电子学报 ›› 2012, Vol. 40 ›› Issue (5): 1000-1004.DOI: 10.3969/j.issn.0372-2112.2012.05.022

• 学术论文 • 上一篇    下一篇

基于局域世界的WSN拓扑加权演化模型

张德干, 戴文博, 牛庆肖   

  1. 天津理工大学, 天津市智能计算及软件新技术重点实验室;天津理工大学计算机视觉与系统省部共建教育部重点实验室, 天津 300384
  • 收稿日期:2011-05-14 修回日期:2011-08-26 出版日期:2012-05-25
    • 基金资助:
    • 国家自然科学基金 (No.60773073,No.61001174,No.61170173); 教育部新世纪优秀人才计划 (No.NCET-09-0895); 天津市自然科学基金 (No.10JCYBJC00500)

Local-World Weighted Topology Evolving Model for Wireless Sensor Networks

ZHANG De-gan, DAI Wen-bo, NIU Qing-xiao   

  1. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology;Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin 300384, China
  • Received:2011-05-14 Revised:2011-08-26 Online:2012-05-25 Published:2012-05-25

摘要: 无标度加权网络模型,反映了现实网络的存在形式和动力学特征,是无线传感网络建模和拓扑演化的有效研究工具.本文基于局域世界理论提出一种不均匀成簇的无线传感网络拓扑动态加权演化模型,考虑节点能量,通信流量和距离等因素,对边权重和节点强度进行了定义,同时研究了拓扑生长对边权重分布的影响.实验证明演化所得网络节点度,强度和边权重均服从幂律分布,结合已有理论成果可知,该拓扑不仅继承了无权网络较高的鲁棒性和抗毁性,同时降低了节点发生相继故障的几率,增强了无线传感网络的同步能力.

关键词: 无线传感网络, 加权网络, 局域世界, 动态演化

Abstract: Many real networks included Wireless Sensor Networks (WSN) can be considered as scale-free weighted networks which reflect their existing forms and dynamic characteristics.Based on local-world theory,we propose an uneven clustering weighted evolving model of WSN in this paper.The definitions of edge weight and vertex strength take sensor energy,communication traffic and distance into consideration.Vertex strength drives the growth of topology and edge weight dynamically changes correspondingly.Experimental results demonstrate that WSN topology we obtain has the property of weighted networks:edge weight,vertex degree and strength follow a power law distribution.Related research work show that weighted WSN not only share the robustness and fault tolerance of weight-free networks,but also reduce the happening probability of successive node-breakdown,furthermore,enhance the synchronization of WSN.

Key words: wireless sensor networks, weighted networks, local world, dynamic evolving

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