An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks

doi:10.7652/xjtuxb201806014

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An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks

更新时间：2025-07-09

- An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks
- Vol. 52, Issue 6, Pages: 91-97(2018)
- 作者机构：
  
  1. 国防科技大学电子对抗学院,合肥,230037
  2. 安徽省电子制约技术重点实验室,合肥,230037
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201806014
  CLC： TN975
- Online First：10 June 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks[J]. 2018, 52(6): 91-97.
DOI：

An Algorithm of Multi-Nodes Jamming Decision in Blind Wireless Ad-hoc Networks[J]. 2018, 52(6): 91-97. DOI： 10.7652/xjtuxb201806014.

摘要

为满足战场环境下无线自组网络通信拒止的干扰需求

提出了一种未知拓扑无线自组网络多节点干扰决策算法(CUCB)。首先

根据战场无线自组网络结构特点构建泊松点过程(PPP)网络模型

并利用其模拟网络中数据流传输过程; 其次

随机对PPP网络中多个节点进行干扰

通过监听确认帧信息或侦察节点活跃度判断阻断网络流数

根据干扰结果构造节点相关性矩阵; 最后

利用强化学习与环境实时交互的特点

在干扰过程中不断更新节点相关性矩阵并将其用于后续节点选择。所提算法无需获悉目标网络拓扑结构、节点重要性等先验信息

仅以阻断网络流数目或节点活跃性作为奖赏标准

适用网络类型更为广泛。仿真结果表明

对不同参数下的无线自组网络进行干扰

所提算法在鲁棒性方面优于现有算法

在累积阻断网络流数量方面比联合利用探索算法提高了27.1%。

Abstract

An algorithm focus on multi-nodes jamming decision is proposed to fulfill the need of interdicting information transmission in wireless Ad-hoc networks. Firstly

a model of Poisson point process(PPP)network is constructed in terms of the structure of wireless Ad-hoc networks

and then it is used to simulate the process of data transmission. Secondly

random interference to multiple nodes are performed

and the number of stopped network flows is counted by monitoring ACK information or reconnoitering nodes activities. A correlation matrix is constructed from jamming effects. Finally

the correlation matrix is continuously updated in jamming process by taking the advantage of interaction in time of reinforcement learning and is used for the selection of subsequent nodes. The proposed algorithm does not need to have a priori knowledge of information such as network topology or importance of nodes

and interaction is only needed in learning nodes correlation matrix which would be helpful when choose nodes to jam. Simulation results by jamming wireless Ad-hoc in different circumstances and a comparison with the joint slotted exploit explore learning show that the proposed interdiction algorithm increases by 27.1% in accumulate stopped flows

and its robustness is superior to existing algorithms.

关键词

Keywords

references

刘蔚, 赵宇, 陈锐. 无线Ad-hoc网络中基于0-1优化的两步骤资源分配算法 [J]. 计算机科学, 2017, 44(1): 103-108.

LIU Wei, ZHAO Yu, CHEN Rui. Zero-one integer programming based optimization model and two phase resource optimization algorithm for wireless Ad-hoc networks [J]. Computer Sciences, 2017, 44(1): 103-108.

MUKHERJEE A, KESHARY V, PANDYA K. Flying Ad-hoc networks: a comprehensive survey [C]∥3rd International Conference on Computer and Communication Technologies. Berlin, Germany: Springer, 2016: 1254-1270.

AMURU S, BUEHRER R M. Optimal jamming using delayed learning [C]∥Proceedings of 33rd Annual IEEE Military Communications Conference. Piscataway, NJ, USA: IEEE, 2014: 1528-1533.

MOON A H, IQBAL U, BASHIR A, et al. Simulating and analyzing RREQ flooding attack in wireless sensor networks [C]∥ International Conference on Electrical, Electronics, and Optimization Techniques. Piscataway, NJ, USA: IEEE, 2016: 3374-3377.

刘书建, 吴晟. 基于Zamp的Dos攻击可行性分析与研究 [J]. 化工自动化及仪表, 2016, 43(7): 725-727.

