基于多属性决策的机会传感器网络关键节点预测

发布时间：2018-05-12 11:26

本文选题：机会传感器网络 + 关键节点　；参考：《计算机研究与发展》2017年09期

【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.
[Abstract]:If we know or predict the key nodes in advance, we can optimize the network according to the relevant information of the key nodes. When the network is paralyzed, the key nodes can be searched in the first time, and the maintenance time and cost of the network can be reduced. The existing prediction methods for critical nodes in static wireless sensor networks are not suitable for opportunistic sensor networks. In view of the dynamic change of the network structure of opportunity sensor and the high delay of message transmission, this paper analyzes the process of message transmission in hierarchical structure of multi-area opportunity sensor network, and defines the contribution degree of Ferry node in the process of message transmission. The definition of regional contribution reflects the contribution of Ferry nodes to the region. On this basis, taking the comprehensive contribution of Ferry nodes in the network as the basis for judging the key nodes, a prediction method for key nodes based on the ideal point method for order preference by similarity to ideal solution technique in multi-attribute decision making is proposed. The experimental results show that the improved TOPSIS algorithm can obtain higher prediction accuracy, and the experimental bed is built to further verify the proposed prediction method. The results show that the improved TOPSIS algorithm has higher prediction accuracy.
【作者单位】：南昌航空大学信息工程学院;南昌航空大学软件学院;
【基金】：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)~~
【分类号】：TP212.9

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