吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (12): 4072-4082.doi: 10.13229/j.cnki.jdxbgxb.20240355

• 通信与控制工程 • 上一篇    

基于多组分混合物扩散的无线传感器网络路由算法

李建坡1(),何敏1,于廷文2,杨月华1()   

  1. 1.东北电力大学 计算机学院，吉林省 吉林市 132012
    2.广东省数字电网技术企业重点实验室 南方电网数字电网研究院有限公司，广州 510000
  • 收稿日期:2024-04-06 出版日期:2025-12-01 发布日期:2026-02-03
  • 通讯作者: 杨月华 E-mail:jianpoli@163.com;yyh0504@126.com
  • 作者简介:李建坡（1980-），男，教授，博士.研究方向：无线传感器网络，5G移动通信.E-mail：jianpoli@163.com
  • 基金资助:
    国家自然科学基金项目(61501106)

Wireless sensor network routing algorithm based on diffusion of multi⁃component mixtures

Jian-po LI1(),Min HE1,Ting-wen YU2,Yue-hua YANG1()   

  1. 1.School of Computer Science，Northeast Electric Power University，Jilin 132012，China
    2.Digital Grid Research Institute，China Southern Power Grid，Guangdong Provincial Key Laboratory of Digital Grid Technology，Guangzhou，51000，China
  • Received:2024-04-06 Online:2025-12-01 Published:2026-02-03
  • Contact: Yue-hua YANG E-mail:jianpoli@163.com;yyh0504@126.com

RICH HTML

 0   

PDF (PC)

17

摘要：

为抑制无线传感器网络（WSN）能量空洞的形成和发展，均衡能量消耗和空洞分布，系统分析了空洞边缘存活区内节点密度、节点剩余能量和数据传输量以及多能量空洞距离等参数对能量空洞产生和发展的影响；为客观描述能量空洞特性，提出了能量空洞、融合能量空洞、空洞边缘域、空洞距离等一系列定义，借鉴多组分液体混合物扩散理论，通过计算能量空洞的扩散角度和扩散速度，构建了能量空洞演化模型。在此基础上，从基于能量空洞扩散系数的WSN分簇方法、抑制能量空洞的WSN数据传输优化方法和能量空洞边缘域内节点休眠调度策略三方面进行了研究。仿真结果表明：与LEACH、UCDTS和EHSRA等算法相比，本文算法在网络寿命、数据传输量、能耗均衡和空洞分布等方面均有显著提升。

关键词: 信息与通信系统, 无线传感器网络, 能量空洞, 动态分簇路由, 多组分混合物扩散

Abstract:

To inhibit the formation and development of energy holes in wireless sensor networks （WSNs） and to equalize the energy consumption and hole distribution， the effects of parameters such as node density， node residual energy， and data transmission within the survival zone of hole edges， as well as the distance of multi-energy holes， on the generation and development of energy holes are systematically analyzed. To objectively describe the characteristics of energy holes， a series of definitions such as energy hole， fusion energy hole， hole edge domain， and hole distance are proposed. Drawing on the theory of diffusion of multi-component liquid mixtures， an energy hole evolution model is constructed by calculating the diffusion angle and diffusion velocity of energy holes. On this basis， the WSN clustering method based on the diffusion coefficient of the energy hole， the WSN data transmission optimization method to suppress the energy hole， and the node dormant scheduling strategy in the edge domain of the energy hole are studied. Simulation results show that the proposed algorithm has significant improvement in terms of network lifetime， data transmission， energy equalization， and hole distribution compared with LEACH， UCDTS， and EHSRA algorithms.

Key words: communication and information systems, wireless sensor networks, energy hole, dynamic cluster routing, diffusion of multi-component mixtures

中图分类号: 

  • TN92

图1

能量空洞示意图"

图2

融合能量空洞示意图"

图3

空洞边缘死亡区示意图"

图4

空洞边缘存活区示意图"

图5

多能量空洞距离示意图"

图6

空洞扩散示意图"

图7

空洞扩散距离示意图"

图8

抑制能量空洞的WSN分簇流程图"

图9

每轮数据传输流程图"

图10

算法存活节点百分比的比较"

图11

算法成功发送数据量比较"

表1

算法在出现不同数量死亡节点时的平均剩余能量"

死亡节点占比/%LEACHUCDTSEHSRARADMCM
100.841 40.975 21.201 21.232 9
200.610 40.776 31.130 01.127 2
300.514 50.648 21.093 81.057 0
400.409 00.542 10.868 80.976 2
500.257 30.399 50.855 90.992 1

表2

算法在出现不同数量死亡节点时的能量极差"

死亡节点占比/%LEACHUCDTSEHSRARADMCM
101.306 91.477 51.581 91.504 1
201.190 21.329 91.488 11.350 8
300.999 51.265 61.418 51.226 0
400.933 81.172 11.290 51.057 4
500.704 01.075 31.227 00.676 1

表3

算法在出现不同数量死亡节点时的能量标准差"

