通信学报 ›› 2015, Vol. 36 ›› Issue (10): 9-16.doi: 10.11959/j.issn.1000-436x.2015216

• 学术论文 • 上一篇    下一篇

基于局域信息和能量因素的WSN关键点判定算法

刘彬1,2,孙雅静1,刘浩然2,尹荣荣2   

  1. 1 燕山大学 电气工程学院,河北 秦皇岛 066004
    2 燕山大学 河北省特种光纤与光纤传感重点实验室,河北 秦皇岛 066004
  • 出版日期:2015-10-25 发布日期:2015-10-27
  • 基金资助:
    河北省自然科学基金资助项目;河北省自然科学基金资助项目

Key node decision algorithm based on local information and energy factor in wireless sensor network

Bin LIU1,2,Ya-jing SUN1,Hao-ran LIU2,Rong-rong YIN2   

  1. 1 School of Electrical Engineering,Yanshan University,Qinghuangdao 066004,China
    2 Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province,Yanshan University,Qinghuangdao 066004 China
  • Online:2015-10-25 Published:2015-10-27
  • Supported by:
    The Natural Science Foundation of Hebei Province;The Natural Science Foundation of Hebei Province

摘要:

针对无线传感器网络现有关键点判定方法中,判定指标比较单一,容易导致关键点判定结果不全面的问题,综合考虑节点局域信息和能量因素,提出一种新的关键点判定算法。此算法首先利用节点自身度与其邻居信息对节点局域信息进行建模,接下来结合节点剩余能量和节点失效后网络能耗的变化,建立能量因素模型,最终得到一个综合的关键点判定模型。利用Matlab仿真平台与其他算法进行对比仿真,结果表明该算法能使关键点判定更加全面,同时验证在保护由此算法判定所得关键点时,网络能够表现出强抗毁性,验证此算法的有效性。

关键词: 无线传感器网络, 关键点判定算法, 局域信息, 能量因素

Abstract:

In the existing key node decision methods for wireless sensor network,the judgment index was so single that the result judged was deficient.In order to solve this problem,a new key node decision algorithm(KNDA)based on local information and energy factor was proposed.Firstly,the local information model based on the degree and neighbor information of node was established.Secondly,according to the residual energy of node and the change of network energy consumption after the node failed,the models of energy factors were established.Finally,an aggregative key node decision model was formed.Extensive simulation experiments were carried out in the MATLAB simulation platform,by comparing with other algorithms,the results verify that the key nodes obtained by this algorithm are more sufficient.Meanwhile when the key nodes judged by KNDA are protected,the network can show strong invulnerability.

Key words: wireless sensor network, key node decision algorithm, local information, energy factor

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