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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (8): 1536-1545    DOI: 10.3785/j.issn.1008-973X.2019.08.012
Computer and Control Engineering     
Repair strategy of military communication network
Guan-yu CHEN1,2(),Peng SUN1,3,*(),Jie-yong ZHANG1,Jun-sheng WU4
1. College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2. Unit 95816 of People’s Liberation Army, Hubei 432700, China
3. Department of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
4. Department of Software Micro-electronics, Northwestern Polytechnical University, Xi’an 710072, China
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Abstract  

The network repair problem after the nodes in the military communication network were hit was described, and the topology of the communication network was repaired by using the network edge-adding method. An edge addition repair model was established with maximizing network invulnerability as objective function, network connection cost and network connectivity as constraints. The network connection cost model considering redundant and necessary edges was defined. A model solving method based on the discrete artificial bee colony algorithm was proposed. Through specific cases of military communication network, simulation experiments were conducted under random and deliberate attacks, respectively. In the experiment, the proposed method was compared with other edge-adding methods, such as random addition, low degree first addition and low betweenness addition. Results showed that the proposed method can improve the survivability of network and the result was better than that of other three methods.



Key wordsmilitary communication network      repair model      repair strategy      edge addition      discrete artificial bee colony algorithm      information flow     
Received: 03 July 2017      Published: 13 August 2019
CLC:  TN 915  
Corresponding Authors: Peng SUN     E-mail: ChenPTN@163.com;Sunypt@163.com
Cite this article:

Guan-yu CHEN,Peng SUN,Jie-yong ZHANG,Jun-sheng WU. Repair strategy of military communication network. Journal of ZheJiang University (Engineering Science), 2019, 53(8): 1536-1545.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.08.012     OR     http://www.zjujournals.com/eng/Y2019/V53/I8/1536


军事通信网络修复策略

描述军事通信网络中节点遭受打击后的网络修复问题,采用网络加边的方法对通信网络的拓扑结构进行修复;建立以最大化网络抗毁性为目标函数,网络连接成本和网络连通为约束的增边修复模型;定义考虑冗余边和必须边的网络连接成本;设计基于离散人工蜂群算法的模型求解算法. 通过具体的军事通信网络案例,在随机攻击和故意攻击2种典型攻击策略下进行仿真实验. 在实验中,与随机加边、低度数加边以及低介数加边方法进行对比,结果表明采用所提出方法修复后的网络抗毁性更高,具有一定的优越性.


