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 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.
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