死亡节点占比/%LEACHUCDTSEHSRARADMCM
100.439 60.513 20.597 70.469 8
200.372 40.4720.618 60.432 2
300.293 80.424 90.600 40.333 6
400.248 40.372 80.529 10.317 5
500.175 00.290 10.507 90.105 4

图12

不同算法在死亡节点占20%的等高线图对比图"

图13

不同算法在死亡节点占30%的等高线图对比图"

图14

不同算法在死亡节点占40%的等高线图对比图"

图15

不同算法在死亡节点占50%的等高线图对比图"

表4

不同算法在出现不同数量死亡节点时的能量空洞情况"

死亡节点占比/%LEACHUCDTSEHSRARADMCM
105（4）3（8）5（3）5（4）
208（5）3（17）4（6）5（6）
309（13）3（27）4（17）4（11）
408（21）6（33）4（29）3（20）
507（29）5（36）5（33）2（27）
[1] Li J P, Li G C, Chu S C, et al. Modified parallel tunicate swarm algorithm and application in 3D WSNs coverage optimization[J]. Journal of Internet Technology, 2022, 23(2): 227-244.
[2] 李建坡, 张庆华, 张展图, 等. 基于拥塞控制的无线传感器网络能耗优化路由算法[J]. 东北电力大学学报,2020, 40(4): 69-74.
Li Jian-po, Zhang Qing-hua, Zhang Zhan-tu, et al. Congestion control and energy optimization routing algorithm for wireless sensor networks[J]. Journal of Northeast Electric Power University, 2020, 40(4): 69-74.
[3] 李建坡, 刘琨, 朱伟华. 基于链路质量优化的无线传感器网络数据传输算法[J]. 吉林大学学报: 工学版,2024, 54(9): 2668-2675.
Li Jian-po, Liu Kun, Zhu Wei-hua. Link quality optimization based data transmission algorithm for wireless sensor network[J]. Journal of Jilin University (Engineering and Technology Edition),2024, 54(9): 2668-2675.
[4] Basumatary H, Debnath A, Deb Barma M K, et al. Performance analysis of centroid-based routing protocol in wireless sensor networks[J]. IETE Technical Review, 2024, 41(2): 187-199.
[5] Dholey M K, Sinha D, Mukherjee S, et al. MSHRP: mobile sink based limited hop routing protocol for wireless sensor networks[J]. Wireless Personal Communications, 2023, 133(1): 93-118.
[6] 刘涛, 庞博. 能耗均衡的低功耗自适应集分区路由改进算法[J]. 科学技术与工程, 2021, 21(31): 13447-13453.
Liu Tao, Pang Bo. Improved LEACH routing algorithm based on partitioning and energy consumption balance[J].Science Technology and Engineering, 2021, 21(31): 13447-13453.
[7] Edla D R, Lipare A, Parne S R. Load balanced cluster formation to avoid energy hole problem in WSN using fuzzy rule-based system[J]. Wireless Networks, 2023, 29(3): 1299-1310.
[8] Dogra R, Rani S, Sharma B, et al. A novel dynamic clustering approach for energy hole mitigation in Internet of Things‐based wireless sensor network[J]. International Journal of Communication Systems, 2021, 34(9): No.e4806.
[9] Aravind K, Maddikunta P K R. Dingo optimization based cluster based routing in internet of things[J]. Sensors, 2022, 22(20): No. 8064.
[10] Rao K R, Rahman M Z U, Satamraju K P, et al. Genetic algorithm for cross-layer-based energy hole minimization in wireless sensor networks[J]. IEEE Sensors Letters, 2022, 6(12): 1-4.
[11] Nayak C K, Das S. Minimization of energy holes with lively bonding and the division of coverage[J]. International Journal of e-Collaboration (IJeC), 2022, 18(2): 1-14.
[12] Li J P, Han Q, Wang W T. Characteristics analysis and suppression strategy of energy hole in wireless sensor networks[J]. Ad Hoc Networks, 2022, 135:No. 102938.
[13] Zhao X J, Xiong X X, Sun Z, et al. An immune clone selection based power control strategy for alleviating energy hole problems in wireless sensor networks[J]. Journal of Ambient Intelligence and Humanized Computing, 2020, 11: 2505-2518.
[14] Behera T M, Mohapatra S K. A novel scheme for mitigation of energy hole problem in wireless sensor network for military application[J]. International Journal of Communication Systems, 2021, 34(11):No. e4886.
