搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于最小刚性图代数特性的无线网络拓扑优化算法

罗小元 李昊 马巨海

基于最小刚性图代数特性的无线网络拓扑优化算法

罗小元, 李昊, 马巨海
PDF
导出引用
导出核心图
  • 对于能量受限的无线传感器网络,拓扑优化能够降低能耗,优化通信链路结构.本文基于最小刚性图原理提出了一种新的拓扑优化算法,算法综合考虑了生成拓扑链路图中通信链路的权值与生成刚性图的代数特性问题,既保证了通信链路较短,有利于延长网络的生命周期,同时使生成的通信链路图结构更加稳定,网络具有较好的鲁棒性.仿真实验表明,与相关算法比较,提出的算法中通信链路较短,具有较好的网络连通性与结构稳定性,同时生成刚性图矩阵的迹较大,具有较好的刚度代数性能.
      通信作者: 罗小元, xyluo@ysu.edu.cn
    • 基金项目: 国家自然科学基金(批准号:61375105)资助的课题.
    [1]

    El Emary I M M, Al-Gamdi A H 2014 J. Appl. Med. Sci. 3 5

    [2]

    Li J, Wang F, Li X 2014 J. Networks 9 244

    [3]

    Xu G, Shen W, Wang X 2014 Sensors 14 16932

    [4]

    Luo X Y, Li S B, Guan X P 2009 IEEE Intelligent Vehicles Symposium Xi'an, China, June 6-9, 2009 p1198

    [5]

    Dhanapala D C, Jayasumana A P 2014 IEEEACM Trans. Network 22 784

    [6]

    Bagci H, Korpeoglu I, Yazici A 2015 Parallel and Distributed Systems 26 914

    [7]

    Wattenhofer R, Zollinger A 2004 Proceedings of the 18th International Parallel and Distributed Processing Symposium Santa Fe, USA, April 26-30, 2004 p216

    [8]

    Chen Y L 2013 IEEE 7th International Conference on Complex, Intelligent, and Software Intensive Systems Taichung, China, July 3-5, 2013 p335

    [9]

    Qin J, Fu W, Gao H 2016 IEEE Trans. Cybernetics 46 1

    [10]

    Song J, Luo Q H, Peng X Y 2014 Acta Phys. Sin. 63 128401 (in Chinese)[宋佳, 罗清华, 彭喜元2014物理学报 63 128401]

    [11]

    Hao X C, Liu W J, Xin M J 2015 Acta Phys. Sin. 64 080101 (in Chinese)[郝晓辰, 刘伟静, 辛敏洁2015物理学报 64 080101]

    [12]

    Fang B, Chen T F 2014 Control Eng. 21 178(in Chinese)[方斌, 陈特放2014控制工程 21 178]

    [13]

    Luo X Y, Yan Y L, Li S B 2013 Computer Networks 57 1037

    [14]

    Shames I, Summers T 2014 Sensor Array and Multichannel Signal Processing Workshop (SAM) A Coruna, Spain, June 22-25, 2014 p29

    [15]

    Zelazo D, Franchi A, Blthoff H H 2013 Int. J. Robot. Res. 34 105

    [16]

    Anderson B D O, Shames I, Mao G 2010 SIAM J. Discrete Mathematics 24 684

    [17]

    Shames I, Fidan B, Anderson B D O 2009 Automatica 45 1058

    [18]

    Shames I, Summers T H 2015 IEEE Trans. Network Sci. Eng. 2 84

    [19]

    Hendrickx J M, Anderson B, Blondel V D 2005 44th IEEE Conference on European Control CDC-ECC'05 Seville, Spain, December 12-15, 2005 p2176

    [20]

    Chen Z J, Ouyang Y L 2012 Computer Eng. 38 104

    [21]

    Heinzelman W R, Chandrakasan A, Balakrishnan H 2000 Proceedings of the 33rd Hawaii International Conference on System Sciences Maui, USA, January 4-7, 2000 p10

  • [1]

    El Emary I M M, Al-Gamdi A H 2014 J. Appl. Med. Sci. 3 5

    [2]