LIU Shujian, WU Sheng. Analysis and study of Dos attack feasibility based on Zmap [J]. Control and Instruments in Chemical Industry, 2016, 43(7): 725-727.

PROANO A, LAZOS L. Selective jamming attacks in wireless network [C]∥ 2010 IEEE International Conference on Communication. Piscataway, NJ, USA: IEEE, 2010: 11412483.

SEFAIR J A, SMITH J C. Dynamic shortest-path interdiction [J]. Networks, 2016, 68(4): 315-330.

ALTNER D S, ERGUN O, UHAN N A. The maximum flow network interdiction problem: valid inequalities, integrality gaps, and approximability [J]. Operations Research Letters, 2010, 38(1): 33-38.

段勇, 崔宝侠, 徐心和. 多智能体强化学习及其在足球机器人角色分配中的应用 [J]. 控制理论与应用, 2009, 26(4): 371-376.

DUAN Yong, CUI Baoxia, XU Xinhe. Multi-agent reinforcement learning and its application to role assignment of robot soccer [J]. Control Theory Application, 2009, 26(4): 371-376.

赵冬斌, 邵坤, 朱圆恒, 等. 深度强化学习综述: 兼论计算机围棋的发展 [J]. 控制理论与应用, 2016, 33(6): 701-717.

ZHAO Dongbin, SHAO Kun, ZHU Yuanheng, et al. Review of deep reinforcement learning and discussion on the development of computer Go [J]. Control Theory Applications, 2016, 33(6): 701-717.

丁乐乐. 基于深度学习和强化学习的车辆定位与识别 [D]. 成都: 电子科技大学, 2016: 42-60.

AMURU S, MICHAEL B R, VAN DER SCHAAR M. Blind network interdiction strategies: a learning approach [J]. IEEE Transactions on Cognitive Communications and Networking, 2015, 1(4): 435-449.

GAI Y, KRISHNAMACHARI B, JAIN R. Combinatorial network optimization with unknown variables: multi-armed bandits with linear reward [J]. Networking IEEE/ACM Transaction on Networking, 2012, 20(5): 1466-1478.

张国峰. 无人机自组网路由协议研究 [D]. 沈阳: 沈阳工业大学, 2017: 26-45.

蒲潇. 战术自组网网络结构及分群算法研究 [D]. 大连: 大连理工大学, 2009: 14-15.

BRANDES U, BORGATTI S P, FREEMAN L C. Maintain the duality of closeness and betweenness centrality [J]. Social Networks, 2016, 44: 153-159.

AUER P, CESA B N, FISCHER P. Finite-time analysis of the multi-armed bandit problem [J]. Machine Learning, 2002, 47(2/3): 2-13.

高阳, 陈世福, 陆鑫. 强化学习研究综述 [J]. 自动化学报, 2004, 30(1): 86-100.

GAO Yang, CHWN Shifu, LU Xin. Research on reinforcement learning technology: a review [J]. Acta Automatica Sinica, 2004, 30(1): 86-100.

颛孙少帅,杨俊安,刘辉,等.采用双层强化学习的干扰决策算法.2018,52(2):63-69.[doi:10.7652/xjtuxb201802010]

李清伟,郭黎利.DS-CDMA系统多波形优化的多址干扰抑制方法.2017,51(10):94-99.[doi:10.7652/xjtuxb201710 016]

杨少奇,田波,周瑞钊.应用双谱分析和分形维数的雷达欺骗干扰识别.2016,50(12):128-135.[doi:10.7652/xjtuxb2016 12020]

刘立,张衡阳,毛玉泉,等.变换域通信系统抗干扰编码幅度谱成型算法.2017,51(2):91-96.[doi:10.7652/xjtuxb2017 02015]

孙黎,徐洪斌.协作式终端直通系统中星座旋转辅助的干扰避免策略.2015,49(12):6-11.[doi:10.7652/xjtuxb201512 002]

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