基于不变波束形成的WSN路由存活概率云计算

发布时间：2018-04-27 11:58

本文选题：波束形成 + 无线传感器网络　；参考：《科技通报》2014年10期

【摘要】：无线传感器网络WSN的路由存活概率云计算模型的优化设计可以提高WSN网络的网络复杂和数据吞吐量,在WSN网络的路由发射功率能耗具有匹配衰减特性,路由存活概率优化困难。传统方法采用灰色马尔可夫链组合模型进行存活概率预测,云计算精度不高。提出一种基于不变波束形成的WSN路由存活概率云计算优化模型,进行WSN网络云计算路由链路模型的结构设计,在WSN网络中进行任务节点信息表征,采用能量剩余度向量概率密度优化分配算法进行能量分配,把WSN的能量节点信号从阵元域变换到波束域得到阵列域,采用空间协方差矩阵逆的高阶次幂来逼近WSN的数据传输信号模型,将旋转角度控制在半功率主瓣以内,由此得到了云计算模型下WSN路由节点数据传输模型。仿真实验表明,该算法能提高WSN路由的存活概率,从而提高了WSN的吞吐量和通信能力,路由概率寻优中抑制了在WSN网络的路由发射功率能耗的匹配衰减性,通过最小的能量开销实现路由分发速率和负载效益的最大化。
[Abstract]:The optimization design of routing survival probability cloud computing model of WSN in wireless sensor network can improve the network complexity and data throughput of WSN network. The energy consumption of routing transmission power in WSN network has the characteristic of matching attenuation, so it is difficult to optimize the routing survival probability. The traditional method uses the grey Markov chain combination model to predict the survival probability, and the accuracy of cloud computing is not high. A cloud computing optimization model of WSN routing survival probability based on invariant beamforming is proposed to design the routing link model of WSN cloud computing network. The task node information is represented in WSN network. The energy distribution algorithm is used to optimize the energy residual vector probability density, and the energy node signal of WSN is transformed from the array element domain to the beam space to obtain the array domain. The high order power of spatial covariance matrix inverse is used to approximate the data transmission signal model of WSN, and the rotation angle is controlled within the half power master lobe, and the data transmission model of WSN routing node in cloud computing model is obtained. The simulation results show that the proposed algorithm can improve the survival probability of WSN routing, thus improve the throughput and communication capability of WSN, and restrain the matching attenuation of routing transmission power consumption in WSN network in routing probability optimization. The route distribution rate and load benefit are maximized by minimum energy cost.
【作者单位】：四川理工学院计算机学院;
【基金】：企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2013WYY07)
【分类号】：TP212.9;TN929.5

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