ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2017, Vol. 54 ›› Issue (9): 2021-2031.doi: 10.7544/issn1000-1239.2017.20160645

• 网络技术 • 上一篇    下一篇

基于多属性决策的机会传感器网络关键节点预测

刘琳岚1,张江1,舒坚2,郭凯1,孟令冲1   

  1. 1(南昌航空大学信息工程学院 南昌 330063);2(南昌航空大学软件学院 南昌 330063) (liulinlan@nchu.edu.cn)
  • 出版日期: 2017-09-01
  • 基金资助: 
    国家自然科学基金项目(61363015,61262020,61501217,61501218);江西省自然科学基金重点项目(20171ACB20018,20171BAB202009,20071BBH80022);江西省教育厅科学技术重点项目(GJJ150702); 江西省研究生创新专项资金项目(YC2015-S324,YC2016-042)

Multiple Attribute Decision Making-Based Prediction Approach of Critical Node for Opportunistic Sensor Networks

Liu Linlan1, Zhang Jiang1, Shu Jian2, Guo Kai1, Meng Lingchong1   

  1. 1(School of Information Engineering, Nanchang Hangkong University, Nanchang 330063);2(School of Software, Nanchang Hangkong University, Nanchang 330063)
  • Online: 2017-09-01

摘要: 提前获知或预测网络的关键节点,便可根据关键节点的相关信息对网络进行优化,当网络瘫痪时,可第一时间排查关键节点,减少网络维护时间和成本.现有静态无线传感器网络关键节点预测方法,不适用于机会传感器网络(opportunistic sensor networks, OSNs).针对机会传感器网络结构动态变化、消息传输时延高的特点,分析多区域机会传感器网络分层结构的消息传输过程,定义阶段贡献度反映Ferry节点在消息传输过程中的贡献程度,定义区域贡献度反映Ferry节点对区域的贡献程度.在此基础上,以Ferry节点在网络中的综合贡献度作为判断关键节点的依据,提出基于多属性决策中理想点法(technique for order preference by similarity to ideal solution, TOPSIS)的关键节点预测方法.实验结果表明:采用改进TOPSIS算法能够获得更高的预测精度;搭建了实验床以进一步验证提出的预测方法,结果表明,采用改进TOPSIS算法的预测精度更高.

关键词: 机会传感器网络, 关键节点, 区域贡献度, 阶段贡献度, 多属性决策

Abstract: If critical nodes have been predicted, the network can be optimized according to the information of the critical nodes. Furthermore, maintenance time and cost of network can be dramatically reduced by checking the critical nodes at the first time when the network is crashed. Unfortunately, the existing methods of predicting critical nodes in static wireless sensor networks are not suitable for opportunistic sensor networks (OSNs). According to the characteristics of dynamic changes of network topology and high latency, for multi-region OSNs (MOSNs) with hierarchical structure, this paper analyzes the message transferring process. The stage contribution is defined to reflect the contribution of Ferry nodes in the process of message transmission, and the region contribution is defined to reflect the contribution of Ferry nodes to regions. In terms of the comprehensive contribution of Ferry nodes, the prediction method of critical nodes is proposed, which is based on multiple attribute decision making—technique for order preference by similarity to ideal solution (TOPSIS). The experimental results show that the prediction method with improved TOPSIS algorithms achieves better accuracy. Furthermore, test bed is established so as to validate the proposed method. The test bed experimental results show that the prediction method with improved TOPSIS algorithms achieves better accuracy as well.

Key words: opportunistic sensor networks (OSNs), critical node, region contribution, stage contribution, multiple attribute decision making

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