电信科学 ›› 2015, Vol. 31 ›› Issue (4): 65-70.doi: 10.11959/j.issn.1000-0801.2015104

• 研究与开发 • 上一篇    下一篇

社区结构感知的间断连接无线网络路由机制

吉福生1,张洪沛1,张炎2   

  1. 1 重庆邮电大学宽带泛在接入技术研究所 重庆 400065
    2 中国信息通信研究院 北京 100191
  • 出版日期:2015-04-15 发布日期:2015-04-15
  • 基金资助:
    国家自然科学基金资助项目;重庆市自然科学重点基金资助项目;重庆市自然科学重点基金资助项目;重庆市青年科技人才培养计划基金资助项目

Community Structure Perception Routing Mecbanism in Intermittently Connected Wireless Network

Fusheng Ji1,Hongpei Zhang1,Yan Zhang2   

  1. 1 Broadband Ubiquitous Network Research Laboratory,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    2 China Academy of Telecommunications Research,Beijing 100191,China
  • Online:2015-04-15 Published:2015-04-15
  • Supported by:
    The National Natural Science Foundation of China;Chongqing Natural Science Foundation;Chongqing Natural Science Foundation;Youth Talents Training Project of Chongqing Science & Technology Commission

摘要:

针对间断连接无线网络中消息投递成功率低的问题,依据网络中节点社会关系的差异性,提出了一种社区结构感知的路由机制。该机制根据节点在各个运动周期内的状态信息建立马尔可夫模型,以描述节点运动状态转换过程,进而以分布式的方式感知节点中心度,并以社区中心度为参数,采用社区标签交换方法对网络结构进行动态检测,最终利用社区内中心节点为中继辅助完成消息的转发。仿真结果表明,所提出的路由机制在投递率方面的性能改善程度接近90%,极大地优化了网络性能。

关键词: 间断连接无线网络, 社会网络, 社区检测, 中心节点

Abstract:

Based on the differences of social relations between nodes,a community structure perception routing mechanism was proposed for the reason of low message delivery probability in intermittently connected wireless network.According to state information of nodes in each movement cycle,Markov models was established to show the state transition process among nodes,further,the centrality of node was obtained.Then considering about the centrality of node and exploiting the method of community label switching,network structure was checked dynamically.Finally,the central node in the community was chosen as the relay node to forward messages.Results show that the proposed method is accurate,and the performance of delivery ratio can be improved 90% approximately,optimizing the network performance greatly.

Key words: intermittently connected wireless network, social network, community detecting, central node

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