Anomaly Node Searching Algorithm of Wireless Sensor Networks Based on Adaptive Mechanism of Clustering Feature

DAI Chun-ni

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Anomaly Node Searching Algorithm of Wireless Sensor Networks Based on Adaptive Mechanism of Clustering Feature

更新时间：2026-09-04

- Anomaly Node Searching Algorithm of Wireless Sensor Networks Based on Adaptive Mechanism of Clustering Feature
- Vol. 34, Issue 4, Pages: 76-82(2017)
- 作者机构：
  
  1. 华东师范大学计算机科学与软件工程学院,上海,200241
  2. 上海建桥学院机构信息技术学院,上海,201306
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
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戴春妮. 基于聚类特征自适应机制的无线传感网异常节点检测算法[J]. 微电子学与计算机, 2017, 34(4): 76-82.

DAI Chun-ni. Anomaly Node Searching Algorithm of Wireless Sensor Networks Based on Adaptive Mechanism of Clustering Feature[J]. 2017, 34(4): 76-82.
戴春妮. 基于聚类特征自适应机制的无线传感网异常节点检测算法[J]. 微电子学与计算机, 2017, 34(4): 76-82. DOI：

DAI Chun-ni. Anomaly Node Searching Algorithm of Wireless Sensor Networks Based on Adaptive Mechanism of Clustering Feature[J]. 2017, 34(4): 76-82. DOI：

摘要

提出了一种基于聚类特征自适应机制的无线传感网节点检测算法.首先

基于节点聚类特征自适应机制

通过特征采样

不断获取异常节点的路由信息、链路抖动率以及数据分组传输率等关键参数信息

从而计算节点特征值

并联合矢量内积对异常节点进行识别

提高对异常节点的感知能力; 随后

采用分区聚类

对可能的疑似异常节点进行聚合提纯

降低网络对正常节点的误判

提高了异常节点的检出效率; 最后

定义异常节点检举规则

通过对分区进行筛选

有效降低了正常节点出现在异常节点集合中的概率

从而提高了算法对异常节点的搜寻效率.与当前PSO算法、SDR算法相比

此算法具有更高的异常节点检出率与更低的数据传输时延

能够有效避免异常节点对传输数据的篡改.

Abstract

This paper presents a method based on the clustering of the abnormal nodes report mechanism of networking malicious behavior selection algorithm. On the basis of node clustering analysis

through the feature sampling construction characteristics of the node values

and according to the actual situation taken to increase the key characteristic parameters of malicious node behavior to carry on the accurate localization; then constructs the cluster mechanism

take the clustering partition continue to suspected nodes aggregate

to further reduce the misjudgment; rules for the prosecution of the definition of malicious nodes

the partition selection further reduces the normal nodes appear in the malicious node in the collection probability and improve the algorithms perception of malicious nodes. Simulation results show that compared with the commonly used PSO algorithm and the SDR algorithm

the algorithm of the malicious node detection rate

and can effectively avoid tampering of malicious nodes to transmit data

has a strong practical value.

关键词

Keywords

references

Li Z, Xi L, Hong J. Cross-layer adaptive resource allocation algorithm with diverse QoS requirements for single-cell OFDMA systems[J]. Journal of Harbin institute of Technology, 2015, 1(1): 15-22. DOI: 15

Cai Z X. Dynamic cluster member selection method for multi-target tracking in wireless sensor network[J]. Journal of Central South University, 2014, 2(11): 636-645. DOI: 636

杨国宁, 冯秀芳, 樊刘娟. 一种基于最优融合集的多传感器数据搜寻算法[J]. 软件学报, 2012, 23(11): 134-140. DOI: 134

Sichitiu. Cross-layer scheduling for power efficiency in WSN[J]. Proceedings of the IEEEI NFO-COM, 2015, 6(9): 105-115. DOI: 105

Zhen X L. A survey on data dissemination in wireless sensor networks[J]. Journal of Computer Science % Technology, 2013, 3(11): 470-486. DOI: 470

Lin G M. Novel congestion control approach in wireless multimedia sensor networks[J]. The Journal of China Universities of Posts and Telecommunications, 2011, 18(2): 1-8. DOI: 1

Ahme A, Abu B K, Cha N M. A survey on trust based detection and isolation of malicious nodes in ad-hoc and sensor networks[J]. Frontiers of Computer Science, 2015, 12(7). 280-296. DOI: 280

Yang S, Qin B D. End-to-end rate-based congestion control with random loss: convergence and stability[J]. Journal of Systems Engineering and Electronics, 2008, 6(17): 1208-1214. DOI: 1208

Wang H, Jin S. An energy-efficient framework for clustering based data collection in wsn[J]. Parallel and Distributed Systems, IEEE Transactions, 2011, 22(6): 1064-1071. DOI: 1064

王翥, 王祁. 多约束容错性WSN中继节点搜寻算法的研究[J]. 电子学报, 2013, 1(2): 115-120. DOI: 115

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