    Li J, Wang F, Li X 2014 J. Networks 9 244

    [3]

    Xu G, Shen W, Wang X 2014 Sensors 14 16932

    [4]

    Luo X Y, Li S B, Guan X P 2009 IEEE Intelligent Vehicles Symposium Xi'an, China, June 6-9, 2009 p1198

    [5]

    Dhanapala D C, Jayasumana A P 2014 IEEEACM Trans. Network 22 784

    [6]

    Bagci H, Korpeoglu I, Yazici A 2015 Parallel and Distributed Systems 26 914

    [7]

    Wattenhofer R, Zollinger A 2004 Proceedings of the 18th International Parallel and Distributed Processing Symposium Santa Fe, USA, April 26-30, 2004 p216

    [8]

    Chen Y L 2013 IEEE 7th International Conference on Complex, Intelligent, and Software Intensive Systems Taichung, China, July 3-5, 2013 p335

    [9]

    Qin J, Fu W, Gao H 2016 IEEE Trans. Cybernetics 46 1

    [10]

    Song J, Luo Q H, Peng X Y 2014 Acta Phys. Sin. 63 128401 (in Chinese)[宋佳, 罗清华, 彭喜元2014物理学报 63 128401]

    [11]

    Hao X C, Liu W J, Xin M J 2015 Acta Phys. Sin. 64 080101 (in Chinese)[郝晓辰, 刘伟静, 辛敏洁2015物理学报 64 080101]

    [12]

    Fang B, Chen T F 2014 Control Eng. 21 178(in Chinese)[方斌, 陈特放2014控制工程 21 178]

    [13]

    Luo X Y, Yan Y L, Li S B 2013 Computer Networks 57 1037

    [14]

    Shames I, Summers T 2014 Sensor Array and Multichannel Signal Processing Workshop (SAM) A Coruna, Spain, June 22-25, 2014 p29

    [15]

    Zelazo D, Franchi A, Blthoff H H 2013 Int. J. Robot. Res. 34 105

    [16]

    Anderson B D O, Shames I, Mao G 2010 SIAM J. Discrete Mathematics 24 684

    [17]

    Shames I, Fidan B, Anderson B D O 2009 Automatica 45 1058

    [18]

    Shames I, Summers T H 2015 IEEE Trans. Network Sci. Eng. 2 84

    [19]

    Hendrickx J M, Anderson B, Blondel V D 2005 44th IEEE Conference on European Control CDC-ECC'05 Seville, Spain, December 12-15, 2005 p2176

    [20]

    Chen Z J, Ouyang Y L 2012 Computer Eng. 38 104

    [21]

    Heinzelman W R, Chandrakasan A, Balakrishnan H 2000 Proceedings of the 33rd Hawaii International Conference on System Sciences Maui, USA, January 4-7, 2000 p10

  • 引用本文:
    Citation:
计量
  • 文章访问数:  1562
  • PDF下载量:  363
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-01-14
  • 修回日期:  2016-07-23
  • 刊出日期:  2016-12-05

基于最小刚性图代数特性的无线网络拓扑优化算法

  • 1. 燕山大学电气工程学院, 秦皇岛 066004
  • 通信作者: 罗小元, xyluo@ysu.edu.cn
    基金项目: 

    国家自然科学基金(批准号:61375105)资助的课题.

摘要: 对于能量受限的无线传感器网络,拓扑优化能够降低能耗,优化通信链路结构.本文基于最小刚性图原理提出了一种新的拓扑优化算法,算法综合考虑了生成拓扑链路图中通信链路的权值与生成刚性图的代数特性问题,既保证了通信链路较短,有利于延长网络的生命周期,同时使生成的通信链路图结构更加稳定,网络具有较好的鲁棒性.仿真实验表明,与相关算法比较,提出的算法中通信链路较短,具有较好的网络连通性与结构稳定性,同时生成刚性图矩阵的迹较大,具有较好的刚度代数性能.

English Abstract

参考文献 (21)

目录

    /

    返回文章
    返回