[15] Lipare A, Edla D R, Dharavath R. Energy efficient routing structure to avoid energy hole problem in multi-layer network model[J]. Wireless Personal Communications, 2020, 112(4): 2575-2596.
[16] Chauhan V, Soni S. Energy aware unequal clustering algorithm with multi-hop routing via low degree relay nodes for wireless sensor networks[J]. Journal of Ambient Intelligence and Humanized Computing, 2021, 12: 2469-2482.
[17] Prakash P S, Nagaraju K, Reddy A V, et al. CDEIR: intelligent routing for efficient wireless sensor networks using BUG algorithm[J]. IEEE Access, 2023, 11: 72895-72910.
[18] Nagadivya S, Manoharan R. Energy efficient fuzzy logic prediction-based opportunistic routing protocol (EEFLPOR) for wireless sensor networks[J]. Peer-to-Peer Networking and Applications, 2023, 16(5): 2089-2102.
[19] Dogra R, Rani S, Sharma B, et al. A novel dynamic clustering approach for energy hole mitigation in Internet of Things‐based wireless sensor network[J]. International Journal of Communication Systems, 2021, 34(9): No. e4806.
[20] 孟建军, 王建明, 李德仓, 等. 铁路沿线线型无线传感器网络节点部署策略[J]. 传感技术学报, 2021, 34(6): 829-834.
Meng Jian-jun, Wang Jian-ming, Li De-cang, et al. The deployment strategy of wireless sensor network nodes along the railway[J]. Chinese Journal of Sensors and Actuators, 2021, 34(6): 829-834.
[21] 桂易琪, 叶子鉴, 陈永康. 一种基于区域划分和内容流行度的NDN缓存策略[J]. 江苏大学学报: 自然科学版, 2023, 44(2): 186-193.
Gui Yi-qi, Ye Zi-jian, Chen Yong-kang. Cache strategy of named-data networking based on community nodes division and content popularity[J]. Journal of Jiangsu University(Natural Science Edition), 2023, 44(2): 186-193.
[22] 吴永胜, 王维德. 多组分液体混合物扩散系数及其计算[J]. 广东化工, 2010, 37(9): 152-153.
Wu Yong-sheng, Wang Wei-de. Diffusion coefficients in multicomponent liquid mixture and their calculation[J]. Guangdong Chemical Industry, 2010, 37(9): 152-153.
[23] Lokhande M P, Patil D D. Enhancing the energy efficiency by LEACH protocol in the internet of things[J]. International Journal of Computational Science and Engineering, 2022, 25(1): 1-10.
[24] Li J P, Dong Z Q. Uneven clustering and data transmission strategy for energy hole problem in wireless sensor networks[J]. Journal of Information Hiding and Multimedia Signal Processing, 2017, 8(2): 500-509.
[1] 滕志军,于沥博,李明哲,苗润升,王继红. 融合交互信誉度与RSSR的WSNs女巫攻击检测策略[J]. 吉林大学学报(工学版), 2025, 55(7): 2455-2463.
[2] 李建坡,刘琨,朱伟华. 基于链路质量优化的无线传感器网络数据传输算法[J]. 吉林大学学报(工学版), 2024, 54(9): 2668-2675.
[3] 特木尔朝鲁朝鲁,张亚萍. 基于卷积神经网络的无线传感器网络链路异常检测算法[J]. 吉林大学学报(工学版), 2024, 54(8): 2295-2300.
[4] 王晓莹,陈小海. 嵌入式无线传感器网络多链路传输随机退避算法[J]. 吉林大学学报(工学版), 2024, 54(7): 2057-2062.
[5] 段锦,姚安妮,王震,于林韬. 改进的麻雀搜索算法优化无线传感器网络覆盖[J]. 吉林大学学报(工学版), 2024, 54(3): 761-770.
[6] 张楠,史建华,亿吉,王平. 基于加权质心定位的井下移动目标实时跟踪方法[J]. 吉林大学学报(工学版), 2023, 53(5): 1458-1464.
[7] 王军,徐彦惠,李莉. 低能耗支持完整性验证的数据融合隐私保护方法[J]. 吉林大学学报(工学版), 2022, 52(7): 1657-1665.
[8] 国强,崔玉强,王勇. 无线传感器网络中基于动态簇的节点调度算法[J]. 吉林大学学报(工学版), 2022, 52(6): 1466-1476.
[9] 于斌斌,胡亮,迟令. 可抵抗内外部攻击的无线传感器网络数字签名方案[J]. 吉林大学学报(工学版), 2019, 49(5): 1676-1681.
[10] 董颖, 崔梦瑶, 吴昊, 王雨后. 基于能量预测的分簇可充电无线传感器网络充电调度[J]. 吉林大学学报(工学版), 2018, 48(4): 1265-1273.
[11] 刘洲洲, 彭寒. 基于节点可靠度的无线传感器网络拓扑控制算法[J]. 吉林大学学报(工学版), 2018, 48(2): 571-577.
[12] 于斌斌, 武欣雨, 初剑峰, 胡亮. 基于群密钥协商的无线传感器网络签名协议[J]. 吉林大学学报(工学版), 2017, 47(3): 924-929.
[13] 董颖, 周占颖, 苏真真, 徐洋, 钱志鸿. 基于路由信息的无线传感器网络跨层MAC协议[J]. 吉林大学学报(工学版), 2017, 47(2): 647-654.
[14] 朱海洋, 张合, 马少杰. 增强型超声波周向旋转扫描节点参数优化[J]. 吉林大学学报(工学版), 2017, 47(1): 262-267.
[15] 刘洲洲, 王福豹. 改进的离散混合蛙跳算法压缩感知信号重构及应用[J]. 吉林大学学报(工学版), 2016, 46(4): 1261-1268.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发