基于模拟退火自适应粒子群算法的WSN拓扑抗毁性方法研究

doi:10.3969/j.issn.1671-1122.2021.06.011

信息网络安全 ›› 2021, Vol. 21 ›› Issue (6): 89-96.doi: 10.3969/j.issn.1671-1122.2021.06.011

基于模拟退火自适应粒子群算法的WSN拓扑抗毁性方法研究

宋玉龙, 王磊(), 武欣嵘, 曾维军

陆军工程大学通信工程学院,南京 210007

收稿日期:2021-03-05 出版日期:2021-06-10 发布日期:2021-07-01
通讯作者: 王磊 E-mail:iponly@126.com
作者简介:宋玉龙（1995—）,男,安徽,硕士研究生,主要研究方向为网络智能运维管理、网络抗毁性|王磊（1983—）,男,江苏,讲师,博士,主要研究方向为系统仿真、优化理论|武欣嵘（1970—）,女,山东,副教授,硕士,主要研究方向为通信网络、网络安全|曾维军（1986—）,男,江西,讲师,博士,主要研究方向为数据挖掘、信号处理
基金资助:
国家自然科学基金(61702543);国家自然科学基金(61971439);江苏省自然科学基金(BK20191329);中国博士后科学基金(2019T120987);陆军工程大学基础前沿创新项目(KUYTYJQZL1906)

Research on WSN Topological Invulnerability Based on Adaptive Simulated Annealing Particle Swarm Optimization Algorithm

SONG Yulong, WANG Lei(), WU Xinrong, ZENG Weijun

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

Received:2021-03-05 Online:2021-06-10 Published:2021-07-01
Contact: WANG Lei E-mail:iponly@126.com

摘要/Abstract

摘要：

针对WSN节点易失效和损毁的特点,文章从拓扑结构角度研究WSN网络抗毁性。以网络自然连通度作为优化目标,主要考虑网络节点全连通、节点和链路数量、节点通信半径以及节点负载等约束,构建网络拓扑抗毁性优化模型。模型求解是NP-hard问题,文章提出了一种新的启发式算法——基于模拟退火自适应粒子群算法,该方法在粒子种群更新前,应用了模拟退火方法,替换了一部分适应度较差的粒子,克服了传统粒子群算法容易陷入局部最优的缺点,同时采用惯性权重自适应方法,保证了收敛速度。实验表明,该方法对网络拓扑抗毁性优化模型求解是有效的。通过使用不同策略对网络节点进行攻击分析网络抗毁性,验证所提算法优化得到的网络拓扑具有较高的抗毁性。

关键词: 粒子群算法, 模拟退火, 网络拓扑, 抗毁性

Abstract:

According to the characteristics of WSN nodes vulnerable to failure and damage, the invulnerability of WSN network was studied from the perspective of topology structure. Taking the natural connectivity of the network as the optimization objective, and considering the constraints of the total connectivity of the network nodes, the number of nodes and links, the communication radius of the nodes and the load of the nodes, a network topology invulnerability optimization model was established. Model was NP-hard problem, this paper proposed a new heuristic algorithm-adaptive simulated annealing particle swarm optimization algorithm, which applied the simulated annealing method before updating the particle population, to replace the part of fitness poor particles, overcome the traditional particle swarm optimization algorithm easy to fall into local optimum, and the inertia weight adaptation method was used to guarantee the convergence speed. Experimental results show that this method is effective to solve the network topology invulnerability optimization model. By using different strategies to attack the network nodes, the network topology optimized by the proposed algorithm is proved to be highly invulnerability.

Key words: particle swarm optimization, simulated annealing, network topology, invulnerability

中图分类号:

TP309

宋玉龙, 王磊, 武欣嵘, 曾维军. 基于模拟退火自适应粒子群算法的WSN拓扑抗毁性方法研究[J]. 信息网络安全, 2021, 21(6): 89-96.

SONG Yulong, WANG Lei, WU Xinrong, ZENG Weijun. Research on WSN Topological Invulnerability Based on Adaptive Simulated Annealing Particle Swarm Optimization Algorithm[J]. Netinfo Security, 2021, 21(6): 89-96.

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参数	符号	取值
网络节点数	N	110
网络总边数	M	260
节点通信半径	R	50
节点最大度值	L	50
种群编码长度	K	N(N-1)/2
种群规模	N₀	600
初始温度	T₀	1000
降温速率	q	0.6
粒子转换概率	p	0.01
速度更新上限	${{V}_{\max }}$	1
速度更新下限	${{V}_{\min }}$	-1
认知参数	${{c}_{1}}$	1.5
社会参数	${{c}_{2}}$	1.5
最大惯性权重	${{\omega }_{\max }}$	1.2
最小惯性权重	${{\omega }_{\min }}$	0.9
算法最大迭代次数	gen	600

算法	最优值	运行时间/s
模拟退火算法	11.459	1292
粒子群算法	10.900	1071
基于模拟退火自适应粒子群算法	12.775	1574

基于模拟退火自适应粒子群算法的WSN拓扑抗毁性方法研究

Research on WSN Topological Invulnerability Based on Adaptive Simulated Annealing Particle Swarm Optimization Algorithm

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