基于模糊聚类和动态权重分配的水下传感器网络智能信任评估方法

王照辉; 韩光洁; 杜嘉欣; 林川; 王雷

doi:10.11993/j.issn.2096-3920.2024-0176

基于模糊聚类和动态权重分配的水下传感器网络智能信任评估方法

doi: 10.11993/j.issn.2096-3920.2024-0176

王照辉^1,,
韩光洁^2, ,,
杜嘉欣³,
林川⁴,
王雷¹

1.
大连理工大学软件学院, 辽宁大连, 116620
2.
河海大学信息科学与工程学院, 江苏常州, 213200
3.
浙江工业大学计算机科学与技术学院, 浙江杭州, 310023
4.
东北大学软件学院, 辽宁沈阳, 110169

基金项目: 国家自然科学基金区域创新发展联合基金(U22A2011).

详细信息

通讯作者:
韩光洁(1972-), 男, 博士, 教授, 主要研究方向为物联网、人工智能、工业互联网、智慧海洋及智能计算等.

中图分类号: TJ630.34; U666.74
计量
- 文章访问数: 91
- HTML全文浏览量: 28
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-20
- 修回日期: 2025-03-13
- 录用日期: 2025-03-17
- 网络出版日期: 2025-03-27

Intelligent Trust Evaluation Method for Underwater Sensor Networks Based on Fuzzy Clustering and Dynamic Weight Allocation

1.
School of Software, Dalian University of Technology, Dalian 116620, China
2.
College of Information Science and Engineering, Hohai University , Changzhou 213200, China
3.
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
4.
School of Software, Northeastern University, Shenyang 110169, China

摘要

摘要: 水下传感器网络(USN)在海洋环境监测等领域具有重要作用, 同时面临诸多的安全挑战。信任模型虽能有效抵御内部攻击, 保障网络可靠性，但现有信任评估方法大多依赖于信任特征的线性加权和阈值比较的决策方式。在水下动态环境中, 水流和温度等因素在时空上的不断变化, 会导致节点信任特征的变化差异和信任值的整体波动, 使得最优权重和合理阈值难以有效确定, 影响评估的准确性与决策的可靠性。为解决这一问题, 文中提出了一种基于模糊聚类和动态权重分配的智能信任评估方法。该方法首先对USN进行分层动态拓扑建模, 以增强普适性; 在此基础上综合计算节点的通信、能量和数据特征, 以全面反映节点的状态; 然后使用无监督机器学习算法模糊C均值聚类, 实现自适应的节点信任决策, 同时采用主客观结合策略, 根据网络和环境条件为特征动态分配权重, 从而实现对节点信任度的智能评估。仿真实验结果表明, 该方法能够有效评估水下环境中节点的信任度, 提高信任决策的可靠性, 增强网络安全性。
- 水下传感器网络 /
- 信任评估 /
- 模糊聚类 /
- 动态权重
Abstract: Underwater sensor networks (USN) play a crucial role in marine environmental monitoring and other fields while facing significant security challenges. Trust models can effectively defend against insider attacks and ensure network reliability. However, most existing trust evaluation methods rely on the linear weighting of trust features and the decision-making method based on threshold comparison. In dynamic underwater environments, factors such as water flow and temperature are constantly changing in time and space, leading to differences in the variations of node trust features and overall fluctuations of trust values. This makes it challenging to effectively determine the optimal weights and the reasonable threshold, thereby affecting the accuracy of evaluation and the reliability of decision-making. To solve this issue, this paper proposed an intelligent trust evaluation method based on fuzzy clustering and dynamic weight allocation. First, a hierarchical dynamic topology model of the USN was developed to enhance universality. On this basis, communication, energy, and data features were comprehensively calculated to fully reflect node states. Then, the unsupervised machine learning algorithm, namely fuzzy C-means clustering, was employed to enable adaptive node trust decision-making. Meanwhile, a subjective and objective combination strategy was adopted to dynamically allocate weights to features according to network and environmental conditions. Consequently, the intelligent evaluation of node trust was achieved. Simulation results demonstrate that the proposed method can effectively evaluate the trust of nodes in underwater environments, improve the reliability of trust decision-making, and enhance the security of the network.
- underwater sensor network /
- trust evaluation /
- fuzzy clustering /
- dynamic weight

HTML全文

图 1 考虑埃克曼漂流的USN模型图

Figure 1. USN model diagram based on the Ekman drift current

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图 2 埃克曼漂流模型图

Figure 2. Diagram of the Ekman drift current model

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图 3 信任评估方法流程图

Figure 3. Flow chart of the trust assessment method

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图 4 不同场景和方法下均值和标准差计算值图

Figure 4. Calculated values of mean and standard deviation under different scenarios and methods

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图 5 不同方法下正常节点数据信任特征值图

Figure 5. Graph of data trust eigenvalues of normal nodes under different methods

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图 6 选择转发攻击下节点平均信任值随时间周期变化曲线

Figure 6. Curves of average trust value of nodes with time period under selective forwarding attack

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图 7 拒绝服务攻击下节点平均信任值随时间周期变化曲线

Figure 7. Curves of average trust value of nodes with time period under denial of service attack

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图 8 选择转发攻击下恶意节点检测率随恶意节点比例变化曲线

Figure 8. Curves of malicious node detection rate versus proportion of malicious nodes under selective forwarding attack

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图 9 拒绝服务攻击下恶意节点检测率随恶意节点比例变化曲线

Figure 9. Curves of malicious node detection rate versus the proportion of malicious nodes under denial of service attack

下载: 全尺寸图片幻灯片

表 1 仿真参数列表

Table 1. Parameters of simulation

参数	取值
纬度/(°)	45
海面10米处风速/(m/s)	10
网络范围/m³	2 000×2 000×1000
节点数量/个	200
节点通信范围/m	500
节点初始能量/J	1000
节点通信频率/kHz	26
节点数据速率/(kbit/s)	10
数据包大小/Byte	100
评估周期/s	60

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表 2 不同方法下特征线性加权值

Table 2. Linear weighting of features under different methods

节点特征值	相等权重	综合权重
(0.90, 0.78, 0.10)	0.59	0.34
(0.68, 0.56, 0.17)	0.47	0.31
(0.77, 0.86, 0.62)	0.75	0.70
(0.73, 0.78, 0.18)	0.56	0.38
(0.77, 1.00, 0.04)	0.60	0.36
(0.82, 0.86, 0.24)	0.64	0.45
(0.81, 0.88, 0.34)	0.67	0.52
(0.76, 0.66, 0.56)	0.66	0.60
(0.84, 0.39, 0.02)	0.42	0.16
(0.83, 0.58, 0.53)	0.65	0.55

下载: 导出CSV

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