【摘要】：針對海量存儲(chǔ)網(wǎng)絡(luò)中高維數(shù)據(jù)傳輸能耗過大的問題,需要對海量存儲(chǔ)網(wǎng)絡(luò)中傳輸能耗進(jìn)行計(jì)算。但是采用當(dāng)前方法進(jìn)行數(shù)據(jù)傳輸能耗計(jì)算時(shí),無法給出感知節(jié)點(diǎn)的覆蓋范圍,存在數(shù)據(jù)傳輸效率低問題。為此,提出一種基于免疫規(guī)劃的海量存儲(chǔ)網(wǎng)絡(luò)中高維數(shù)據(jù)傳輸能耗優(yōu)化方法。上述方法先利用連通支配集構(gòu)造海量存儲(chǔ)網(wǎng)絡(luò)的節(jié)點(diǎn)調(diào)度機(jī)制,計(jì)算出每個(gè)節(jié)點(diǎn)覆蓋區(qū)域面積,建立了以網(wǎng)絡(luò)覆蓋率和節(jié)點(diǎn)率為目標(biāo)函數(shù)的數(shù)學(xué)模型,得到節(jié)點(diǎn)調(diào)度適應(yīng)度函數(shù),融合免疫理論思想將高維數(shù)據(jù)傳輸能耗優(yōu)化問題定義為抗原,將能耗優(yōu)化的可行解定義為抗體,利用親和度函數(shù)來判斷可行解的優(yōu)劣,并完成對海量存儲(chǔ)網(wǎng)絡(luò)中高維數(shù)據(jù)傳輸能耗優(yōu)化。仿真證明,所提方法優(yōu)化性能好,可以有效地提升海量存儲(chǔ)網(wǎng)絡(luò)中高維數(shù)據(jù)傳輸?shù)男省?br/>[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.
【作者單位】： 桂林電子科技大學(xué)海洋信息工程學(xué)院;
【分類號】：O157.5
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本文編號：2397616