期刊文献+

伯努利节点网络模型的拓扑鲁棒性分析方法 被引量:3

Analysis Method of Robustness for Topology of Bernoulli Node Model
下载PDF
导出
摘要 基于网络连通和恢复能力提出连接鲁棒性和恢复鲁棒性两种测度指标,根据随机故障和恶意攻击两种网络失败类型将连接鲁棒性分为随机故障鲁棒性和恶意攻击鲁棒性,将恢复鲁棒性分为随机故障节点恢复鲁棒性、随机故障边恢复鲁棒性、恶意攻击节点恢复鲁棒性、恶意攻击边恢复鲁棒性,并给出了这六个测度指标的确切定义.利用这六个测度指标分析了伯努利节点网络模型的拓扑鲁棒性,得出不同情形下拓扑结构与这六个测度指标的关系,结果表明:无线网络平面拓扑结构的恶意攻击鲁棒性要优于层次拓扑结构,而其随机故障鲁棒性要劣于层次拓扑结构;平面拓扑结构的边恢复鲁棒性要优于层次拓扑结构,而其节点恢复鲁棒性要劣于层次拓扑结构. Robustness is divided into connectivity robustness and recovery robustness according to connectivity and recovery.Then we divide connectivity robustness into random-fault robustness and hostile-attacks robustness,and divide recovery robustness into node recovery robustness and edge recovery robustness according to type of network failure,besides we define the concepts of connectivity robustness and recovery robustness.Then these concepts are used to evaluate the degree of robustness of Bernoulli node model,and the conclusion is achieved that the hostile-attacks robustness of hierarchy structure is less robust than planar structure while the random-fault robustness is more robust than planar structure,and that the node recovery robustness of hierarchy structure is more robust than planar structure while edge recovery robustness is less robust than planar structure.
出处 《电子学报》 EI CAS CSCD 北大核心 2011年第7期1673-1678,共6页 Acta Electronica Sinica
基金 国家863高技术研究发展计划基金(No.2007AA01Z429) 国家自然科学基金(No.60972078) 甘肃省高等学校基本科研业务费基金(No.0914ZTB186) 甘肃省自然科学基金(No.2007GS04823) 兰州理工大学博士基金(No.BS14200901) 网络安全与密码技术福建省高校重点实验室开放课题(No.09A006)
关键词 无线通信网络 连接鲁棒性 恢复鲁棒性 伯努利节点模型 wireless network connectivity robustness recovery robustness Bernoulli node model
  • 相关文献

参考文献10

  • 1杜巍,蔡萌,杜海峰.网络结构鲁棒性指标及应用研究[J].西安交通大学学报,2010,44(4):93-97. 被引量:27
  • 2王良民,马建峰,王超.无线传感器网络拓扑的容错度与容侵度[J].电子学报,2006,34(8):1446-1451. 被引量:22
  • 3Wang Bing,Tang Huanwen,Guo Chonghui,et al.Optimizationof network structure to random failure. Physical A:Statisti-cal Mechanics and its Application . 2007
  • 4Kwon Y K,Cho K H.Analysis of feedback loops and robust-ness in network evolution based an Booleam model. BMCBioinformatics . 2007
  • 5Peng-jun Wan,Chih-wei Yi.Asymptotic critical transmissionrange for connectivity in wireless ad hoc networks withBernoulli nodes. Proceedings of the5th ACM InternationalSymposiumon Mobile ad hoc Networking and Computing (MOB IHOC2004) . 2004
  • 6Bohannon J.Counterterrorism’s new tool:‘metanetwork’analysis. Science . 2009
  • 7Peter S D,Duncan J W,Charles F S.Information exchange and robustness of organizational Networks. Proceedings of the National Academy of Sciences of the United States of America . 2003
  • 8Ash J,Newth D.Optimizing complex networks for resilience against cascading failure. Physica a-Statistical Mechanics and Its Applications . 2007
  • 9Newman, M.E.J,Barabasi, A,Watts, D.J.The Structure and Dynamics of Networks. . 2006
  • 10Albert R,Jeong H,Barabási A-L.Error and attack tolerance of complex networks. Nature . 2000

二级参考文献22

  • 1NEWMAN M E J,BARABASI A L,Watts D J.The structure and dynamic of networks[M].Princeton,NJ,USA:Princeton University Press,2006.
  • 2ALBERT R,JEONG H,BARABASI A L.Attack and error tolerance in complex networks[J].Nature,2000,406(6794):387-482.
  • 3KWON Y K,CHO K H.Analysis of feedback loops and robustness in network evolution based on Boolean models[J].BMC Bioinformatics,2007,8 (9):430-438.
  • 4ASH J,NEWTH D.Optimizing complex networks for resilience against cascading failure[J].Physica A Statistical Mechanics and its Applicatioas,2007,380(7):673-683.
  • 5GAO Liang,LI Menhui,WU Jinshan,et al.Between-ness-based attacks on nodes and edges of food weds,dynamics of continuous[J].Discrete and Impulsive Systems:Series B,2006,13(3):421-428.
  • 6WANG Bing,TANG Huanwen,GUO Chonghui,et al.Optimization of network structure to random failures[J].Physica A:Statistical Mechanics and its Applications,2006,368(2):607-614.
  • 7DODDS S P,WATTS D J,SABEL F C.Information exchange and robustness of organizational networks[J].Proceedings of the National Academy of Sciences,2003,100(21):12516-12521.
  • 8BOHANNON J.Counterterrorism's new tool:'meta-network' analysis[J].Science,2009,325(5939):409-411.
  • 9BARABASI A L,ALBERT R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
  • 10WATTS D J,STROGATZ S H.Collective dynamics of 'small-world' networks[J].Nature,1998,393 (6684):440-442.

共引文献47

同被引文献24

  • 1王一平,黄建中.离心机在制药工业应用中的技术要求[J].过滤与分离,2005,15(4):37-39. 被引量:4
  • 2周知进,傅彩明.卧螺离心机转鼓主要参数对其模态的影响[J].机械设计,2006,23(9):28-30. 被引量:8
  • 3Akyildiz I F, Su W, Sankarasubramaniam Y, et al. Wireless sensor networks: a survey. Comput Netw, 2002, 38: 393-422.
  • 4Pottie G J, Kaiser W J. Wireless integrated network sensors. Commun ACM, 2000, 43: 51-58.
  • 5Cullar D, Estrin D, Strvastava M. Guest editor's introduction: overview. IEEE Comput Soc, 2004, 37: 41-49.
  • 6Enz C C, EI-Hoiydi A, Decotignia J D, et al. WiseNET: an ultralow-power wireless sensor network solution. IEEE Comput Soc, 2004, 37: 62-70.
  • 7Helmy A. Small worlds in wireless networks. IEEE Commun Lett, 2003, 7: 490-492.
  • 8Cui S, Goldsmith A J, Bahai A. Energy-constrained modulation optimization. IEEE Trans Wirel Commun, 2005, 4: 2349-2360.
  • 9Zhu J, Papavassiliou S, Xu S. Modeling and analyzing the dynamics of mobile wireless sensor networking infrastructures. In: Proceedings of Vehicular Technology Conference, 2002. 1550-1554.
  • 10Zhao Y, Wu J, Li F, et al. On maximizing the lifetime of wireless sensor networks using virtual backbone scheduling. IEEE Trans Parall Distr, 2012, 23: 1528-1535.

引证文献3

二级引证文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部