongde, et al. An energy-aware and void-avoidable routing protocol for underwater sensor networks[J]. IEEE Access, 2018, 6: 7792–7801. doi: 10.1109/ACCESS.2018.2805804
    [10] COUTINHO R W L, BOUKERCHE A, VIEIRA L F M, et al. Design guidelines for opportunistic routing in underwater networks[J]. IEEE Communications Magazine, 2016, 54(2): 40–48. doi: 10.1109/MCOM.2016.7402259
    [11] YAN Hai, SHI Z J, and CUI J H. DBR: Depth-based routing for underwater sensor networks[C]. Proceedings of the 7th International Conference on Research in Networking, Singapore, 2008: 72–86.
    [12] XIE Peng, CUI Junhong, and LAO Li. VBF: Vector-based forwarding protocol for underwater sensor networks[C]. Proceedings of the 5th International Conference on Research in Networking, Coimbra, Portugal, 2006: 1216–1221.
    [13] NICOLAOU N, SEE A, XIE P, et al. Improving the robustness of location-based routing for underwater sensor networks[C]. Proceedings of the Oceans 2007-Europe, Aberdeen, UK, 2007: 1–6.
    [14] COUTINHO R W L, BOUKERCHE A, VIEIRA L F M, et al. Geographic and opportunistic routing for underwater sensor networks[J]. IEEE Transactions on Computers, 2016, 65(2): 548–561. doi: 10.1109/TC.2015.2423677
    [15] JAFFE J and SCHURGERS C. Sensor networks of freely drifting autonomous underwater explorers[C]. Proceedings of the 1st International Workshop on Underwater Networks, Los Angeles, USA, 2006: 93–96.
    [16] JIN Zhigang, DUAN Chenxu, YANG Qiuling, et al. Q-learning-Based Opportunistic Routing with an on-site architecture in UASNs[J]. Ad Hoc Networks, 2021, 119: 102553. doi: 10.1016/j.adhoc.2021.102553
    [17] HU Tiansi and FEI Yunsi. QELAR: A machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensor networks[J]. IEEE Transactions on Mobile Computing, 2010, 9(6): 796–809. doi: 10.1109/TMC.2010.28
    [18] YU Haitao, YAO Nianmin, WANG Tong, et al. WDFAD-DBR: Weighting depth and forwarding area division DBR routing protocol for UASNs[J]. Ad Hoc Networks, 2016, 37: 256–282. doi: 10.1016/j.adhoc.2015.08.023
    [19] BOUKERCHE A and DAREHSHOORZADEH A. Opportunistic routing in wireless networks: Models, algorithms, and classifications[J]. ACM Computing Surveys, 2014, 47(2): 22. doi: 10.1145/2635675
    [20] ZHANG Ying, ZHANG Zheming, CHEN Lei, et al. Reinforcement learning-based opportunistic routing protocol for underwater acoustic sensor networks[J]. IEEE Transactions on Vehicular Technology, 2021, 70(3): 2756–2770. doi: 10.1109/TVT.2021.3058282
  • 加载中
图(10) / 表(4)
计量
  • 文章访问数:  333
  • HTML全文浏览量:  186
  • PDF下载量:  70
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-01-17
  • 修回日期:  2023-05-04
  • 网络出版日期:  2023-05-09
  • 刊出日期:  2024-01-17

目录

    /

    返回文章
    返回