海量存储网络中高维数据传输能耗优化仿真

发布时间：2019-01-01 13:12

【摘要】：针对海量存储网络中高维数据传输能耗过大的问题,需要对海量存储网络中传输能耗进行计算。但是采用当前方法进行数据传输能耗计算时,无法给出感知节点的覆盖范围,存在数据传输效率低问题。为此,提出一种基于免疫规划的海量存储网络中高维数据传输能耗优化方法。上述方法先利用连通支配集构造海量存储网络的节点调度机制,计算出每个节点覆盖区域面积,建立了以网络覆盖率和节点率为目标函数的数学模型,得到节点调度适应度函数,融合免疫理论思想将高维数据传输能耗优化问题定义为抗原,将能耗优化的可行解定义为抗体,利用亲和度函数来判断可行解的优劣,并完成对海量存储网络中高维数据传输能耗优化。仿真证明,所提方法优化性能好,可以有效地提升海量存储网络中高维数据传输的效率。
[Abstract]:In order to solve the problem of excessive energy consumption of high-dimensional data transmission in mass storage network, it is necessary to calculate the transmission energy consumption in mass storage network. However, when the current method is used to calculate the energy consumption of data transmission, the coverage of perceptual nodes can not be given, and the efficiency of data transmission is low. Therefore, an optimization method for energy consumption of high dimensional data transmission in mass storage networks based on immune programming is proposed. Firstly, the node scheduling mechanism of mass storage network is constructed by using the connected dominating set, and the coverage area of each node is calculated, and the mathematical model with network coverage and node rate as the objective function is established. The fitness function of node scheduling is obtained. The optimization problem of energy consumption for high dimensional data transmission is defined as antigen, the feasible solution for energy consumption optimization is defined as antibody, and the affinity function is used to judge the advantages and disadvantages of the feasible solution. The energy consumption of high dimensional data transmission in mass storage network is optimized. Simulation results show that the proposed method has good performance and can effectively improve the efficiency of high-dimensional data transmission in mass storage networks.
【作者单位】： 桂林电子科技大学海洋信息工程学院;
【分类号】：O157.5
，

本文编号：2397616