关键词: 军事通信网络,  修复模型,  修复策略,  增边,  离散人工蜂群算法,  信息流 
Fig.1 Relationship between entities in C2 organization
Fig.2 Information flow in C2 organization
Fig.3 Connection relationships between communication entities
Fig.4 Communication structure of C2 organization
序号 YN(1) YN(2) ··· YN(n ··· YN(Z
0?1值 x1 x2 ··· xn ··· xZ
Tab.1 Double-stranded coding structure
战术决策实体 所指控的平台
${\rm{TD}}{{\rm{M}}_{\rm{1}}}$ ${P_{\rm{1}}}{\rm{,}}{P_{{\rm{10}}}}{\rm{,}}{P_{{\rm{11}}}}{\rm{,}}{P_{{\rm{14}}}}{\rm{,}}{P_{{\rm{15}}}}$
${\rm{TD}}{{\rm{M}}_2}$ ${P_{\rm{2}}}{\rm{,}}{P_{\rm{6}}}{\rm{,}}{P_{\rm{8}}}{\rm{,}}{P_{{\rm{16}}}}{\rm{,}}{P_{{\rm{19}}}}$
${\rm{TD}}{{\rm{M}}_{\rm{3}}}$ ${P_{\rm{7}}}{\rm{,}}{P_{{\rm{12}}}}{\rm{,}}{P_{{\rm{13}}}}{\rm{,}}{P_{{\rm{20}}}}$
${\rm{TD}}{{\rm{M}}_{\rm{4}}}$ ${P_{\rm{3}}}{\rm{,}}{P_{\rm{4}}}{\rm{,}}{P_{\rm{5}}}{\rm{,}}{P_{\rm{9}}}{\rm{,}}{P_{{\rm{17}}}}{\rm{,}}{P_{{\rm{18}}}}$
Tab.2 Command and control of platform by TDM
决策实体 所连接的通信实体 决策实体 所连接的通信实体
${\rm{ODM}}$ ${C_{\rm{1}}}{\rm{,}}{C_{{\rm{24}}}}$ ${\rm{TD}}{{\rm{M}}_{\rm{3}}}$ ${C_{{\rm{36}}}}$
${\rm{TD}}{{\rm{M}}_{\rm{1}}}$ ${C_{{\rm{31}}}}{\rm{,}}{C_{{\rm{37}}}}$ ${\rm{TD}}{{\rm{M}}_{\rm{4}}}$ ${{\rm{C}}_{\rm{5}}}{\rm{,}}{{\rm{C}}_{\rm{7}}}$
${\rm{TD}}{{\rm{M}}_{\rm{2}}}$ ${C_{{\rm{40}}}}$
Tab.3 Connection relationship between DM and communication entity
平台实体 所连接的通信实体 平台实体 所连接的通信实体
${P_{\rm{1}}}$ ${C_{{\rm{21}}}}{\rm{,}}{C_{{\rm{22}}}}$ ${P_{{\rm{11}}}}$ ${C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{30}}}}$
${P_{\rm{2}}}$ ${C_{\rm{9}}}{\rm{,}}{C_{{\rm{10}}}}{\rm{,}}{C_{{\rm{22}}}}$ ${P_{{\rm{12}}}}$ ${C_{\rm{3}}}{\rm{,}}{C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{30}}}}$
${P_{\rm{3}}}$ ${C_{{\rm{11}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{32}}}}{\rm{,}}{C_{{\rm{38}}}}$ ${P_{{\rm{13}}}}$ ${C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{30}}}}$
${P_{\rm{4}}}$ ${C_{\rm{8}}}{\rm{,}}{C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{26}}}}$ ${P_{{\rm{14}}}}$ ${C_{{\rm{18}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{35}}}}$
${P_{\rm{5}}}$ ${C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{23}}}}{\rm{,}}{C_{{\rm{39}}}}$ ${P_{{\rm{15}}}}$ ${C_{\rm{6}}}{\rm{,}}{C_{{\rm{12}}}}{\rm{,}}{C_{{\rm{18}}}}{\rm{,}}{C_{{\rm{22}}}}$
${P_{\rm{6}}}$ ${C_{\rm{9}}}{\rm{,}}{C_{{\rm{19}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{39}}}}$ ${P_{{\rm{16}}}}$ ${C_{\rm{6}}}{\rm{,}}{C_{\rm{9}}}{\rm{,}}{C_{{\rm{14}}}}{\rm{,}}{C_{{\rm{22}}}}$
${P_{\rm{7}}}$ ${C_{\rm{9}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{23}}}}{\rm{,}}{C_{{\rm{26}}}}$ ${P_{{\rm{17}}}}$ ${C_{{\rm{19}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{30}}}}$
${P_{\rm{8}}}$ ${C_{\rm{3}}}{\rm{,}}{C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{30}}}}$ ${P_{{\rm{18}}}}$ ${C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{29}}}}{\rm{,}}{C_{{\rm{39}}}}$
${P_9}$ ${C_{{\rm{10}}}}{\rm{,}}{C_{{\rm{18}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{38}}}}$ ${P_{{\rm{19}}}}$ ${C_{{\rm{21}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{27}}}}{\rm{,}}{C_{{\rm{38}}}}$
${P_{{\rm{10}}}}$ ${C_{{\rm{18}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{26}}}}{\rm{,}}{C_{{\rm{30}}}}$ ${P_{{\rm{20}}}}$ ${C_{{\rm{17}}}}{\rm{,}}{C_{{\rm{22}}}}{\rm{,}}{C_{{\rm{35}}}}$
Tab.4 Connection relationship between platform and communication entity
Fig.5 Pre-destruction topology structure
Fig.6 Algorithm performance under random attack
Fig.7 Algorithm performance under deliberate attack
Fig.8 Variation of experimental results with cost under random attack
Fig.9 Variation of experimental results with cost under deliberate attack
Fig.10 Algorithm performance comparison under random attack
Fig.13 Variation of experimental results with cost upper under deliberate attack for different algorithms
Fig.11 Variation of experimental results with cost upper under random attack for different algorithms
Fig.12 Algorithm performance comparison under deliberate attack
Fig.14 Algorithm performance comparison of different weight coefficients under random attack
Fig.15 Algorithm performance comparison of different weight coefficients under deliberate attack
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